The application of NARX neural network model based on wavelet analysis for water level prediction
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Abstract:
Reliable water level forecasting is essential for flood prevention decision-making and water resources management,which can effectively reduce the loss of flood and droughts disasters.In order to improve the accuracy of forecasting,a nonlinear autoregressive with exogenous inputs neural network (NARX) model based on wavelet analysis (DWT-NARX) was proposed and compared with BP neural network,and NARX neural network model.The daily inflow,outflow,water utilization and the previous daily water level of Hongze Lake were considered to forecast the water level of Hongze Lake.The results indicated that three models achieved good simulation results with higher accuracy when the leading time was short,such as 1 or 2 days.The results exhibited that Nash-Sutcliffe coefficient was higher than 0.9,and the qualified rates surpass was not less than 85%.When the prediction period was further increased to 3 days,the NARX model showed poor prediction and the water level changed greatly,while BP model suggest severe oscillations.In overall performance,the NARX and DWT-NARX models showed superiority in comparison of BP neural network,while DWT-NARX yields the best performance among all other models.The research results can provide a certain reference value for the water level forecast of Hongze Lake.
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