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Urban water networks for water supply, sanitation and storm drainage provide important services, the failure of which can be very serious for the urban environment and the population. The sustainability of such assets, which frequently, if not continuously, interact with each other and with other components of urban water cycle (i.e., surface water, groundwater and receiving waters), is therefore an important issue for the asset managers. The services provided by these assets are at risk in three primary respects including structural integrity, hydraulic performance and environmental impact, which indicate the need for a total life cycle approach to managing the network assets, from planning and design, through construction and operation to replacement and disposal. This paper describes a 5th EU Framework project, named Hydroplan-EU, which covers asset management of water supply and drainage networks. This project is important because of the fact that there are more than 20,000 significant wastewater and water supply networks in Europe, most of which do not have associated with them formal asset management practices. In view of the acknowledged need for hundreds of billions of Euros in terms of capital investments in these networks throughout Europe over the next 10 years, and the need for more than 20 billion Euros on asset management activities annually, the introduction and dissemination of an internationally accepted procedure can make significant economic savings, and br

Flooding in urbanized areas has become a very important issue around the world. The level of service (or performance) of urban drainage systems (UDS) degrades in time for a number of reasons. In order to maintain an acceptable performance of UDS, early rehabilitation plans must be developed and implemented. In developing countries the situation is serious, little investment is done and there are smaller funds each year for rehabilitation. The allocation of such funds must be “optimal” in providing value for money. However this task is not easy to achieve due to the multicriteria nature of the rehabilitation process, taking into account technical, environmental and social interests. Most of the time these are conflicting, which make it a highly demanding task. The present book introduce a framework to deal with multicriteria decision making for the rehabilitation of urban drainage systems, and focuses on several aspects such as the improvement of the performance of the multicriteria optimization through the inclusion of new features in the algorithms and the proper selection of performance criteria. The use of Genetic Algorithms, parallelization and application in countries like Brazil, Colombia y Venezuela are treated in this book.

In the last few decades, the consequences of floods and flash floods in many parts of the world have been devastating. One way of improving flood management practice is to invest in data collection and modelling activities which enable an understanding of the functioning of a system and the selection of optimal mitigation measures. A Digital Terrain Model (DTM) provides the most essential information for flood manager. Light Detection and Ranging (LiDAR) surveys which enable the capture of spot heights at a spacing of 0.5m to 5m with a horizontal accuracy of 0.3m and a vertical accuracy of 0.15m can be used to develop high accuracy DTM but it need careful processing before it can be used for any application. The research presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modelling framework. The key characteristics of this improved algorithm are: (1) the ability to deal with different kinds of buildings; (2) the ability to detect elevated road/rail lines and represent them in accordance to the reality; (3) the ability to deal with bridges and riverbanks; and (4) the ability to recover curbs and the use of appropriated roughness coefficient of Manning’s value to represent close-to-earth vegetation (e.g. grass and small bush).

The intensive uses of limited water resources, the growing population rates and the various increasing human activities put high and continuous stresses on these resources. Major problems affecting the water quality of rivers, streams and lakes may arise from inadequately treated sewage, poor land use practices, inadequate controls on the discharges of industrial waste waters, uncontrolled poor agricultural practices, excessive use of fertilizers, and a lack of integrated watershed management. This study explores the impact of these pollution problems and the water quality degradation of Irrigated agricultural watersheds When the watersheds have a complex physical basis of interacting water bodies such as canals, drains and coastal lagoons as in the case of irrigated watersheds in coastal river Deltas, and when these environments are ‘data scarce environments’, the problems of managing water quality becomes more obvious and the need for reliable solutions becomes an urgent requirement. This study focused on the management of surface water quality problems in such watersheds and the importance of taking into consideration all the watershed components and the effects of pollution from the upstream canals on the downstream coastal lakes. In this study a generic framework for a (Water Quality Management Information System) is developed depending on the integration of physically based hydrodynamic and water quality models with GIS capabilities and the spatial and temporal capabilities of remote sensing in water quality modeling. The application is developed and tested for the Edko drainage catchment and shallow lake system in the western part of the Nile Delta, Egypt. The developed framework includes a hierarchy of modeling tools: a 1D-2D basic hydrodynamic model for a combined shallow lake-drainage system, a detailed 2D hydrodynamic model of the shallow lake, and a 2D water quality and eutrophication screening models for the lake system. The basic water quality model for the lake system simulates the main water quality parameters including the oxygen compounds, nutrients compounds, temperature, salinity and the total suspended matter (TSM). The complexity of the physical and ecological properties of the lake system implied the use of different methodologies for models calibration using remote sensing. The combination of remote sensing with mathematical modelling, for the calibration and verification of TSM and chlorophyll-concentrations in the shallow lake system showed reliable and successful results.

Day-to-day water management is challenged by meteorological extremes, causing floods and droughts. Often operational water managers are informed too late about these upcoming events to be able to respond and mitigate their effects, such as by taking flood control measures or even requiring evacuation of local inhabitants. Therefore, the use of weather forecast information with hydrological models can be invaluable for the operational water manager to expand the forecast horizon and to have time to take appropriate action. This is called Anticipatory Water Management. Anticipatory actions may have adverse effects, such as when flood control actions turn out to have been unnecessary, because the actual rainfall was less than predicted. Therefore the uncertainty of the forecasts and the associated risks of applying Anticipatory Water Management have to be assessed. To facilitate this assessment, meteorological institutes are providing ensemble predictions to estimate the dynamic uncertainty of weather forecasts. This dissertation presents ways of improving the end-use of ensemble predictions in Anticipatory Water Management.

This thesis presents powerful machine learning (ML) techniques to build predictive models of uncertainty with application to hydrological models. Two different methods are developed and tested. First one focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulations of hydrological models using efficient ML techniques. Second method aims at modelling uncertainty by building an ensemble of specialised ML models on the basis of past hydrological model’s performance. Methods employed include artificial neural networks, model trees, locally weighted regression and fuzzy logic. The application of the methods to several real-world case studies demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.

The book presents machine learning as an approach to build models that learn from data, and that can be used to complement the existing modelling practice in aquatic and geotechnical environments. It provides concepts of learning from data, and identifies segmentation (clustering), classification, regression and control as the learning tasks. A unified methodology based on the concepts of machine learning, information theory and statistics is presented that can be followed to build models using data as well as expert knowledge. Several machine learning methods (neur mentatlon a new algorithm is developed for segmentation of data series where the constraint of contiguity has to be satisfled. In classification multi-scale data (signal) transformatlon methods (e.g. scale-space transformation) are used to extract features to build data-driven models in geotechnics. A set of regression models are built to predict sediment transport rates and in assessing harbour sedimentation. Controllers that replicate the control strategy of model-based optimal controllers of water systems are built for situations where fast and accurate decisions are needed. The models built demonstrate excellent performance; they can complement or even replace the existing models and can be used In practice. The performance of the models proves the effectiveness of the methodology and machine learning in general.