Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Uncertainty analysis in rainfall-runoff modelling : application of machine learning techniques /
Author Shrestha, Durga Lal.
Publisher CRC/Balkema,
Year Published 2009
OCLC Number 528397614
ISBN 9780415565981 (pbk.); 0415565987 (pbk.)
Subjects Runoff--Mathematical models. ; Rain and rainfall--Mathematical models. ; Hydrologic models. ; Runoff--Computer simulation. ; Rain and rainfall--Computer simulation.
Library Call Number Additional Info Location Last
ELBM  GB980.S54 2009 AWBERC Library/Cincinnati,OH 08/15/2011
Collation xvi, 205 p. : ill. (some col.) ; 25 cm.
Issued as the author's thesis (doctoral)--UNESCO-IHE Institute for Water Education, 2009. Includes bibliographical references (p. [181]-195).
Contents Notes
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 techniques. Second method aims at modelling uncertainty by building an ensemble of specialised ML models on the basis of past hydrological model's performance. Methods employed include artificial neural networks, model trees, locally weighted regression and fuzzy logic. The application of the methods to several real-world case studies demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.