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

doi:10.1007/s11707-019-0796-2

Front. Earth Sci.

2020, Vol. 14

Issue (3) : 625-636 https://doi.org/10.1007/s11707-019-0796-2

RESEARCH ARTICLE

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

Qing LING^1,², Wei QU¹(

), Qin ZHANG¹, Lingjie KONG², Jing ZHANG¹, Li ZHU³

¹. College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China
². School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China
³. Information Engineering University, Zhengzhou 450002, China

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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

Corresponding Author(s): Wei QU

Online First Date: 23 April 2020 Issue Date: 04 December 2020

Cite this article:

Qing LING,Wei QU,Qin ZHANG, et al. Improved Kalman filter method considering multiple factors and its application in landslide prediction[J]. Front. Earth Sci., 2020, 14(3): 625-636.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0796-2
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I3/625

Fig.1 Geographic locations of the Jingyang landslides.

Fig.2 Landslide in the study area (Photos were taken on December 5, 2015). (a) Landslides that occurred in 2013; (b) Landslide that occurred on May 26, 2015; (c) geological profile of Miaodian landslide.

Fig.3 Deformation monitoring network of landslides in the study area.

Fig.4 Monitoring curves for accumulated displacement of landslides at 9 monitoring stations.

Fig.5 Monthly cumulative rainfall, daily maximum precipitation and monthly displacement monitoring data of station MD09.

Tab.1 The displacement forecasting performance of improved multi-factors KF, improved single-factor KF, multi-factors KF, multi-factors RBF and multi-factors SVR

Fig.6 The total displacement prediction adopting the improved multi-factors KF, the improved single-factor KF, multi-factors KF, multi-factors RBF and multi-factors SVR models (M-factors means multi-factors, and S-factor means single-factor).

Fig.7 The APE of the improved multi-factors KF (Improved M-factor KF), the improved single-factor KF (S-factor KF), multi-factors KF (M-factor KF), multi-factors RBF (M-factor RBF) and multi-factors SVR (M-factor SVR) models.

Fig.8 Fitting and predicting displacement of MD09 using the improved multi-factors KF.

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