Real-time correction method of Muskingum model based on Kalman filter

Li Qian,Yu Huanfei,Ge Hangjian,Hu Linlin,Zheng Hongri,W Qin,L Wang,V Yepes

doi:10.1051/e3sconf/202016506002

Li Qian, Yu Huanfei + Show 6 more

Open Access

https://doi.org/10.1051/e3sconf/202016506002

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Abstract

In flood forecasting, general flood forecasting models or empirical forecasts reflect the average optimal value or relationship curve under the previous data. However, in the operation forecast, the forecast plan value often deviates from the actual situation. This paper takes Muskingum model as an example, and uses the Kalman filter algorithm to correct the forecast results. The algorithm structure and principles were described detailed, and the numerical simulation test was set to verify the efficiency of the Kalman filter algorithm. The correct results with corrected method were compared. The results indicated that the efficiency of the updating system using Kalman filter algorithm was improved. Conclusively, the proposed method could be widely applied in real-time flood forecast updating.

Highlights

IntroductionWhen using the hydrological model for flood forecasting, due to various reasons, the actual situation is that each forecast has more or less errors (Bogner et al 2008)
Watershed hydrological system is a very complex natural system
Relative errors (ΔR and ΔQm) have been reduced.In particular, the certainty coefficient Nash‐Sutcliffe efficiency (NSE), which reflects the consistency of the overall process of the sub-flood, has been improved after each flood

Summary

Introduction

When using the hydrological model for flood forecasting, due to various reasons, the actual situation is that each forecast has more or less errors (Bogner et al 2008). Traditional hydrological forecasting methods are difficult to make up for some errors in the actual situation. Real-time forecasting is not appropriate for such considerations that were not considered in the original model, cannot be considered, or even considered, and there is a certain amount of flood estimation (Madsen, 2005). The factors that cause certain errors in the forecast, such as the structure, parameters, state variables, or input values of the model, are considered. In addition to a reasonable and effective model of river basin hydrological forecasting for flood forecasting, a set of reasonable and effective real-time correction techniques is needed

Methods

Results

Conclusion