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- W3130682287 abstract "Despite the availability of a variety of modeling techniques developed by different scientists in previous studies, such as data-driven and knowledge-based, accurate modeling of precipitation runoff processes remains a challenging task. Among these models, the water runoff model depend on an artificial neural network (ANN) plays important role in hydrology, since it can reproduce highly nonlinear properties of many features participated in basin hydrology. The paper developed ANN-based model for the prediction of runoff of the Narmada River. This work includes ANN model implementation using a feed-forward backpropagation (FFBP) algorithm with the Levenberg–Marquardt (LM) algorithm to establish correlations of monthly and annual rainfall-runoff. This research focuses on the development of the runoff prediction model based on an ANN for the Hoshangabad watershed of the Narmada River. The best performance of the model is calculated using metrics such as R, and RMSE, E, and MAPE. The research shows ANN model gives better results for data sets that scale from 0 to 1. The results obtained help managers to properly exploit water resources in the case of extreme phenomena such as floods and drought." @default.
- W3130682287 created "2021-03-01" @default.
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- W3130682287 date "2021-02-01" @default.
- W3130682287 modified "2023-10-14" @default.
- W3130682287 title "WITHDRAWN: An approach to utilize artificial neural network for runoff prediction: River perspective" @default.
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- W3130682287 doi "https://doi.org/10.1016/j.matpr.2021.01.198" @default.
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