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1. Evaluation of an early flood warning system in Bamako (Mali): Lessons learned from the flood of May 2019

Author

Nanée Chahinian, Matias Alcoba, Ndji dit Jacques Dembélé, Fréderic Cazenave, and Christophe Bouvier

Subjects
early warning systems, experience feedback (EF), flash flood, forecasting and warning, hydrological modelling, pluvial flooding, River protective works. Regulation. Flood control, TC530-537, Disasters and engineering, TA495
Abstract

Abstract Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory.

Published
2023
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2. Network representation in hydrological modelling on urban catchments in data-scarce contexts: A case study on the Oued Fez catchment (Morocco)

Author

Ismail Bouizrou, Nanée Chahinian, Jean-Louis Perrin, Rémi Müller, and Naoual Rais

Subjects
Hydrological modelling, Flood events, Urbanisation, Distributed hydrological models, ATHYS, Oued Fez catchment, Physical geography, GB3-5030, Geology, QE1-996.5
Abstract

Study region: Oued Fez, Morocco. Study focus: Urban catchments are heterogeneous in terms of land use and have both natural and artificial drainage networks. Modelling them is not a straightforward task especially in data-scarce settings. This study investigates network representation in hydrological modelling using field data collected between 2008 and 2018 on the Oued Fez catchment. The road network is used as a proxy for the stormwater network. Two production functions, SCS (1972) and a linear reservoir, are coupled to the lag and route transfer function. Three types of land use classes are used. Tests are carried out at hourly and 5-minute time steps using both the natural and modified drainage networks. New hydrological insights for the region: Fifty-three rainfall–runoff events are monitored on the urban part of the catchment over the 2008–2018 period. The highest rainfall values are recorded in 2008/2009, while the highest peak flow values are recorded in 2017/2018. This is due to stream channelisation and increased stormwater network coverage. Using the road network with minimal land use classes improves the model performance at both the hourly and 5-minute time steps.

Published
2021
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3. Parametrization of a wastewater hydraulic model under incomplete data constraint

Author

Yassine Belghaddar, Carole Delenne, Nanée Chahinian, Abderrahmane Seriai, Ahlame Begdouri, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
missing data, SWMM, Wastewater network, hydraulic simulation, General Medicine, General Chemistry, parameter estimation, [SDE.ES]Environmental Sciences/Environmental and Society
Abstract

Hydraulic simulation represents a powerful tool for studying wastewater networks. In order to achieve this target, hydraulic software require a set of parameters such as pipe slopes, roughness, diameters, etc. However, these pieces of information are rarely known for each and every pipe. Moreover, underground networks are frequently expanded, repaired and improved and these changes are not always reported in databases. The task of completing the required data represents the most time-consuming part of model implementation. In this context, we present algorithms that complete missing data required by hydraulic software. We automated this data insertion and transformation in SWMM© format to make it quicker and easier for the user. This automated solution was compared with manually estimated inputs. The simulation results show a coherent hydraulic behaviour.

Published
2022
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4. Towards a generic fusion framework for underground networks involving model-driven engineering domain

Author

Yassine Belghaddar, Abderrahmane Seriai, Ahlame BEGDOURI, Carole Delenne, Nanée Chahinian, Rima Bachar, Mustapha Derras, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Berger-Levrault, Laboratoires Systèmes Intelligents et Applications (LSIA), and Université Sidi Mohamed Ben Abdellah (USMBA)

Subjects
[INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract

Underground networks, particularly sewerage networks require accurate information for their management. To successfully and smoothly accomplish the required tasks for network expansion, reparation or simulation analysis, operators collect and analyse data coming from multiple sources. The various and heterogeneous sources available often provide different representations for a network. Thus, raising challenges for information exploitation and communication between the different actors managing these networks. In addition, the imperfections related to the sources are numerous and their consideration in the decision making process is mandatory. In this paper, we propose a generic data modelling for the fusion of sewerage networks data. Our meta-model supports imperfection modelling at data-source level as well as at network object position and attribute levels, allowing thus formal fusion operations to be conducted efficiently and reliably. To validate our meta-model, we implemented it using data analysis and reengineering platform called Moose, and we conducted a test on the town of Prades-le-Lez (France). We took into account three data-sources providing information on the node positions of the sewerage network : 1- the official network map as semi-structured source, 2- a high resolution aerial image database and 3- a Google Street View database as unstructured sources. As result, we were able to reliably perform data monitoring and visualization requests on real heterogeneous multi-source data related to a specific sewerage network., International Journal of Information Science and Technology, Vol 6 No 2 (2022)

Published
2022
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5. Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Author

Nanée Chahinian, Reda Abdou, Yassine Bel-Ghaddar, Abderrahmane Seriai, Ahlame Begdouri, Carole Delenne, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Service (systems architecture), Exploit, Computer science, Process (engineering), Geography, Planning and Development, graph neural network, Topology (electrical circuits), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Aquatic Science, computer.software_genre, Biochemistry, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, TD201-500, Water Science and Technology, Structure (mathematical logic), Water supply for domestic and industrial purposes, Database, Hydraulic engineering, Missing data, missing value imputation, 6. Clean water, machine learning, Wastewater, Graph (abstract data type), 020201 artificial intelligence & image processing, wastewater network, TC1-978, computer
Abstract

International audience; Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

Published
2021
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6. Rainfall measurement from Commercial microwave links for urban hydrology in Africa: a simulation framework for sensitivity analysis

Author

Marielle Gosset, Matias Alcoba, Christophe Bouvier, Nanée Chahinian, Maxime Turko, Modeste Kacou, Aaron Boone, Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Laboratoire de Physique de l'Atmosphère [Abidjan] (LAPA), Université de Cocody, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de l'Atmosphère et de Mécanique des Fluides (LPA-MF), Université Félix Houphouët-Boigny (UFHB), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
Rainfall, Atmospheric Science, Hydrologic models, 010504 meteorology & atmospheric sciences, Meteorology, 0207 environmental engineering, 02 engineering and technology, Urban area, 01 natural sciences, law.invention, Operator (computer programming), law, Sensitivity (control systems), Precipitation, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, 0105 earth and related environmental sciences, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], geography, Microwave observations, geography.geographical_feature_category, Attenuation, Tropics, Uncertainty, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 6. Clean water, Current (stream), 13. Climate action, Environmental science, Weather radar, Microwave
Abstract

Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou where a hydrological model has been set up. The analysis is based on numerical simulations, using high resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: i) the precision of the raw information provided by the operator and ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or short (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

Published
2021
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7. Missing data completion in wastewater network databases: the added-value of Graph Convolutional Neural Networks

Author

Abderrahmane Seriai, Carole Delenne, Nanée Chahinian, Yassine Bel-Ghaddar, Ahlame Begdouri, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoires Systèmes Intelligents et Applications (LSIA), Université Sidi Mohamed Ben Abdellah (USMBA), Berger-Levrault, Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Computer science, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, Missing data, Convolutional neural network, 6. Clean water, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Wastewater, Added value, Graph (abstract data type), [INFO]Computer Science [cs], Data mining, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, computer, ComputingMilieux_MISCELLANEOUS
Abstract

Wastewater networks are mandatory for urbanization. Their management, which includes reparation and expansion operations, requires precise information about their underground components, mainly pipes. For hydraulic modelling purposes, the characteristics of the nodes and pipes in the model must be fully known via specific, complete and consistent attribute tables. However, due to years of service and interventions by different actors, information about the attributes and characteristics associated with the various objects constituting a network are not properly tracked and reported. Therefore, databases related to wastewater networks, when available, still suffer from a large amount of missing data.A wastewater network constitutes a graph composed of nodes and edges. Nodes represent manholes, equipment, repairs, etc. while edges represent pipes. Each of the nodes and edges has a set of properties in the form of attributes such as diameters of the pipes. In this work, we seek to complete the missing attributes of wastewater networks using machine learning techniques. The main goal is to make use of the graph structures in the learning process, taking into consideration the topology and the relationships between their components (nodes and edges) to predict missing attribute values.Graph Convolutional Network models (GCN) have gained a lot of attention in recent years and achieved state of the art in many applications such as chemistry. These models are applied directly on graphs to perform diverse machine learning tasks. We present here the use of GCN models such as ChebConv to complete the missing attribute values of two datasets (1239 and 754 elements) extracted from the wastewater networks of Montpellier and Angers Metropolis in France. To emphasize the importance of the graph structure in the learning process and thus on the quality of the predictions, GCNs' results are benchmarked against non-topological neural networks. The application on diameter value completion, indicates that using the structure of the wastewater network in the learning process has a significant impact on the prediction results especially for minority classes. Indeed, the diameter classes are very heterogeneous in terms of number of elements with a highly majority class and several classes with few elements. Non-topological neural networks always fail to predict these classes and affect the majority class value to every missing diameter, yielding a perfect precision for this class but a null one for all the others. On the contrary, the ChebConv model precision is slightly lower (0.93) for the majority class but much higher (increases from 0.3 to 0.81) for other classes, using only the structure of the graphs. The use of other available information in the learning process may enhance these results.

Published
2021
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8. WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

Author

Marin Julien, Maguelonne Teisseire, Laurent Deruelle, Francesca Frontini, Carole Delenne, Lucile Sautot, Rachel Panckhurst, Mathieu Roche, Nanée Chahinian, Thierry Bonnabaud La Bruyère, Serge Conrad, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Common Language Resources and Technology Infrastructure - European Research Infrastructure Consortium (CLARIN ERIC), Laboratoire de sociolinguistique, d’anthropologie des pratiques langagières et de didactique des langues-cultures (DIPRALANG), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Berger-Levrault, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Laboratoire de linguistique diachronique, de sociolinguistique et de didactique des langues (DIPRALANG)

Subjects
Petroleum engineering, Text mining, Information extraction, Computer science, Pipeline (computing), Wastewater, Natural language Processing, computer.software_genre, 6. Clean water, Domain (software engineering), Weir, [SDE]Environmental Sciences, [INFO]Computer Science [cs], computer, ComputingMilieux_MISCELLANEOUS
Abstract

Urbanization has been an increasing trend over the past century (UN, 2018) and city managers have had to constantly extend water access and sanitation services to new peripheral areas. Originally these networks were installed, operated, and repaired by their owners (Rogers et al. 2012). However, as concessions were increasingly granted to private companies and new tenders requested regularly by public authorities, archives were sometimes misplaced and event logs were lost. Thus, part of the networks’ operational history was thought to be permanently erased. The advent of Web big data and text-mining techniques may offer the possibility of recovering some of this knowledge by crawling secondary information sources, i.e. documents available on the Web. Thus, insight might be gained on the wastewater collection scheme, the treatment processes, the network’s geometry and events (accidents, shortages) which may have affected these facilities and amenities. The primary aim of the "Megadata, Linked Data and Data Mining for Wastewater Networks" (MeDo) project (http://webmedo.msem.univ-montp2.fr/?page_id=223&lang=en), is to develop resources for text mining and information extraction in the wastewater domain. We developed a specific Natural Language Processing (NLP) pipeline named WEIR-P (WastewatEr InfoRmation extraction Platform) which allows users to retrieve relevant documents for a given network, process them to extract potentially new information, assess this information also by using an interactive visualization and add it to a pre-existing knowledge base. The system identifies the entities and relations to be extracted from texts, pertaining network information, wastewater treatment, accidents and works, organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system. The preliminary results obtained on the Montpellier corpus (1,557 HTML and PDF documents in French) are encouraging and show how a mix of Machine Learning approaches and rule-based techniques can be used to extract useful information and reconstruct the various phases of the extension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities.

Published
2021
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9. Automatic mapping of urban wastewater networks based on manhole cover locations

Author

Benjamin Commandré, Laurent Deruelle, Jean-Stéphane Bailly, Nanée Chahinian, Mustapha Derras, Carole Delenne, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Berger-Levrault, AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), European Regional Development Fund (ERDF) European Union (EU), Languedoc Roussillon RegionRegion Occitanie, Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Cover (telecommunications), Computer science, Geography, Planning and Development, 0211 other engineering and technologies, Topology (electrical circuits), 02 engineering and technology, Wastewater, computer.software_genre, Set (abstract data type), Integrated management, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, General Environmental Science, [SDE.IE]Environmental Sciences/Environmental Engineering, Ecological Modeling, Multigraph, Elevation, 021107 urban & regional planning, 6. Clean water, Urban Studies, Network connectivity, Mapping, Urban hydrology, Graph (abstract data type), Data mining, computer, Street network
Abstract

International audience; Accurate maps of wastewater networks in cities are mandatory for an integrated management of water resources. However, in many countries around the world this information is unavailable or inaccurate. A new mapping method is put forward to create maps using manhole cover locations which could be available via ground surveys, remote sensing techniques or stakeholder's databases. A new algorithm is developed which considers manhole covers as the nodes of the network and connects them automatically by minimizing cost functions defined by industry rules thus generating an optimized network. The various input data and rules used to build the deterministic tree-shaped graph being uncertain, a stochastic version of the algorithm is also proposed to generate a set of probable networks in addition to the optimized one. The method is tested on the wastewater networks of Prades-le-Lez and Ramnonville Saint Agne, two towns located in Southern France. The shape and topology of the mapped networks are compared to the actual one. The results indicate an overall good agreement between the real and generated networks. The proposed algorithm may thus be used to map wastewater networks from sampled georeferenced manhole covers, elevation and street network databases.

Published
2019
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10. Comparison of index systems for rating water quality in intermittent rivers

Author

Christian Salles, Jean-Louis Perrin, Nanée Chahinian, Naoual Rais, Marie-George Tournoud, Chrystelle Bancon-Montigny, Claire Rodier, Lauryan Dowse, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université Sidi Mohamed Ben Abdellah (USMBA)

Subjects
Pollution, Mediterranean climate, Water flow, media_common.quotation_subject, 0208 environmental biotechnology, Drainage basin, 02 engineering and technology, 010501 environmental sciences, Management, Monitoring, Policy and Law, 01 natural sciences, River Vene (France), Intermittent rivers, Hydrology (agriculture), Rivers, Hierarchical cluster analysis, Water Quality, Environmental monitoring, Distinguishing water quality indexes, Mediterranean and semi-arid zones, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, General Environmental Science, media_common, Hydrology, geography, geography.geographical_feature_category, Greece, Oued Fez and River Sebou (Morocco), General Medicine, Arid, 6. Clean water, 020801 environmental engineering, Morocco, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Water quality, France, Desert Climate, Water Pollutants, Chemical, Environmental Monitoring
Abstract

Water quality indexes (WQI) are a practical way to evaluate and compare the level of chemical contamination of different water bodies and to spatially and temporally compare levels of pollution. The purpose of this study was to check if these indexes are appropriate for intermittent rivers under arid and semi-arid climates. A literature review enabled the comparison of 25 water quality indexes to discern their capability to evaluate spatial (inter and intra catchment) and temporal (high and low water flow conditions) variations in water quality in three Mediterranean intermittent rivers: the River Vene (France) and the Oued Fez and the River Sebou (Morocco). Hierarchical cluster analysis identified groups of WQI with similar behavior and brought to light the 6 most distinguishing indexes. Whatever the hydrological conditions at the two sites, both the ME-MCATUHE and NCS indexes, which were developed for Morocco and Greece, and the CCMEWQI and BCWQI indexes, which were developed for non-arid or semi-arid zones, gave appropriate water quality evaluations.

Published
2018
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11. Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

Author

Carole Delenne, Nanée Chahinian, Jean-Stéphane Bailly, Sandra Bringay, Benjamin Commandre, Marc Chaumont, Mustapha Derras, Laurent Deruelle, Mathieu Roche, Fabrice Rodriguez, Gérard Subsol, Maguelonne Teisseire, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université Paul-Valéry - Montpellier 3 (UM3), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Image & Interaction (ICAR), Berger-Levrault, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Project funded by the European Regional Development Fund and the Occitanie Region, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Paul-Valéry - Montpellier 3 (UPVM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SDE.MCG]Environmental Sciences/Global Changes, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Abstract

International audience; In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the " Cart'Eaux " project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information.

Published
2017

12. Automatic reconstruction of urban wastewater and stormwater networks based on uncertain manhole cover locations

Author

Benjamin Commandre, Nanée Chahinian, Jean-Stéphane Bailly, Marc Chaumont, Gérard Subsol, Fabrice Rodriguez, Mustapha Derras, Laurent Deruelle, carole delenne, Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Recherche pour le Développement (IRD), AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Nîmes (UNIMES), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Berger-Levrault, Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
EAU USEE, IMAGE AERIENNE, Wastewater and stormwater networks, Spanning tree, GESTION, VILLE, OPTIMISATION, EAU PLUVIALE, RESSOURCES EN EAU, High resolution aerial imagery, RESEAU HYDROLOGIQUE, Network mapping, AMENAGEMENT HYDRAULIQUE, [SDE]Environmental Sciences, Optimisation, CARTOGRAPHIE, METHODOLOGIE, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; Accurate maps of sewer and stormwater networks in cities are mandatory for an integrated management of water resources. However, in many countries this information is unavailable or inaccurate. A new two-fold mapping method is put forward. The first step consists in using imageprocessing techniques to detect buried network surface elements such as manhole covers on very high resolution aerial imagery. The second step consists in connecting them automatically using a tree-shaped graph constrained by industry rules. The method is tested on Prades-le-Lez, Southern France. The shape and topology of the reconstructed network are compared to the actual ones.The impact of the detected objects’ density is also assessed.

Published
2017

13. LARGE-SCALE GIS-BASED URBAN FLOOD MODELLING: A CASE STUDY ON THE CITY OF OUAGADOUGOU

Author

Christophe Bouvier, Marko Adamovic, Matias Alcoba, Claire Cassé, Nanée Chahinian, Agnès Crès, Anne Crespy, Archives Nationales, Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de recherches pour le développement, Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Scale (ratio), SYSTEME D'INFORMATION GEOGRAPHIQUE, VILLE, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, GIS-based modelling, ATHYS, Kinematic wave, Flood risks, Urban planning, African cities, 11. Sustainability, CALIBRAGE, LOGICIEL ATHYS, Drainage, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, Digital elevation model, LOGICIEL D'APPLICATION, 0105 earth and related environmental sciences, Hydrology, Flood myth, Flooding (psychology), MODELISATION, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 6. Clean water, INONDATION, ETUDE DE CAS, 13. Climate action, Urban hydrology, SIMULATION, Environmental science, Surface runoff, METHODOLOGIE
Abstract

International audience; African cities are prone to recurrent flooding due to unfavorable rainfall conditions, often insufficient drainage infrastructure and fast and poorly controlled urban development. It is thus important to put forward efficient tools to characterize flooding and its consequences over large urban areas, e.g. the entire agglomeration. However, scarce data and long computation time limit the use of classic hydraulic models in such cases and require to propose alternative models. Therefore a GIS-based urban flood model is proposed as a case study in the city of Ouagadougou (Burkina Faso), in order to produce flood mapping at small spatial resolution over the entire conglomeration. Spatial discretization is done using a Digital Terrain Model (DTM) forced by various obstacles (urban blocks) or drainage axis (roads or water collectors). The mesh size is set to 10m to get a good representation of the urban objects and drainage directions. Runoff at the cell scale is calculated using an SCS model, within parameters are linked to the density of the built-up areas and to the type of soil. Runoff is first routed with a Lag and Route model over 1057 catchments which drain nearly 10 ha. The velocity of the model derives from a geomorphological formula involving the slope and the upstream area of each cell. Runoff is then routed in the main hydrological network (i.e. streets and channels) with a Kinematic Wave model. The dimensions of the cross-sections derive from a geomorphological formula, involving slope and upstream areas of the cells. An example of flooded areas is shown, as an application of the models over the entire conglomeration. The model strength resides in its quick implementation at large scale using easily accessible data, and in a fast computation time. The model appears as a valuable tool for decision-makers, real time forecasting and infrastructures management. This modelling approach may be complemented by finer, local scale models which may use the computed fluxes as boundary conditions.

Published
2017

14. Detection of manhole covers in high-resolution aerial images of urban areas by combining two methods

Author

Jérôme Pasquet, Thibault Desert, Olivier Bartoli, Carole Delenne, Mustapha Derras, Nanée Chahinian, Marc Chaumont, Gérard Subsol, Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Nîmes (UNIMES), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Berger-Levrault, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Littoral, Environment: MOdels and Numerics (LEMON), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut de Recherche pour le Développement (IRD), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, Computer science, Feature extraction, 0211 other engineering and technologies, High resolution, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, Task (project management), Image (mathematics), Histogram, 11. Sustainability, Buried utility network, Computer vision, Limit (mathematics), Computers in Earth Sciences, Image resolution, Index Terms—Machine learning, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Signal processing, circular object detection, business.industry, high resolution, Urban sprawl, Pattern recognition, Filter (signal processing), [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Object detection, machine learning, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Satellite, Artificial intelligence, business, geometrical filter, buried utility network
Abstract

International audience; The detection of small objects from aerial images is a difficult signal processing task. To localise small objects in an image, low-complexity geometry-based approaches can be used, but their efficiency is often low. Another option is to use appearance-based approaches that give better results but require a costly learning step. In this paper, we treat the specific case of manhole covers. Currently many manholes are not listed or are badly positioned on maps. We implement two conventional previously published methods to detect manhole covers in images. The first one searches for circular patterns in the image while the second uses machine learning to build a model of manhole covers. The results show non optimal performances for each method. The two approaches are combined to overcome this limit, thus increasing the overall performance by about forty percent.

Published
2016
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15. Flow and nutrient transport in intermittent rivers: a modelling case-study on the Vène River using SWAT 2005

Author

Marie-George Tournoud, Nanée Chahinian, Bernadette Picot, and Jean-Louis Perrin

Subjects
010504 meteorology & atmospheric sciences, Soil and Water Assessment Tool, Scale (ratio), Flow (psychology), 0207 environmental engineering, Drainage basin, QUALITE DE L'EAU, 02 engineering and technology, SEDIMENT, 01 natural sciences, VALIDATION, MODELE HYDROLOGIQUE, AZOTE, DEBIT SOLIDE, CALAGE, SWAT model, 020701 environmental engineering, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, geography, geography.geographical_feature_category, PHOSPHORE, BASSIN EXPERIMENTAL, Sediment, NUTRIMENT, 6. Clean water, DEBIT, COURS D'EAU, ETUDE DE CAS, SIMULATION, HYDRODYNAMIQUE, MODELE SWAT, Environmental science, CRUE, Water quality, TRANSPORT SOLIDE, POLLUTION DES EAUX CONTINENTALES, Sediment transport
Abstract

Intermittent rivers have a specific hydrological behaviour which also influences water quality dynamics. The objective of this work was to model the flow and water quality dynamics of a coastal Mediterranean intermittent river using the Soil and Water Assessment Tool (SWAT 2005). Flow, sediment, nitrogen and phosphorus transport were simulated on the Vene experimental catchment, France. The model was sequentially calibrated at sub-catchment scale and validated both at sub-catchment and catchment scales. A procedure for building the data records for the point sources is presented. The results indicate that, while the model produces good results for flow simulation, its performance for sediment transport is less satisfactory. This in turn impacts on the nutrient transport module. The reasons behind these shortcomings are analysed, taking into account the length of the data records, their distribution and the equations used in the SWAT model. The need for a thorough multi-objective model validation ...

Published
2011
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16. Manhole cover detection using a geometrical filter on very high resolution aerial and satellite images

Author

Marie-George Tournoud, Aude Allard, Jean-Stéphane Bailly, Olivier Bartoli, Christian Salles, Fabrice Rodriguez, Carole Delenne, Nanée Chahinian, Katia Chancibault, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Eau et Environnement (IFSTTAR/GERS/EE), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Nantes Angers Le Mans (UNAM), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH), and Institut National de la Recherche Agronomique (INRA)

Subjects
Very high resolution, 010504 meteorology & atmospheric sciences, Cover (telecommunications), Computer science, IMAGE, DRAINAGE, RESEAU DE DRAINAGE, ASSAINISSEMENT, 02 engineering and technology, 01 natural sciences, DETECTION, TRAITEMENT DU SIGNAL, [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, DETECTION D'OBJET, Drainage network, Computer vision, TRAITEMENT DES IMAGES, TELEDETECTION, Image resolution, SATELLITE, 0105 earth and related environmental sciences, Remote sensing, business.industry, Orthophoto, Filter (video), Utility network, FILTRE, EGOUT, 020201 artificial intelligence & image processing, Satellite, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, DETECTION DES CONTOURS
Abstract

JURSE 2015, Joint Urban Remote Sensing Event, LAUSANNE, SUISSE, 30-/03/2015 - 01/04/2015; One of the direct consequences of urban growth is the multiplication of buried utility networks. It is difficult to obtain complete and accurate maps of these networks because they have often been produced by different parties at different times. This work aims at putting forward a methodology to detect manhole covers on high resolution images, in order to reconstruct an urban drainage network. A circular detection filter is applied to high resolution orthophotos and Pleiade images to pinpoint mahole covers. The primary results are encouraging as 42% of manhole covers are detected by this method. Further work is carried out to improve the method and extend it to rectangular grates, that are not detected by the circular filter.; Détection automatique de regards du réseau d'assainissement par analyse d'images satellites

Published
2015
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17. Temporal and spatial variability of organotins in an intermittent Mediterranean river

Author

Christian Salles, Jean Louis Perrin, C. Rodier, F. Hernandez, J.L. Seidel, Chrystelle Bancon-Montigny, V. Brunel, E. Gayrard, P. Marchand, M. G. Tournoud, G. Aubert, Nanée Chahinian, Laboratoire Physique des Surfaces Naturelles et Génie Rural, AGROCAMPUS OUEST, Service de Physique Théorique (SPhT), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Génétique et Ecophysiologie des Légumineuses à Graines (UMRLEG) (UMR 102), Etablissement National d'Enseignement Supérieur Agronomique de Dijon (ENESAD)-Institut National de la Recherche Agronomique (INRA)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire de Synthèse et Fonctionnalisation de Céramiques (UMR 3080), Saint Gobain, Synchrotron SOLEIL (SSOLEIL), Centre National de la Recherche Scientifique (CNRS), Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Corpusculaire - Clermont-Ferrand (LPC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mediterranean climate, Environmental Engineering, 010504 meteorology & atmospheric sciences, Stabilization pond, Stormwater, Sewage, QUALITE DE L'EAU, 010501 environmental sciences, Management, Monitoring, Policy and Law, 01 natural sciences, Waste Disposal, Fluid, FACTEUR ANTHROPIQUE, VARIATION TEMPORELLE, Spatio-Temporal Analysis, Rivers, Tributary, Organotin Compounds, BUTYLETAIN, [CHIM]Chemical Sciences, Waste Management and Disposal, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Pollutant, Hydrology, geography, TOXICITE, geography.geographical_feature_category, business.industry, General Medicine, 6. Clean water, ORGANOETAIN, COURS D'EAU, OCTYLETAIN, Biodegradation, Environmental, Water Framework Directive, 13. Climate action, VARIATION SPATIALE, [SDE]Environmental Sciences, Environmental science, Spatial variability, France, business, POLLUTION DES EAUX CONTINENTALES, Water Pollutants, Chemical, Environmental Monitoring
Abstract

Organotin compounds (OTs) are exclusively anthropogenic and have been widely used for their biocidal properties and as stabilizers in various industrial applications. Hence organotins are common pollutants. Their high toxicity has led to their entry on the EU water framework's priority substances' list. However, few studies are available regarding their behaviour in surface waters, in particular, in intermittent Mediterranean rivers. The Vene is an intermittent river located in Languedoc-Roussillon, southern France. It is the main tributary of an important shellfish farming site: the Thau lagoon. The present study aims at establishing the presence of OTs on a 1.5 km long reach of the river into which a waste water treatment plant (WWTP) discharges. The study is carried out during steady-state flow conditions over two consecutive years and investigates potential OTs sources in everyday domestic activities. Routine field monitoring was carried out over a 5 month period during the springs of 2008 and 2009. The results establish the presence of butyltins and octyltins throughout the 1.5 km long reach at concentrations exceeding the maximum allowable concentration levels imposed by the water framework directive. The WWTP is recognized as an important OTs point source; however, using trace and rare earth elements as tracers, an urban stormwater sewage gutter is identified as a secondary source. Its impact on the river's pollutant loads is however variable in time because of flow intermittency. The paper discusses the need for specific monitoring and management schemes for intermittent rivers.

Published
2013
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18. The role of river sediments in contamination storage downstream of a waste water treatment plant in low flow conditions: Organotins, faecal indicator bacteria and nutrients

Author

Jean-Louis Perrin, David Rosain, Claire Rodier, Patrice Got, Chrystelle Bancon-Montigny, Audrey Caro, Marie-George Tournoud, Nanée Chahinian, Bernadette Picot, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), and Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Pollution, 010504 meteorology & atmospheric sciences, media_common.quotation_subject, 010501 environmental sciences, Aquatic Science, Oceanography, water quality, 01 natural sciences, priority substances, Point source pollution, Nutrient, anthropogenic pollution, 11. Sustainability, Tributary, [CHIM]Chemical Sciences, Thau lagoon (France), Effluent, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, media_common, Pollutant, Hydrology, geography, geography.geographical_feature_category, sediment-water column interactions, Sediment, river-coastal lagoon continuum, 6. Clean water, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Water quality
Abstract

In intermittent rivers, characterised by a specific hydrological behaviour, sediment–water column interactions may influence water quality during low flows. The main objective of this work was to assess the extent of anthropogenic pollution (organotins, faecal indicator bacteria and nutrients) in the river sediment of an intermittent river and its impact on the downstream water bodies: The Vene River, main tributary of the Thau lagoon. We first investigated anthropogenic pollution from water and sediment samples collected in situ along the river course (1.5 km); then, during laboratory experiments, we assessed the survival of faecal bacteria and quantified the degradation rates of organotins on the same sediments. The results indicate the presence of anthropogenic pollution all along the study reach. The waste water treatment plant effluent is a direct pollution input source for anthropogenic pollution. The sediment data points to an urban drainage ditch as a secondary point pollution source while the organotins data highlights the presence of other diffuse sources, specific to this substance. The results of the laboratory experiments show that both faecal bacteria and organotins may persist in the river sediments for up to two months in summer and even longer in winter. This indicates that sediments may, under favourable conditions, become important pollutant stores which may later be released and transported to the Thau lagoon during floods.

Published
2012
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19. Distributed hydrological modelling of a Mediterranean mountainous catchment - Model construction and multi-site validation

Author

Nanée Chahinian, Roger Moussa, Claude Bocquillon, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université Montpellier 2 - Sciences et Techniques (UM2)

Subjects
MODEL CALIBRATION AND VALIDATION, 010504 meteorology & atmospheric sciences, Correlation coefficient, Hydrological modelling, [SDV]Life Sciences [q-bio], 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, Interflow, Water balance, Evapotranspiration, MEDITERRANEAN MOUNTAINOUS CATCHMENT, 020701 environmental engineering, Digital elevation model, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, Base flow, HYDROLOGIE, 6. Clean water, 13. Climate action, [SDE]Environmental Sciences, Environmental science, PARAMETERISATION, SPATIALLY DISTRIBUTED HYDROLOGICAL MODEL, Surface runoff, MULTI-SITE APPROACH
Abstract

Corresponding author. fax: +33 4 67 63 26 14. E-mail Addresses: moussa@ensam.inra.fr; International audience; A multi-site validation approach is necessary to further constrain distributed hydrological models. Such an approach has been tested on the Gardon catchment located in the mountainous Mediterranean zone of southern France using data gathered over a 10 year period on nine internal subcatchments. A spatially distributed hydrological model linked to a Geographical Information System, was developed on the basis of simplified physical process representations (infiltration, evapotranspiration, base flow, interflow,overland flow, channel routing), using conventional hydro-meteorological data and readily accessible geographical maps. The model. parameters were estimated from a Digital Elevation Model, soil and land-use maps; and only five parameters were calibrated for the whole catchment. Three procedures with different levels of calibration and validation were conducted at a daily time step, and the results of both calibration and validation were compared on the basis-of their performance with regards to objective criteria representing the water balance, the Nash and Sutcliffe efficiency and the correlation coefficient. The first application corresponds to the case of an ungauged catchment i.e. a simple application of the model without calibration. In the second application, the model. was calibrated using discharge values measured at the outlet on the first five year period and validated using data from intermediate gauging stations and on the remaining period at the outlet. In the third application, a multi-site calibration and validation was conducted simultaneously for all available stations using the first five year period and validated on the second five year period for all stations. Calibration against the outlet station and internal validation against eight additional stations revealed some short-comings for a few upstream tributaries. Further calibration against additional discharge stations improved the model's performance at the subcatchment level. These different calibration and validation tests challenge the predictive capability of the model both at the catchment and subcatchment level and hence illustrate the model's possible improvements (structure, data and parameterisation strategy) for predictions on ungauged catchments

Published
2007
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20. Catalogue of the models used in MOPEX 2004/2005

Author

Andreassian, V., Bergström, S., Nanée Chahinian, Duan, Q., Littlewood, I., Mathevet, T., Michel, C., Andrea Montanari, Moretti, G., Roger Moussa, Nasonova, O. N., Connor, K. O., Paquet, E., Perrin, C., Rousseau, A., Schaake, J., Wagener, T., Xie, Z., Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), SMHI SWE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de Recherche pour le Développement (IRD), and LLNL USA

Subjects
VIC, SWAP, NOAH, RRMT, MOPEX, SMAR, SAC-SMA, AFFDEF, SWB, HYDROTEL, [SDE]Environmental Sciences, HBV, GR4J, GR4H, MORDOR, MODSPA, IHACRES
Abstract

International audience; The following section presents a synthetic description of the models that were used in the various articles of this Red Book. The following models are covered (descriptions can be found in alphabetical order) : AFFDEF, GR4H, GR4J, HBV, HYDROTEL, IHACRES, MODSPA, MORDOR, NOAH, RRMT, SAC-SMA, SMAR, SWAP, SWB, VIC.

Published
2006

21. Accounting for temporal variation in soil hydrological properties when simulating surface runoff on tilled plots

Author

Nanée Chahinian, Marc Voltz, Roger Moussa, Patrick Andrieux, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
STORMFLOW GENERATION, Hydraulics, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Hydrograph, Soil science, 02 engineering and technology, FARMED CATCHMENTS, law.invention, FLOOD EVENT, Hydraulic conductivity, law, GENERATION DES CRUES, 020701 environmental engineering, RUNOFF MODEL, Water Science and Technology, Hydrology, PLOT SCALE, TILLAGE, CALIBRATION, HYDROLOGIE, 04 agricultural and veterinary sciences, 6. Clean water, Runoff model, Tillage, Infiltration (hydrology), INFILTRATION, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Surface runoff
Abstract

Correspondance: fax: +33 4 6763 2614. E-mail address: moussa@ensam.inra.fr; International audience; Tillage operations are known to greatly influence local overland flow, infiltration and depressional storage by altering soil hydraulic properties and soil surface roughness. The calibration of runoff models for tilled fields is not identical to that of unfilled fields, as it has to take into consideration the temporal variability of parameters due to the transient nature of surface crusts. In this paper, we seek the application of a rainfall-runoff model and the development of a calibration methodology to take into account the impact of tillage on overland flow simulation at the scale of a tilled plot (3240 m(2)) located in southern France. The selected model couples the (Morel-Seytoux, H.J., 1978. Derivation of equations for variable rainfall infiltration. Water Resources Research. 14(4), 561-568). Infiltration equation to a transfer function based on the diffusive wave equation. The parameters to be calibrated are the hydraulic conductivity at natural saturation K-s, the surface detention S-d and the lag time omega. A two-step calibration procedure is presented. First, eleven rainfall-runoff events are calibrated individually and the variability of the calibrated parameters are analysed. The individually calibrated K-s values decrease monotonously according to the total amount of rainfall since tillage. No clear relationship is observed between the two parameters S-d and omega, and the date of tillage. However, the lag time omega increases inversely with the peakflow of the events. Fairly good agreement is observed between the simulated and measured hydrographs of the calibration set. Simple mathematical laws describing the evolution of K-s and omega are selected, while S-d is considered constant. The second step involves the collective calibration of the law of evolution of each parameter on the whole calibration set. This procedure is calibrated on 11 events and validated on ten runoff inducing and four non-runoff inducing rainfall events. The suggested calibration methodology seems robust and can be transposed to other gauged sites.

Published
2006
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22. Estimation of rainfall-runoff model parameters based on a large sample of basin characteristics

Author

Bourqui, M., Plantier, M., Loumagne, C., Andreassian, V., Nanée Chahinian, Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), DAF GUYANE, Partenaires IRSTEA, and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
[SDE]Environmental Sciences
Abstract

Estimation of model parameters has been a major problem since the last two decades and if we want to address this problem in the context of ungauged basins it becomes much more challenging. We might think that technological progress in collecting spatialized data (Geographic Information Systems (GIS), Digital Elevation Models (DEM), radars~) offers the possibility to take into account the spatial variability of rainfall and watersheds physical characteristics, in rainfall-runoff models without carrying out any measurement in the field. To use this information, the lumped approach can be easily replaced by the semi-distributed approach. But, does the integration of additional information in a semi-distributed approach effectively improve the performance of flow simulations of this type of models? Moreover, which kind of heterogeneities are to be taken into account to make significant improvements ? In this paper, we deal with the impacts of using physical heterogeneities of basins on the performances of lumped and semi-distributed approaches. Given the requirement of a large database to produce statistically significant results, chimeras basins were used. Chimera basins are built by gathering two real but physically distant basins. Thus, we obtain a large sample of artificial catchments having real rainfall and runoff data at the scale of each real sub-basin.

Published
2005

23. Comparison of infiltration models to simulate flood events at the field scale

Author

Nanée Chahinian, Marc Voltz, Roger Moussa, Patrick Andrieux, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Calibration and validation, 010504 meteorology & atmospheric sciences, Mean squared error, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Hydrograph, 02 engineering and technology, 01 natural sciences, Transfer function, FIELD SCALE, Statistics, RUNOFF, 020701 environmental engineering, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Water Science and Technology, Hydrology, Flood myth, 6. Clean water, INFILTRATION MODEL, Infiltration (hydrology), 13. Climate action, MODEL CALIBRATION, [SDE]Environmental Sciences, Environmental science, Mathematical structure, Surface runoff
Abstract

Most runoff simulation and infiltration models have been developed at the global-catchment scale or the local-soil column scale. Few models have been specifically developed at the scale of agricultural fields and there are no guidelines to help modellers choose an adequate model to simulate overland flow and hence analyse the impact of different soil management practices on flood generation. A comparison is undertaken to select and calibrate models that simulate Hortonian overland flow at the field or small plot scale. The proposed methodology couples a runoff production model to a unit hydrograph transfer function. Four different models were tested: Philip, Morel-Seytoux, Horton and SCS. These models differ by their mathematical structure and the parameters to be calibrated while input hydrologic data are the same site data: rainfall/runoff and initial water content. The models are calibrated on 14 events and validated on 14 others. The results of both the calibration and validation phases are compared on the basis of their performance with regards to six objective criteria, three global criteria and three relative criteria representing volume, peakflow, and the root mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent on the type of objective criteria used. Furthermore, when analysing the performance of the six objective criteria used, it can be seen that the global volume, global RMSE and relative peakflow criteria give the best compromise between bias and precision. Within the selected modelling framework, Morel-Seytoux's model performed better than the other three and the SCS gave the worst results. Horton's model showed to be more consistent in overall performance than Philip's model. Results also highlight problems related to the simulation of low flow events and intermittent rainfall events.

Published
2005
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24. WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

Author

Rachel Panckhurst, Mathieu Roche, Laurent Deruelle, Marin Julien, Maguelonne Teisseire, Lucile Sautot, Carole Delenne, Thierry Bonnabaud La Bruyère, Francesca Frontini, Nanée Chahinian, Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Istituto di Linguistica Computazionale 'Antonio Zampolli' (CNR-ILC), Consiglio Nazionale delle Ricerche [Roma] (CNR), Littoral, Environnement : Méthodes et Outils Numériques (LEMON), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de linguistique diachronique, de sociolinguistique et de didactique des langues (DIPRALANG), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Berger-Levrault, Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de sociolinguistique, d’anthropologie des pratiques langagières et de didactique des langues-cultures (DIPRALANG), and Supported by the Occitanie-Pyrenees-Mediterrannee Region under grant 2017006570/DF-000014.

Subjects
Text mining, Information extraction, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Wastewater, Reconnaissance d'Entités Nommées (REN), computer.software_genre, NLP, Domain (software engineering), Annotation, TALN Traitement Automatique des Langues Naturelles, 0202 electrical engineering, electronic engineering, information engineering, Domain adapted systems, [INFO]Computer Science [cs], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Adaptation (computer science), 021101 geological & geomatics engineering, Information retrieval, Réseaux d'assainissement, Extraction d'information IE, Conceptual model (computer science), [SCCO.LING]Cognitive science/Linguistics, Pipeline (software), 6. Clean water, NER, Domain knowledge, 020201 artificial intelligence & image processing, State (computer science), computer, Fouille de données textuelles
Abstract

International audience; We present the MeDO project, aimed at developing resourcesfor text mining and information extraction in the wastewater domain.We developed a specific Natural Language Processing (NLP) pipelinenamed WEIR-P (WastewatEr InfoRmation extraction Platform) which identifies the entities and relations to be extracted from texts, pertaining to network information, wastewater treatment, accidents and works,organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system which was developed to automate the extraction of the aforementioned annotationfrom texts and its integration with existing domain knowledge. The preliminary results obtained on the Montpellier corpus are encouraging and show how a mix of supervised and rule-based techniques can be used to extract useful information and reconstruct the various phases of theextension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities. French resources are less developed in the NLP community than English ones. The datasets obtained in this project are another original aspect of this work.

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