Kalman filter correction technique based on multi-source forecast residuals

In order to improve the flood forecasting accuracy, the real-time online correction of the flood forecasting results by mining the measured water level and discharge data is used to make full use of the information contained in the measured sequences of water level and discharge. A Kalman filter correction technique is proposed based on multi-source forecast residuals. Corresponding rising difference model and autoregressive model were used to construct the multi-source error information source, and then Kalman filtering technology was used to fuse the multi-source error sequences for the real-time correction of flood forecast results. This paper selected the Qiantang River Basin of Zhejiang Province as the study area. The validation results show that the multi-source residual fusion correction technique based on Kalman filtering technology can significantly reduce the flow simulation error and the average relative error is reduced by more than 10%.