Stochastic Disaggregation Modelling of Rainfall series

Shashank Singh

ISBN 10: 3659435783 / ISBN 13: 9783659435782

Published by LAP LAMBERT Academic Publishing Aug 2013, 2013

About this Item

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

Bibliographic Details

• Title: Stochastic Disaggregation Modelling of Rainfall series
• Publisher: LAP LAMBERT Academic Publishing Aug 2013
• Publication Date: 2013
• Language: English
• ISBN 10: 3659435783
• ISBN 13: 9783659435782
• Binding: Taschenbuch
• Condition: Neu

About this title

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.

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.