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
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.
Y Alimohammadlou, A Najafi, C Gokceoglu (2014). Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. Catena, 120: 149–162 https://doi.org/10.1016/j.catena.2014.04.009
2
H An, T T Viet, G Lee, Y Kim, M Kim, S Noh, J Noh (2016). Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow. Environ Model Softw, 85: 172–183 https://doi.org/10.1016/j.envsoft.2016.08.009
3
S Bai, J Wang, Z Zhang, C Cheng (2012). Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena, 99:18–25 https://doi.org/10.1016/j.catena.2012.06.012
4
D Bui, T Tuan, H Klempe, B Pradhan, I Revhaug (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2): 361–378 https://doi.org/10.1007/s10346-015-0557-6
5
T Carlà, E Intrieri, P Farina, N Casagli (2017). A new method to identify impending failure in rock slopes. Int J Rock Mech Min, 93: 76–81 https://doi.org/10.1016/j.ijrmms.2017.01.015
6
Y Cao, K L Yin, D E Alexander, C Zhou (2016). Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides, 13(4): 725–736 https://doi.org/10.1007/s10346-015-0596-z
7
W Chen, J L Wang, X S Xie, H Y Hong, N V Trung, D T Bui, G Wang, X R Li (2016). Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci, 75(20): 1344 https://doi.org/10.1007/s12665-016-6162-8
8
R Cojean, Y J Caï (2011). Analysis and modeling of slope stability in the Three Georges Dam Reservoir (China)—the case of Huangtupo landslide. J Mt Sci, 2(8): 166–175 https://doi.org/10.1007/s11629-011-2100-0
9
I Colkesen, E K Sahin, T Kavzoglu (2016). Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci, 118: 53–64 https://doi.org/10.1016/j.jafrearsci.2016.02.019
10
G B Crosta, F Agliardi (2003). Failure forecast for large rock slides by surface displacement measure. Can Geotech J, 40(1):176–191(16)
11
X Z Cui, Z S Yu, B Z Tao, D J Liu, Z L Yu (2009). Generalized Adjustment. Wuhan: Wuhan University Press
12
A M De Livera, R J Hyndman, R D Snyder (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. J Am Stat Assoc, 106(496): 1513–1527 https://doi.org/10.1198/jasa.2011.tm09771
13
Z Duang (2013). Study on the trigger mechanism of loess landslide. Dissertation for the Doctoral Degree. Xi’an: Chang’an University
14
J Fu (2013). Application of Kalman filter method in landslide deformation forecast. Dissertation for the Doctoral Degree. Wuhan: China University of Geosciences
15
T Fukuzono (1985). New methods for predicting the failure time of a slope. In: Proceedings of the 4th International Conference and Field Workshop on Landslides. Tokyo: Tokyo University Press, p145–150 https://doi.org/10.1016/0148-9062(87)91524-5
16
W Gao, X Feng (2006). Study on displacement predication of landslide based on grey system and evolutionary neural network. Computer Methods Eng Sci, 890–894
17
Y He (2016). Identification and monitoring of the loess landslide by using of high resolution remote sensing and InSAR. Dissertation for the Doctoral Degree. Xi’an: Chang’an University
18
K Q He, S Q Wang, W Du, S J Wang (2010). Dynamic features and effects of rainfall on landslides in the Three Gorges Reservoir region, China: using the Xintan landslide and the large Huangya landslide as the examples. Environ Earth Sci, 59(6): 1267–1274 https://doi.org/10.1007/s12665-009-0114-5
19
H Y Hong, B Pradhan, M N Jebur, D T Bui, C Xu, A Akgun (2016). Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci, 75(1): 40 https://doi.org/10.1007/s12665-015-4866-9
20
R Huang (2004) On time predication of landslide. Scientific and Technological Management of Land and Resources, (06):15–20
21
R Huang (2007). Large-scale landslides and their sliding mechanisms in China since the 20th Century. Chinese Journal of Rock Mechanics and Engineering, 26(03): 433–454
22
F M Huang, K L Yin, G R Zhang, L Gui, B B Yang, L Liu (2016). Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory. Environ Earth Sci, 75(20): 1376 https://doi.org/10.1007/s12665-016-6133-0
23
Y L Jing, F C Dai (2007). The mechanism of irrigation-induced landslides of loess. Chinese Journal of Geotechnical Engineering, 10: 1493–1499
24
M Krkač, D Špoljarić, S Bernat, S M Arbanas (2017). Method for prediction of landslide movements based on random forests. Landslides, 14(3): 947–960 https://doi.org/10.1007/s10346-016-0761-z
25
C Li, L Fan, J Zhang, S Miao, Y Wang (2010). Application of Kalman filtering to high and steep slope deformation monitoring prediction of open-pit mines. J Univ Sci Technol Beijing, 32(01): 8–13
26
R P Li, H B Shi, J G Chi, Y Zhang Q(2007). Characteristics of air temperature and water-salt transfer during freezing and thawing period. Transactions of the Chinese Society of Agricultural Engineering, 23(04): 70–74
27
X Li, J Kong, Z Wang (2012). Landslide displacement prediction based on combining method with optimal weight. Nat Hazards, 61(2): 635–646 https://doi.org/10.1007/s11069-011-0051-y
28
Y Li, C Li, C Yan, Y Zeng (2008). Application of multivariable time series based on RBF neural network in prediction of landslide Displacement. In: Proceedings of 2008 International Workshop on Chaos-Fractals Theories and Applications & the 9th International Conference for Young Computer Scientists, 2707–2712
29
Z Liu, T Gu, X Kang (2017). The influence of the rising of groundwater level on the stability of loess slope. Ground Water, 39(6):61–63+162
30
Z Liu, J Shao, W Xu, H Chen, C Shi (2014). Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides, 11(5): 889–896 https://doi.org/10.1007/s10346-013-0443-z
31
F Lu, T Jiang (2017). The deformation forecast model of landslides based on multiple factors and taylor series. Journal of Geodesy and Geodynamics, (37): 1029–1032
32
F Miao, Y Wu, Y Xie, Y Li (2018). Prediction of landslide displacement with step-like behavior based on multi algorithm optimization and a support vector regression model. Landslides, 15(3): 475–488 https://doi.org/10.1007/s10346-017-0883-y
33
C Polykretis, M Ferentinou, C Chalkias (2015). A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bull Eng Geol Environ, 74(1): 27–45 https://doi.org/10.1007/s10064-014-0607-7
34
B Pradhan, S Lee (2010). Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides, 7(1): 13–30 https://doi.org/10.1007/s10346-009-0183-2
35
B Pradhan (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci, 51: 350–365 https://doi.org/10.1016/j.cageo.2012.08.023
36
H Qian, G Lei, P Yu (2012). Multiple fading factors Kalman filter and its application in SINS initial alignment. J Chin Inert Technol, 20: 287–291
37
M Saito (1965). Forecasting the time of occurrence of a slope failure. In: Proceedings of 6th International Congress of Soil Mechanics and Foundation Engineering, Montreal: 537–541
38
E Tazik, Z Jahantab, M Bakhtiari, A Rezaei, S K Alavipanah (2014). Landslide susceptibility mapping by combining the three methods fuzzy logic, frequency ratio and analytical hierarchy process in Dozain basin. Int Arch Photogramm Remote Sens Spat Inf Sci, XL-2 (W3): 267–272 https://doi.org/10.5194/isprsarchives-XL-2-W3-267-2014
N Wang, Y Yao (2008). Characteristics and mechanism of landslides in loess during freezing and thawing periods in seasonally frozen ground regions. Journal of Disaster Prevention and Mitigation Engineering, 28(02): 163–166
42
Y Wu, W Teng, Y Li (2007). Application of grey-neural network model to landslide deformation prediction. Chinese Journal of Rock Mechanics and Engineering, 26(03): 632–636
43
C Xu, F C Dai, X Xu, Y H Lee (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145–146: 70–80 https://doi.org/10.1016/j.geomorph.2011.12.040
44
S L Xu, R Q Niu (2018). Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Comput Geosci, 111: 87–96 https://doi.org/10.1016/j.cageo.2017.10.013
45
Y Xu, Y Tang, X Li, G Ye (2011). The landslide deformation prediction with improved Euler method of gray system model GM(1,1). Hydrogeology Eng Geol, 38(1): 110–113
46
A Yalcin, S Reis, A C Aydinoglu, T Yomralioglu (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena, 85(3): 274–287 https://doi.org/10.1016/j.catena.2011.01.014
47
T Yan (1988). Statistical prediction method of landslide. In: Landslide Anthology. Beijing: China Railway Publishing House
48
Y X Yang, H He, G Xu (2001). Adaptively robust filtering for kinematic geodetic positioning. J Geod, 75(2-3): 109–116 https://doi.org/10.1007/s001900000157
Y X Yang, W Gao, X Zhang (2010). Robust Kalman filtering with constraints: a case study for integrated navigation. J Geod, 84(6): 373–381 https://doi.org/10.1007/s00190-010-0374-6
51
J Zhang, Z Q Liu, H Wang, Z L Zhang (2012). Landslide deformation monitoring analysis and forecast using Kalman filtering considering rainfall. Science of Surveying and Mapping, 37(6): 58–61
52
J Zhang, K L Yin, J Wang, F Huang (2015). Displacement prediction of baishuihe landslide based on time series and PSO-SVR model. Chinese Journal of Rock Mechanics and Engineering, 34(2): 382–391
53
C Zhou, K L Yin, Y Cao, E Intrieri, B Ahmed, F Catani (2018). Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides, 15(11): 2211–2225 https://doi.org/10.1007/s10346-018-1022-0
54
Z X Zou, C G Xiong, H M Tang, R E Criss, A Su, X Liu (2017). Prediction of landslide runout based on influencing factor analysis. Environ Earth Sci, 76(21): 723 https://doi.org/10.1007/s12665-017-7075-x
