Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 6
Abstract
Flood forecasting at Jamtara gauging site of the Ajay River Basin in Jharkhand, India is carried out using an artificial neural network (ANN) model, an adaptive neuro-fuzzy interference system (ANFIS) model, and an adaptive neuro-GA integrated system (ANGIS) model. Relative performances of these models are also compared. Initially the ANN model is developed and is then integrated with fuzzy logic to develop an ANFIS model. Further, the ANN weights are optimized by genetic algorithm (GA) to develop an ANGIS model. For development of these models, 20 rainfall–runoff events are selected, of which 15 are used for model training and five are used for validation. Various performance measures are used to evaluate and compare the performances of different models. For the same input data set ANGIS model predicts flood events with maximum accuracy. ANFIS and ANN model perform similarly in some cases, but ANFIS model predicts better than the ANN model in most of the cases.
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Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 6 • June 2009
Pages: 647 - 652
Copyright
© 2009 ASCE.
History
Received: Oct 12, 2007
Accepted: Oct 5, 2008
Published online: Feb 16, 2009
Published in print: Jun 2009
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