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Fuzzy Neural Network hybrid modelling for runoff estimation: A case study on Koga catchment,Ethiopia,Used
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The problem of accurately determining river flows from rainfall, evaporation and other factors, occupies an important place in hydrology as the rainfallrunoff process is believed to be highly nonlinear, time varying, spatially distributed and not easily described by simple model. The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers which is capable of handling nonlinear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in estimating the daily runoff at the outlet of Koga catchment within Blue Nile river basin in Ethiopia. The methodology and results were analysed for different input scenarios. The analysis should be especially useful to the Hydrologist, civil engineering students, field engineers and researchers who may be considering utilizing latest soft computing techniques for runoff estimation in limited data, uncertainty and partially understood hydrological processes of a catchment.
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