Improved Kalman filter method considering multiple factors and its application in landslide prediction

Abstract

Landslides, seriously threatening human lives and environmental safety, have become some of the most catastrophic natural disasters in hilly and mountainous areas worldwide. Hence, it is necessary to forecast landslide deformation for landslide risk reduction. This paper presents a method of predicting landslide displacement, i.e., the improved multi-factor Kalman filter (KF) algorithm. The developed model has two advantages over the traditional KF approach. First, it considers multiple external environmental factors (e.g., rainfall), which are the main triggering factors that may induce slope failure. Second, the model includes random disturbances of triggers. The proposed model was constructed using a time series which consists of over 16-month of data on landslide movement and precipitation collected from the Miaodian loess landslide monitoring system and nearby meteorological stations in Shaanxi province, China. Model validation was performed by predicting movements for periods of up to 7 months in the future. The performance of the developed model was compared with that of the improved single-factor KF, multi-factor KF, multi-factor radial basis function, and multi-factor support vector regression approaches. The results show that the improved multi-factor KF method outperforms the other models and that the predictive capability can be improved by considering random disturbances of triggers.

Keywords

landslide / improved Kalman filter / triggering factors / displacement prediction

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Acknowledgments

The authors are grateful to surveyors who work hard around the Jingyang in a challenging environment to obtain Monitoring data. This study is also supported. by the National Natural Science Foundation of China (Grant Nos. 41731066, 41674001, 41790445), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2019JM-202), the Special Fund for Basic Scientific Research of Central Universities (No. CHD300102268204), the Fundamental Research Funds for the Central Universities (No. CHD300102269104), the Natural Science Foundation in Gansu Province of China (No. 2017GS10845). We thank Professor Li Wang (Chang’an University) for conducting the measurement. Constructive comments from editor and two anonymous reviewers improved the manuscript.

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