Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques: UNESCO-IHE...

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Durga Lal Shrestha

Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

UNESCO-IHE PhD Thesis

Durga Lal Shrestha

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English

CRC Press
15 January 2010

Hydrology & the hydrosphere; Machine learning

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model's performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.

By: Durga Lal Shrestha
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 246mm, Width: 174mm,
Weight: 408g
ISBN: 9780415565981
ISBN 10: 0415565987
Pages: 222
Publication Date: 15 January 2010
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Content: Introduction; Uncertainty analysis in rainfall-runoff modelling; Machine learning techniques and parameter uncertainty, residual uncertainty; Conclusions and recommendations.

Durga Lal Shrestha is a researcher in the Hydroinformatics and Knowledge Management Department of the UNESCO-IHE Institute for Water Education, Netherlands. He received his Masters degree in hydroinformatics from the UNESCO-IHE Institute for Water Education in 2002. His research interests include hydrological modelling, uncertainty analysis, global and evolutionary optimisation, machine learning techniques and their applications in water based systems.