Abstract
Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This Special Issue of the journal Water is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses can contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures.
Highlights
IntroductionAround the world each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering
Around the world each year, extreme floods, which appear to be occurring more frequently in recent years, lead to enormous economic damage and human suffering.As such, it is imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event
The information and analyses can contribute to the development and implementation of effective hydrological predictions, and of appropriate precautionary measures
Summary
Around the world each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management [1,2,3,4,5,6,7,8,9,10,11,12]. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. The papers cover a number of applications of different novel meta-heuristic techniques in addressing a variety of hydrological modelling problems, tailored for different areas of geography and climatic conditions
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