Flood forecasting method based on ensemble Kalman filtering

Abstract

The invention proposes a flood forecasting method based on ensemble Kalman filtering. The method comprises the steps of interfering with an intermediate state quantity, rainfall and evaporation amountof a selected basin to form a sample set, selecting an average value of a part of set closest to a measured value in a model prediction set to substitute a real value to calculate and obtain a prediction covariance matrix, then obtaining a Kalman gain matrix with the combination of the model prediction set according to the variance of the measured value, at the same time, updating a predicted value to obtain a model analysis value. According to the method, the certain improvement of the set Kalman filtering is carried out, the correction effect is better, a better flood prediction result is achieved, and the flood prediction accuracy is improved.

Description

technical field [0001] The invention relates to a flood forecasting method based on ensemble Kalman filtering, which belongs to the field of hydrology. Background technique [0002] Flood forecasting is an important content in flood control non-engineering measures, serving flood control and disaster reduction, water resource utilization and protection, water conservancy project construction and dispatch, industrial and agricultural safety production, etc. Therefore, the accuracy and timeliness of flood forecasting are of great significance. Real-time flood forecasting is a kind of online water regime forecasting system that uses real-time rain, water regime and other relevant hydrometeorological information as input into the flood forecasting model, and constantly corrects or improves the original model parameters according to the new information, and strives to gradually improve the forecast results. Flood forecasting close to the true value. [0003] However, errors alwa...

AI Technical Summary

Problems solved by technology

In flood forecasting, the most widely used hydrological model in China is the Xin'anjiang model; in the existing technology, the Xin'anjiang model is combined with ensemble Kalman filtering, and the model prediction set may be collectively large or collectively large during the correction process. The situation is too small, which causes the final correction effect to be less than ideal

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