Areal Rainfall Forecast over Hunan Drainage Basin Using Multi-layer Fully Connected Neural Network
Based on ECMWF,JMA reanalysis data in East Asia,OCF,0.5° grid products of intel-ligent grid forecast in Hunan Province,numerical weather forecast model products in South China and East China,this research constructs a model by using multilayer fully connected neural network(MFC-NN)to predict the future 24 h,3 h and 1 h areal rainfall.The average absolute error(MAE),root mean square error(RMSE)and determination coefficient(R2)are used to test and evaluate the prediction effect of MFCNN model for regional rainfall of Hunan major reservoir drainage basin in 2020,and the forecast effect is compared with the effects of other models.The results show that the MFCNN model has better forecasting effects(MAE,RMSE,R2)of 24 h,3 h and 1 h areal precipitation than other models.With the improvement of time resolution,the prediction effect of MFCNN model becomes even better.The error coefficient shows that the precipitation predicted by the MFCNN model has the smallest devia-tion in Xiangjiang River and Dongting Lake,the moderate deviation in middle reach of Yuanshui River and the upper reach of Zishui River,and the largest deviation in Lishui River basin,the lower reach of Zishui River and the upper and lower reaches of Yuanshui River.It has been found that the MFCNN model has the strongest ability to capture the dynamic variation of areal rainfall in Dongting Lake,upper reach of Lishui River and middle reach of Yuanshui River,the second in Xiangjiang River basin,and the weakest in upper and lower reaches of Yuanshui River,the low reach of Zishui River and the lower reach of Lishui River.
万方数据
维普
