A Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework

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...

Multi-Objective Optimization for Urban Drainage Rehabilitation

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...

Integrating GIS, remote sensing and mathematical modelling for surface water quality management in irrigated watersheds

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...

Optimisation of monitoring networks for water systems: Information theory, value of information and public participation

Eco-hydraulic modelling of eutrophication for reservoir management

Anticipatory water management: Using ensemble weather forecasts for critical events

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...

Uncertainty analysis in rainfall-runoff modelling: Application of machine learning techniques

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...

Model linking by mimicking hydrological and hydraulic models with artificial neural networks

Learning from data for aquatic and geotechnical environments

Abstract not available

Neural network design strategies and modelling in hydroinformatics