Prediction of daily sediment discharge using a back propagation neural network training algorithm:A case study of the Narmada River,India

作     者：Nibedita Bisoyi Harish Gupta Narayan Prasad Padhy Govind Joseph Chakrapani 

作者机构：Department of Humanities and SciencesCollege of Engineering Roorkee Department of Civil Engineering University College of Engineering Osmania University Department of Electrical Engineering Indian Institute of Technology Roorkee Department of Earth SciencesIndian Institute of Technology Roorkee 

出 版 物：《International Journal of Sediment Research》 (国际泥沙研究(英文版))

年 卷 期：2019年第34卷第2期

页      面：125-135页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081502[工学-水力学及河流动力学] 0815[工学-水利工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Central Water Commission Narmada Basin Organization 

主　　题：Artificial neural network Back propagation Sediment discharge Prediction Error Narmada River 

摘      要：Most of the studies on Artificial Neural Network(ANN)models remain restricted to smaller rivers and *** this paper,an attempt has been made to correlate variability of sediment loads with rainfall and runoff through the application of the Back Propagation Neural Network(BPNN)algorithm for a large tropical *** algorithm and simulation are done through MATLAB *** methodology comprised of a collection of data on rainfall,water discharge,and sediment discharge for the Narmada River at various locations(along with time variables)and application to develop a threelayer BPNN model for the prediction of sediment *** training and validation purposes a set of549 data points for the monsoon(16 June-15 November)period of three consecutive years(1996-1998)was *** testing purposes,the BPNN model was further trained using a set of 732 data points of monsoon season of four years(2006-07 to 2009-10)at nine *** model was tested by predicting daily sediment load for the monsoon season of the year *** evaluate the performance of the BPNN model,errors were calculated by comparing the actual and predicted *** validation and testing results obtained at all these locations are tabulated and *** obtained from the model application are robust and encouraging not only for the sub-basins but also for the entire *** results suggest that the proposed model is capable of predicting the daily sediment load even at downstream locations,which show nonlinearity in the transportation ***,the proposed model with further training might be useful in the prediction of sediment discharges for large river basins.