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The groundwater planning problems are often multiobjective. Due to conflicting objectives and non-linearity of the variables involved, several feasible solutions may have to be evolved rather than single optimal solution. In this study, the simulation model built on an Analytic Element Method (AEM) and the optimization model built on a Non-dominated Sorting Genetic Algorithm (NSGA-II) were coupled and applied to study a part of the Dore river catchment, France. The maximization of discharge, the minimization of pumping cost and the minimization of piping cost are the three objectives considered. 2105 non-dominated groundwater planning strategies were generated. K-Means cluster analysis was employed to classify the strategies, and clustering was performed for 3 to 25 clusters. A cluster validation technique, namely Davies–Bouldin (DB) index, was employed to find the optimal number of clusters of groundwater strategies which were found to be 20. Multicriterion Decision-Making (MCDM) techniques, namely VIKOR and TOPSIS, were developed to rank the 20 representative strategies. Both these decision-making techniques preferred representative strategy A5 (piping cost, pumping cost and discharge respectively of 880,000 Euro, 679,000 Euro and 1,263.1 m3/s). The sensitivity analysis of parameter v in VIKOR suggested that there were changes in ranking pattern for various values of v. However, the first position remained unchanged. HIGHLIGHTS The simulation model built on an Analytic Element Method (AEM) and the optimization model built on a Non-dominated Sorting Genetic Algorithm (NSGA-II) were linked and applied for groundwater planning.; The Davies–Bouldin (DB) index was employed to find the optimal number of clusters of groundwater strategies.; Multicriterion Decision-Making (MCDM) techniques, VIKOR and TOPSIS were implemented to rank the 20 representative strategies.

With rapid growth of urban populations and limited water resources, achieving an appropriate balance between water supply capacity and residential water demand poses a significant challenge to water supplying agencies. With the recent emergence of smart metering technology, where water use can be monitored and recorded at high resolution (e.g., observations of water use every 5 seconds), most existing research has been aimed at providing water managers with detailed information about the water use behavior of their consumers and the performance of water using fixtures. However, replacing existing meters with smart meters is expensive, and effectively using data produced by smart meters can be a roadblock for water utilities that lack sophisticated information technology expertise. The research in this dissertation presents low cost, open source cyberinfrastructure aimed at addressing these challenges. Components developed include an open source algorithm for identifying and classifying water end use events from smart meter data, a low cost datalogging and computational device that enables existing water meters to collect high resolution data and compute end use information, and a detailed water demand model that uses end use event information to simulate residential water use at a municipality level. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah. We tested the applicability of the disaggregation algorithm in quantifying water end uses for different meter sizes and types. We tested the datalogging computational device at a residential household and demonstrated collection, disaggregation, and transfer of high resolution flow data and classified events into a secure server. Finally, we demonstrated a water demand model that simulates the detailed water end uses of Logan’s residents using a combination of a set of representative water end use events and monthly billing data. Using the data we collected and the outputs from the model, we demonstrated opportunities for conserving water through improving the efficiency of water using fixtures and promoting behavior changes.

Scientific and related management challenges in the water domain are inherently multi-disciplinary, requiring synthesis of data of multiple types from multiple domains. Many data manipulation, visualization, and analysis tasks performed by water scientists are difficult because (1) datasets are becoming larger and more complex; (2) standard data formats for common data types are not always agreed upon, and, when they are, they are not always mapped to an efficient structure for visualization and/or analysis within an analytical environment; and (3) water scientists generally lack training in data intensive scientific methods that would enable them to use new and existing tools to efficiently tackle large and complex datasets. This project advances Data Science and Analytics for Water (DSAW) through developing: (1) an advanced object data model that maps common water-related data types to performant data structures within the object-oriented Python language and analytical environment based upon standard file, data, and content types established by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system; (2) two new Python packages that enable users to write Python code for automating retrieval of required water data, loading it into performant memory objects specified by the object data model we design, and performing analysis in a reproducible way that can be shared, collaborated around, and formally published for reuse. We have developed domain-specific data science applications to demonstrate how the new Python Packages can be paired with the powerful data science capabilities of existing Python packages like Pandas, numpy, and scikit-learn to develop advanced analytical workflows within a Python environment. By doing so, we are extending the data access, collaboration, and archival capabilities of the HydroShare data and model repository and promoting its use as a platform for reproducible water-data science., Funding Agency: National Science Foundation (US) Award Title: Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) Award Number: 1931297 Award URL: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931297

Climate change and global sustainability are among the most urgent concerns facing humanity. One important aspect of this is hydrology, i.e., the scientific study of the movement, distribution and management of water. In light of the pervasiveness of Web technology and more broadly of information and communication technology (ICT), this article presents an overview of recent advances in Web data analytics for hydrology and hydroinformatics, via scientific perspectives, experiments, and tools pertaining to hydrology data and its relevance to areas such as climate change. Researchers have conducted studies on hydrological problems that are spreading globally, and the role of the Web is crucial here for providing ubiquitous access to information, enabling its efficient analysis and adequately disseminating the results via websites and apps. This aids in predictive analytics and decision support. We address the management, mining, and dissemination of hydrology data with the long-term goal of global sustainability. We survey classical and state-of-the-art works in hydroinformatics and the Web, also including recent COVID-19 related issues, and discuss notable open avenues for future research both with regard to climate change as well as the future of the Web.

Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

This research investigated relationships between the most notable characteristics of end-use events, namely, event duration, volume, and intensity, in order to categorize water use as being...

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).

Maintaining a positive cost–benefit ratio for hydrologic data collection is challenging for public water management and regulatory agencies given budgetary constraints. We examine and discuss the e...

A method for generating combined multivariate time series at multiple locations and at different time scales is presented. The procedure is based on three steps: first, the Monte Carlo method generation of data with statistical properties as close as possible to the observed series; second, the rearrangement of the order of simulated data in the series to achieve target correlations; and third, the permutation of series for correlation adjustment between consecutive years. The method is non-parametric and retains, to a satisfactory degree, the properties of the observed time series at the selected simulation time scale and at coarser time scales. The new approach is tested on two case studies, where it is applied to the log-transformed streamflow and precipitation at weekly and monthly time scales. Special attention is given to the extrapolation of non-parametric cumulative frequency distributions in their tail zones. The results show a good agreement of stochastic properties between the simulated and observed data. For example, for one of the case studies, the average relative errors of the observed and simulated weekly precipitation and streamflow statistics (up to skewness coefficient) are in the range of 0.1–9.2% and 0–5.4%, respectively. This is the submitted version of the article: Đ. Marković, S. Ilić, D. Pavlović, J. Plavšić, and N. Ilich, ‘Multivariate and multi-scale generator based on non-parametric stochastic algorithms’, Journal of Hydroinformatics, vol. 21, no. 6, pp. 1102–1117, Nov. 2019, [https://doi.org/10.2166/hydro.2019.071]

Water related problems are ranked as some of the greatest problems facing mankind in the present century. It has become evident that the world’s availability of water is reduced due to excessive consumption, contamination of aquifers, inadequate waste management, lack of proper water collection and distribution technologies, and excessive farming. There are numerous reports that over 1 billion people in developing countries lack access to safe drinking water and almost 3 billion have no access to adequate sanitation facilities. Climate change has also become a challenging factor: changes in rainfall patterns are making traditional water systems less and less effective (e.g., drainage and flood protection, water control, storage and distribution facilities and so on). Despite the fact that our technological capabilities for dealing with hydro-meteorological events have advanced rapidly over the last hundred years and while global economic growth per capita has doubled, such events have become ever more disastrous. Modelling in fluid mechanics, hydraulics and hydrology, whether using digital tools or scale models, has reached sufficient maturity to be in daily use by engineers for analysis, design and for communication. Increasingly, complex cases can be handled thanks to ever-more sophisticated tools and increasingly abundant computing power. The emerging environment populated with new generation of sensors, using cloud-computing resources, is challenging the current practices of modelling and request innovation in methodology and concepts for a real integration into the decisionmakings processes. At the same time, the request to integrate vulnerability and resilience dimension in the various engineering approaches is becoming more and more frequent. Hydroinformatics offers a considerable opportunity to address some of these waterrelated issues in a way that it can meaningfully provide integration between data, models and decision support. The paper presents the results of the analysis of modern software used in hydroinformatics.

Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model.

With the development of hydroinformatics and computer technology, a new generation of hydrological modeling software is required to satisfy the users' demands for simulation accuracy and to comply with the progress of big data analysis and intelligent technology. This paper has proposed an Object and Modular-based Hydrological Modeling System (OMHMS) applied for the new generation modular software for hydrological simulation. This software was based on functionally flexible module library and SQLite lightweight database. The core of this model is the spatiotemporal variable-source mixed runoff model which enables parallel computation of models in space, time and subprocess. To reach the objectives including modularization, parameterization, intelligence, visualization and automation, the model has been applied in many small watersheds in China with favorable results. In particular, the model has been integrated into the flood system of Henan Province and run for many years for flood forecasting. The model shown high-level of applicability to be implemented in the small watershed of China with or without sufficient observation.

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered satisfactory (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than the ones positioned along the mainstream. IDW was the most chosen model for data imputation.

Water is a key environmental factor for the life of humans, animals, and plants representing an indispensable resource for the economy. The state of water resources is important both in terms of quantity and quality. To use and effectively protect water resources and ecosystems of Europe against pollution, climate change and marine waste, it requires coordinated action at European Union (EU) level. In this respect, the EU has established a community framework for the protection of water resources management by developing water directives. An important step to develop sustainable management plans of surface and groundwater resources is forecast of their quality evolution. To achieve this, it requires modeling the phenomena that occur in surface and groundwater (viz. hydrodynamic modeling and propagation of various substances which pollute water with diffuse and/or point pollution sources and due to various accidents), which is possible using advanced hydroinformatic modeling tools with satisfactory precision. The detailed results obtained from modeling increase general understanding of the evolution of water quality and support authorities to act (in time and space), in case of pollution, according to the management plans, based on plans risk management of pollution of water resources. This chapter shows the necessity and usefulness of advanced hydroinformatics tools in modeling of the evolution of water quality. We present several modelling tools, such as MODFLOW—with ASMWIN and PMWIN variants; MIKE BY DHI and FEFLOW. In the last section, we present some examples of application of these softwares.

Floods and droughts are among the most common natural hazards worldwide. They produce major impacts on society, economy, and ecosystems. Even worst, the frequency and severity of hydrological extremes are expected to increase with climate change and land-use alteration. As a countermeasure, during last decades, implementation of flood and drought forecasting models have globally become an emerging field of research for water management and risk assessment. In mountainous areas, hydrological extremes forecasting is unfortunately more challenging considering that information other than precipitation and runoff is not commonly available due to budget constraints, remoteness of the study areas and extreme spatio-temporal variability of additional driving forces. This is especially true for the tropical Andes in South America, which is the longest and widest cool region in the tropics. Recent advances in computational science coupled with long-term data availability have boosted Machine Learning (ML) applications. Among the variety of ML techniques, there is a potential to use the Random Forest (RF) algorithm due to its simplicity, robustness and capacity to deal with complex data structures. We used a step-wise methodology to developed short-term flood and drought forecasting models for several lead times (4, 8, 12 and 24 h) for two catchment representative of the Ecuadorian Andes. We found that derived models can reach maximum validation performances (Nash–Sutcliffe efficiency, NSE) from 0.860 (4-h) to 0.545 (24-h) for optimal inputs composed only by features accounting for 80% of the model’s outcome variance. Moreover, we found that a set of RF hyper-parameters can be transferred to a comparable catchment with a maximum model performance reduction of 0.10 (NSE). Overall, the forecasting of hydrological extremes (especially floods) is challenging mainly due to lack of relevant data (driving forces) and sufficient extreme events from which RF models learn. The applicability of this study is to assist authorities in flood and drought management to evaluate hazard risks and to found the basis for developing integrated action plans from a local and regional perspective.

It is increasingly recognized that water scarcity, rather than a lack of arable land, will be the major constraint to increase agricultural production over the next few decades. Therefore, water represents a unique agricultural asset to drive agricultural sustainability. However, its planning, management and usage are often influenced by a mix of interdependent economic, engineering, social, hydrologic, environmental, and even political factors. Such a complex interdependency suggests that a sociotechnical approach to water resources management, a subject of the field of Hydroinformatics, represents a viable path forward to achieve sustainable agriculture. Thus, this paper presents an overview of the intersection between hydroinformatics and agriculture to introduce a new research field called agricultural hydroinformatics. In addition, it proposes a general conceptual framework taking into account the distinctive features associated with the sociotechnical dimension of hydroinformatics when applied in agriculture. The framework is designed to serve as a stepping-stone to achieve, not only integrated water resources management, but also agricultural sustainability transitions in general. Using examples from agricultural water development to horticultural and livestock farming, the paper highlights facets of the framework applicability as a new paradigm on data flows/sources consideration, and information and simulation models engineering as well as integration for a holistic approach to water resources management in agriculture. Finally, it discusses opportunities and challenges associated with the implementation of agricultural hydroinformatics and the development of new research areas needed to achieve the full potential of this emerging framework. These areas include, for example, sensor deployment and development, signal processing, information modeling and storage, artificial intelligence, and new kind of simulation model development approaches.

Hydroinformatics, throughout its more than 25 years of existence, has been applied to a set of research areas. So far, these applications include: hydraulics and hydrology, environmental science and technology, knowledge systems and knowledge management, urban water systems management.This paper introduces agricultural water systems management as a new application for hydroinformatics, and terms it as “agricultural hydroinformatics”. It presents a discipline-delineated conceptual framework originating from the particularities of the socio-technical dimension of applying hydroinformatics in agriculture. It epitomizes the wholeness and inter-dependencies of agricultural systems studies and modelling. It is suitable to support, not only integrated agricultural water resources management in particular, but also agricultural sustainability in general, in addition to a wide range of agricultural development situations beyond connections between agro-economic and water engineering development and its socio-economic impacts.The paper also highlights some contributions of hydroinformatics to agriculture including new kinds of sensing technologies, information and simulation models development that bear the potential to boost reproducibility of agricultural systems research through systematic and formal records of the relationships among raw data, the processes that produce results and the results themselves.

This paper presents an integrated, computer-based generic framework to couple urban water related models into a decision support tool (DST) for urban-water management. The DST, built on a participatory modelling approach, enables stakeholders to analyze impacts of climate variation, population growth, and alternative management solutions ranging from centralized to distributed options. The coupling of external models, building of the DST, and execution of simulation processes is achieved by employing GoldSim, which is linked to a database management tool and a shared library. A post-processing package generates outcomes for decision makers in the form of a new criterion, the Water System Performance Index (WSPI). The application of DST is demonstrated for the water service area of the Salt Lake City Department of Public Utilities (SLCDPU). Results show that a decentralized alternative increases the reliability of the system without changing the system's vulnerability. Centralized alternative improves the overall performance of the system based on WSPI results, but decrease in performance is still noticeable under the hot and dry climate condition. Overall, the system is very sensitive to changes in climatic conditions. Results show that during droughts, supply management is not the sole solution and compulsory demand-management practices should be instigated.

During the last decades, the role of data as a vital resource that enhances decision-making and supports efficient systems operation has become evident, with a growing number of companies viewing data as a key organizational aspects that has to be properly managed, instead of an operational side-product. At the same time, drinking water systems increase in complexity and feature smart sensors, which in turn leads to data-richer operation environments for the water services. Given this challenging context, the often-overlooked factor of ensuring high data quality and preventing errors in data streams becomes increasingly important. In this chapter the current data validation techniques, challenges and best practices of the Dutch drinking water companies is presented.

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models., 42 pages, 11 figures

Hydrologic modeling can be used to aid in decision-making at the local scale. Developed countries usually have their own hydrologic models; however, developing countries often have limited hydrologic modeling capabilities due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. However, large-scale forecasting systems come with their own challenges. These New hydroinformatic challenges can prevent these models from reaching their full potential of becoming useful in the decision making process. This article discusses these challenges along with the background leading to the development of a large-scale streamflow prediction system. In addition, we present a large-scale streamflow prediction system developed using the GloFAS-RAPID model. The developed model covers Africa, North America, South America, and South Asia. The results from this model are made available using a Hydrologic Modeling as a Service approach (HMaaS) as an answer to some of the discussed challenges. In contrast to the traditional modeling approach, which makes results available only to those with the resources necessary to run hydrologic models, the HMaaS approach makes results available using web services that can be accessed by anyone with an internet connection. Web applications and services for providing improved data accessibility, and addressing the discussed hydroinformamtic challenges are also presented. The HydroViewer app, a custom application to display model results and facilitate data consumption and integration at the local level is presented. We also conducted validation tests to ensure that model results are acceptable. Some of the countries where the presented services and applications have been tested include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. Overall, a HMaaS approach to operationalize a GSPS and provide meaningful and easily accessible results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity of ensemble techniques available for implementation in hydrological sciences has led to the development and utilization of different strategies in the implementation. This review paper explores and refers to the advancement of ensemble methods, including the resampling ensemble methods (e.g., bagging, boosting, and dagging), model averaging, and stacking viz. generalized stacked, in different application fields of hydrology. The main hydrological topics in this review study cover subjects such as surface hydrology, river water quality, rainfall-runoff, debris flow, river icing, sediment transport, groundwater, flooding, and drought modeling and forecasting. The general findings of this survey demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology. In addition, the boosting techniques (e.g., boosting, AdaBoost, and extreme gradient boosting) have been more frequent and successfully implemented in hydrological problems than the bagging, stacking, and dagging approaches.

Today’s water sector is governed by some hot topics, and this is no different in Belgium. As for the International Water Association (IWA), the goal of its Belgian division (B-IWA) is to gather different stakeholders, i.e. academics, policy makers and people from industry active in the (Belgian) water sector and trigger the debate. In May 2015, a first Nocturnal was organised to accomplish just this. Seven hot water topics, proposed by participants at the time of their online registration, were addressed in rotating round table discussions. These topics included resource recovery, micropollutants, water scarcity, hydroinformatics, integrated water management and modeling, technology vs. legislation and Computational Fluid Dynamics in water applications. The lively discussions led to the idea of this short contribution describing the outcomes of these round table discussions, along with some additional research on the topics. Main conclusions include the need for education (both of scholars and young professionals), inter-domain communication and the growing importance of IT in the water sector.

Big data is popular in the areas of computer science, commerce and bioinformatics, but is in an early stage in hydroinformatics. Big data is originated from the extremely large datasets that cannot be processed in tolerable elapsed time with the traditional data processing methods. Using the analogy from the object-oriented programming, big data should be considered as objects encompassing the data, its characteristics and the processing methods. Hydroinformatics can benefit from the big data technology with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper provides a timely review on big data with its relevance to hydroinformatics. A further exploration on precipitation big data is discussed because estimation of precipitation is an important part of hydrology for managing floods and droughts, and understanding the global water cycle. It is promising that fusion of precipitation data from remote sensing, weather radar, rain gauge and numerical weather modelling could be achieved by parallel computing and distributed data storage, which will trigger a leap in precipitation estimation as the available data from multiple sources could be fused to generate a better product than those from single sources.

Analysis of single point pressure transient signal has the potential of providing information on the pipeline system. The technique uses the frequency response and experiments are satisfactory for low-frequency bandwidth. The concept behind this approach is that reflections are detected by leak-induced disturbances. Such reflections are often very difficult to identify, particularly because of excessive noise. The common approach to boost the analysis is to use a data denoising approach. The wavelet transforms for leak detection in water pipelines are proposed in this article. The wavelet filter is optimally calibrated using maximal kurtosis values and is chosen for leakage detection. The suggested approach is tested using high-noise laboratory test signals. The findings show that the method is superior to current signal processing methods for the conditions used. The system permits not only the detection of leakage but also the location and evaluation of its magnitude with errors of less than 7%.

This special issue of Journal of Hydroinformatics presents a collection of papers initially presented at the 2nd EWaS International Conference ‘Efficient & Sustainable Water Systems toward Worth Living Development’. The conference was held on June 1–4, 2016, in Platanias/Chania, Crete, Greece (http://www.ewas2.tuc.gr/). The conference was co-organized by the University of Thessaly/Civil Engineering Department and the Technical University of Crete/School of Environmental Engineering (Co-chairmen: V. Kanakoudis – University of Thessaly, G. Karatzas – Technical University of Crete, vice chairman: E. Keramaris – University of Thessaly). The EWaS series of conferences started in 2013, when the 1st EWaS Conference was held in Thessaloniki. The 2nd EWaS International Conference highlighted the need to improve the efficiency and sustainability of water systems in a changing and fragile environment, especially under the frustrating economic conditions encountered today. Water scarcity and climate change are both considered today as the main causes of water-related problems. Moreover, it is estimated that 20–40% of Europe's available drinking water is being wasted through real (physical) losses occurring along the supply systems. This results in inefficient use of water and energy resources as well as negative economic, technical, social and environmental impacts. Efficient and sustainable management of water distribution systems asks for advanced tools and strategies for their analysis, monitoring, planning and operation. In this context, the integration with ICT innovations in the water sector offers new opportunities for water distribution systems management in urban areas, while exploiting the smart water networks paradigm. The current special issue of Journal of Hydroinformatics was guest-edited by Associate Professor Vasilis Kanakoudis (University of Thessaly, Volos, Greece) and Professor Marco Franchini (University of Ferrara, Italy). The papers included in this special issue are based on the initial presentations at the conference. However, they have been extended (by at least 50%) and revised, having gone through …

Neurocomputing methods have contributed significantly to the advancement of modelling techniques in surface water hydrology and hydraulics in the last couple of decades, primarily due to their vast performance advantages and usage amenity. This comprehensive review considers the research progress in the past two decades, the current state-of-the-art, and future prospects of the application of neurocomputing to different aspects of hydrological sciences, i.e., quantitative surface hydrology and hydraulics. An extensive literature survey, by running over more than 800 peer-reviewed papers, outlines and concisely explores the past and recent tendencies in the application of conventional neural-based approaches and modern neurocomputing models in relevant topics of hydrological and hydraulic sciences. Apart from segregated descriptions and analyses of the main facets of surface hydrology and hydraulics, this review offers a practical summary of prevailing neurocomputing methods used in different subfields of hydrology and water engineering. Six relevant topics to modelling hydrological and hydraulic sciences are articulated and analysed, including modelling of water level in surface water bodies, flood and risk assessment, sediment transport in river systems, urban water demand prediction, modelling flow through hydro-structures, and hydraulics of sewers. This review is meant to be a mainstream guideline for researchers and practitioners whose work is associated with data mining and machine learning methods in various areas of water engineering and hydrological sciences to assist them to decide on suitable methods, network structures and modelling strategies for a given problem.

Clearing the water reservoirs from sediments is one of the most pressing problems. There are several ways to deal with this problem, and in most cases, they require enormous amounts of money and energy. Studying the motion of a muddy flow in pressure pipelines is one of the important works for solving this problem. Designed by the Department of Hydraulics and Hydroinformatics (TIIAME), the the inkjet apparatus is considered to be an energy-saving device and uses the potential of the flow itself to remove the muddy sediment from the water structures to the bottom. Typically, the flow carrying capacity depends on several hydraulic flow elements. The article discusses that fuzzy transporting ability depends on relative depth. Relative depth is the ratio of the suction height to the water head in front of the water outlet. The studies were conducted in laboratory conditions. Theoretical work is justified by the law of energy conservation. As a result, a new expression is obtained showing the relationship between the transporting ability of the flow and relative depth.

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

A SYSTEM FOR GENERATION OF NEAR REAL-TIME FEEDS OFUSER-CUSTOMIZED HYDROMETEOROLOGY DATA-PRODUCTSFROM NEXRAD RADAR-DATAbySatpreet Harcharan SinghA thesis submitted in partial ful llment of therequirements for the Master of Science degreein Electrical and Computer Engineeringin the Graduate College ofThe University of IowaDecember 2010Thesis Supervisor: Associate Professor Anton Kruger

A new concept for the selection of rational construction and engineering parameters for the building of large deep-set edifices is set out for an environment where the requirements on preservation of historical sites and rational usage of land in big cities are growing more taxing. Based on the proposed geotechnical model, and the assessment of the results of numerical modeling, research and experiments, a number of techniques have been developed intended to optimize the technological modes of construction under adverse urban conditions involving hard-to-handle soils and congested urban development.

Background nbsp Currently many the hydrological studies require of the analysis of information related to climate change scenarios generated by global circulation models GCMs This information must be processed to be representative of the study area To use the Geographic Information Systems GIS as instruments in the analysis processes is fundamental for the interpretation of results The objective of this work is present the way to use geomatics tools to process information related with climatic change scenarios This is possible through to the script in python language that converts Network Common Data Form netCDF files to vectors maps and raster As application example the monthly precipitation average data were analyzed of RCP W m y RCP W m sceneries to the near future and far future As results it had the monthly average precipitation to basin

The increased frequency of disaster events and the need for a rapid response for the prevention and remediation of flood disasters require realization of flood risk maps with very good precision based on high performance hydrodynamic river models using advanced hydroinformatics tools. Modelling performance, topographical data quality and quantity, hydrological and hydro meteorological surveys as well have very important for correct understanding and interpretation of all hydraulic aspects for minor and major riverbed. These are key factors in the need for new sources of data for hydrological modelling and flood risks management process. In this regard the actually development of satellite sensors, data, availability, Geographic Information System (GIS), and on the Hydroinformatics based river flow modelling techniques gives new opportunities in the analysis of the floods phenomenon. Use of the Very High Resolution remote sensing raster images to derive data useful for hydrological modelling is inviting and the answer to the question of accuracy of the data derived from remote sensing images is very important. In the present paper is investigated the methodology for using of multispectral satellite images acquired by Worldview2, SPOT 6 and SPOT 7 sensors for deriving hydrological model data and its corresponding accuracies. A case study using multispectral images related to rivers Timis, Bega and Timis-Bega canal in Romania and river flow model is foreseen for the future to verify the results of the methodology.

Critical water-resources issues ranging from flood response to water scarcity make access to integrated water information, services, tools, and models essential. Since 1995 when the first water data web pages went online, the US Geological Survey has been at the forefront of water data distribution and integration. Today, real-time and historical streamflow observations are available via web pages and a variety of web service interfaces. The Survey has built partnerships with Federal and State agencies to integrate hydrologic data providing continuous observations of surface and groundwater, temporally discrete water-quality data, groundwater well logs, aquatic biology data, water availability and use information, and tools to help characterize the landscape for modeling. In this paper, we summarize the status and design patterns implemented for selected data systems. We describe how these systems contribute to a US Federal Open Water Data Initiative and present some gaps and lessons learned that apply to global hydroinformatics data infrastructure.

Several millions of water liters are lost around the world for a deficient management of water between source to crop field UAD Unmanned Aerial Devices Remotely Piloted Aerial Devices also called drones are an efficient tool of increasing use in engineering and can be used in situations where a manned inspection is not possible With the advent of low cost equipment with sophisticated computer vision robotics and geomatic engineering these devices are able to generate high resolution images and videos even with low cost cameras Performance of hydrologic region depends on its maintenance to get functionality operability and to ensure life expectancy which demand frequent supervision and use of resources Mexican hydrologic conditions are high linked at stochastic environment conditions plus large surface implied a real difficult to basin management Consequently monitoring of environment factor such as precipitation and evapotranspiration are constant activities in order to get a sustainably resources In this work a potential use of UADs as show as a tool to help monitor operations and maintenance activities of Mexican hydrologic and hydraulic systems The results indicate that UADs provide the appropriate platforms to transport remote sensor and achieve the supervision and monitoring process of hydraulic conditions in a basin The results are opportune detection of water volume in basin water plan delivery of water leaks and performance of irrigation service

Water is our most precious food item This article deals with water related perceptions among residents and professionals and with the role of wastewater to mitigate supply of nutrients and water The topic covers different times and different geographical areas with a focus on urban areas Examples from Europe and Asian cities guide the text The purpose is to enhance our understanding of why our cities are or aspire to become pipe bound despite that this leads to resource problems

The last decades have seen a massive advance in technologies for Earth observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly developed algorithms have grown sharply. Such advances have extended the range of possibilities for hydrologists, who are trying to exploit these potentials the most, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events. The description of flood processes may particularly benefit from the integrated use of recent algorithms and monitoring techniques. In fact, flood exposure and risk over large areas and in scarce data environments have always been challenging topics due to the limited information available on river basin hydrology, basin morphology, land cover, and the resulting model uncertainty. The ability of new tools to carry out intensive analyses over huge datasets allows us to produce flood studies over large extents and with a growing level of detail. The present Special Issue aims to describe the state-of-the-art on flood assessment, monitoring, and management using new algorithms, new measurement systems and EO data. More specifically, we collected a number of contributions dealing with: (1) the impact of climate change on floods; (2) real time flood forecasting systems; (3) applications of EO data for hazard, vulnerability, risk mapping, and post-disaster recovery phase; and (4) development of tools and platforms for assessment and validation of hazard/risk models.

The development of experimental theories has been the normal practice for studying hydraulics and fluid dynamics Based on the fundamental equations some simplifications are included and through laboratory experimentation many of the relationships that are currently applied have been obtained With the advancement of technology the numerical simulation of the government equations has taken an important role in complementing the existing theories However it also has a certain level of simplification so it has not been able to fully replace laboratory experimentation A combination of both types of analysis based on fundamental principles theory is necessary to obtain reliable results In the present study a theoretical experimental and numerical analysis is applied to the case of flow in a Venturi tube determining that there is a high concordance when the results obtained with each of the methodologies are compared With this a reliable methodology is defined to analyze complex fluid flow problems