Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques: UNESCO-IHE PhD Thesis - Rilegato

Shrestha, Durga Lal

Rilegato

ISBN 10: 1138424099 ISBN 13: 9781138424098

Casa editrice: CRC Press, 2017

Riassunto

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.

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Informazioni sull'autore

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.

Dettagli dell'edizione

• Editore: CRC Press
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 1138424099
• ISBN 13: 9781138424098
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 222
• Contatto del produttore: Taylor and Francis, GPSR Taylor and Francis; gpsr@taylorandfrancis.com; ; Taylor & Francis Verlag GmbH, Kaufingerstraße 24; München; 80331; Germania