Stochastic Disaggregation Modelling Rainfall by Singh Shashank (8 results)

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Taschenbuch. Condition: Neu. Stochastic Disaggregation Modelling of Rainfall series | Application of Artificial Neural Network to Disaggregation of Rainfall Time Series | Shashank Singh (u. a.) | Taschenbuch | 140 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659435782 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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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.

Condition: New. Print on Demand pp. 140 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 140.

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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.

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.