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Covers many aspects of data based modelling issues with application to Hydrology
Brings readers up to date with clear case studies
Enables engineers to appropriately identify modelling approaches and issues
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Content Level »Research
Keywords »Applied hydrology - Artificial intelligence in hydrology - Evapotranspiration modelling - Hydrologic modelling - Rainfall-Runoff modelling - Solar radiation - Support vector - Time series modelling