Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

R. Gopakumar (), Kaoru Takara and E. James

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2007, vol. 21, issue 11, 1915-1940

Abstract: The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models. Copyright Springer Science+Business Media, Inc. 2007

Keywords: river flow forecasting; hydrologic data exploration; artificial neural networks; self organizing maps; multi layer perceptron; humid tropical region (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/s11269-006-9137-9

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