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A Semivirtual Watershed Model by Neural Networks
Author(s) -
Guo James C. Y.
Publication year - 2001
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00217
Subject(s) - hydrograph , weighting , watershed , kinematic wave , surface runoff , computer science , runoff model , set (abstract data type) , environmental science , hydrology (agriculture) , time of concentration , geology , geotechnical engineering , machine learning , ecology , medicine , biology , radiology , programming language
A semivirtual watershed model is presented in this study. This model places the design rainfall distribution on the input layer and the predicted runoff hydrograph on the output layer. The optimization scheme developed in this study can train the model to establish a set of weights under the guidance of the kinematic wave theory. The weights are time‐dependent variables by which rainfall signals can be converted to runoff distributions by weighting procedures only. With the consideration of time dependence, the computational efficiency of virtual watershed models is greatly enhanced by eliminating unnecessary visitations between layers. The weighting procedure used in the semivirtual watershed model expands the rational method from peak runoff predictions to complete hydrograph predictions under continuous and nonuniform rainfall events.