AN ANALYSIS OF EFFECTS OF ARTIFICIAL NEURALNETWORK STRUCTURES ON PRECISION OFSTREAM FLOW FORECASTING
人工神经网络结构对径流预报精度的影响分析

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.