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Stochastic Disaggregation Modelling of Rainfall series: Application of Artificial Neural Network to Disaggregation of Rainfall Time Series - Softcover
Synopsis
Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane’s Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling.
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About the Author
Shashank Singh got his M.S. in ABE from Purdue University, USA in 2009. He is also MTech in SWE from JAU, 2004. He had worked at prestigious IIM Ahmedabad in a World Bank Project on “Dew Harvesting”, 2004-2007. He is presently HOD at MITB and has research interest working on Watershed modelling, GIS-Remote Sensing and Stochastic hydrology.
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Stochastic Disaggregation Modelling of Rainfall series
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LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659435783
ISBN 13: 9783659435782
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane's Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling. 140 pp. Englisch. Seller Inventory # 9783659435782
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Stochastic Disaggregation Modelling of Rainfall series : Application of Artificial Neural Network to Disaggregation of Rainfall Time Series
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435783
ISBN 13: 9783659435782
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane's Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling. Seller Inventory # 9783659435782
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Stochastic Disaggregation Modelling of Rainfall series
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435783
ISBN 13: 9783659435782
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Stochastic Disaggregation Modelling of Rainfall series
Published by
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659435783
ISBN 13: 9783659435782
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for general circulation models and disaggregation is required. Disaggregation models were introduced in hydrology by the pioneering work of Valencia and Schaake (1972, 1973). Disaggregation models are widely used tools for the stochastic simulation of hydrologic series. They divide known higher-level values (e.g. annual) into lower level ones (e.g. seasonal), which add up to the given higher level. Thus ability to transform a series from a higher time scales to a lower one. Artificial Neural Network that mimics working of human neurons has proved to be a better performing model compared to stochastic and mathematical modeling of hydrological series. The result identified for Valencia-Schaake Model, Lane¿s Model and using ANN technique have been thoroughly discussed for their application and better understanding of Disaggregation modeling.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch. Seller Inventory # 9783659435782
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Stochastic Disaggregation Modelling of Rainfall series: Application of Artificial Neural Network to Disaggregation of Rainfall Time Series
Published by
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435783
ISBN 13: 9783659435782
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