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- W2927522069 abstract "Rainfall-runoff models are used to describe the hydrological behavior of a catchment. The relationship between rainfall and runoff is known to be highly non- linear, time varying and spatially distributed. This relationship is important in dealing with water resources management schemes. Recently, soft computing techniques such as Artificial Neural Networks, Fuzzy Logic, Adaptive Neuro- Fuzzy Inference System have emerged to model the various hydrological processes. This study presents the application of Artificial Neural Networks and Fuzzy Logic to model rainfall- runoff for Osmansagar catchment, Hyderabad, India. To study the performance of the models, various statistical indices such as Threshold statistics, Average absolute relative error, Correlation coefficient, Mean square error, Nash coefficient of efficiency were estimated.. The Nash coefficient of efficiency of the ANN and Fuzzy model were found to be 95% and 92 % during training/ calibration and 91% and 96% during testing / validation. The correlation coefficient between the observed and computed series for both the models are 0.97, 0.96 respectively during training /calibration and 0.97, 0.99 respectively during testing/validation periods. The results indicate that ANN and Fuzzy logic can effectively be used to establish relationship between rainfall and runoff." @default.
- W2927522069 created "2019-04-11" @default.
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- W2927522069 date "2017-03-01" @default.
- W2927522069 modified "2023-09-24" @default.
- W2927522069 title "Rainfall Runoff Relationship using Neural Networks and Fuzzy Logic" @default.
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