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FENG Guo zhang(Institute of Agricultural Water Soil Engineering,Northwestern Agricultural University, Yangling 712100)LI Pei cheng(Department of Hydrogeology and Engineering Geology, Xi'an Institute of Engineering, Xi'an 710054)  
A stream flow forecasting model of feed forward multi layer artificial neural network(ANN), in which current precipitation and antecedent flow are considered as the model inputs according to runoff generation mechanism, is introduced. The deterministic coefficient is adopted as a norm to control ANN training error and precision of model calibration and verification. It is shown through the study that ANN training error is decreased and the coefficient of model calibration is increased, and meanwhile the coefficient of model verification is persistently decreased, with increase of complexity of ANN structures. It is also recognized that the key factor affecting the model precision is the number of neurons in the input layer, i e., the number of flow effecting factors. A method to select models for operational application, and to combine optimal forecasting ranges is proposed.
【Fund】: 陕西省自然科学基金 国家“九五”攻关项目
【CateGory Index】: P338.2
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【Secondary References】
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6 JIANGLin li;School of Mathematics and Computer Science,Guangxi Science & Technology Normal University;State Key Lab of Software Engineering,Wuhan University;;Research on Runoff Water Level Prediction Based on Improved PSO-NN[J];湘潭大学自然科学学报;2016-04
7 CUI Dongwen;JIN Bo;Yunnan province Wenshan Water Bureau;;Application of the Bird Swarm Algorithm-projection Pursuit Regression Model to Prediction of Multivariate Annual Runoff[J];人民珠江;2016-11
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