Abstract
The updating scheme with high precision and strong robustness is one of the most important factors affecting the real-time flood forecasting system. The standard Kalman filter algorithm is often used to real-time updating, because of its timeliness and strong tracking. However, it is sensitive to outliers, a small number of outliers can cause seriously collapse. In order to withstand the destruction of outliers on updating process, a robust Kalman filter method is put forward. The robust weight function is introduced to adjust the weight of the measured data recursively. By compressing the weight of the suspicious observations and resulting in a decreased filter gain, the harmful influence of the abnormal observations on the determination of the state variables can be resisted effectively and the robustness of the updating can be achieved. The performances of the proposed method have been compared with the standard Kalman filter by both data with and without outliers. The robust method shows the robust results and the filters the impact of the abnormal observations.
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Acknowledgements
we would like to express sincere thanks to the editor and the anonymous reviewers whose comments led to great improvement of this paper.
Funding
This study is funded by Natural Science Foundation of Fujian Province (2022J011232) and Scientific Research Climbing Plan of **amen University of Technology (XPDKT19028) and Science and technology project of **amen (3502Z20203063) and Innovation and start-up project of **amen University of Technology (YKJCX2021148).
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Zhiqiang, H., like, L., Kaiqi, S., Chao, Z. (2023). Robust Real-Time Updating of Real-Time Flood Forecasting System Based on Kalman Filter. In: Baeyens, J., Dewil, R., Rossi, B., Deng, Y. (eds) Proceedings of 2022 4th International Conference on Environment Sciences and Renewable Energy. ESRE 2022. Environmental Science and Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-9440-1_4
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DOI: https://doi.org/10.1007/978-981-19-9440-1_4
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