Abstract
The displacement prediction of an active landslide is a complicated and challenging problem worldwide. Currently, most prediction experiments focus on the mechanism model and fail to integrate with the influence factors. In this paper, a method of landslide data assimilation is proposed to predict the landslide displacement, and real data tests are carried out to support the theoretical calculation. The obtained results show better performance of the proposed method compared with the general method. Data assimilation shows a relatively 40.32% improve in RMSE. This study can strongly confirm our proposed method presents a superior quality, improves the accuracy of landslide deformation prediction. And it is expected to be significant for the landslide displacement prediction in the future.
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Ardiclioglu M, Kuriqi A (2019) Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey. SN Appl Sci 1(9):1080. https://doi.org/10.1007/s42452-019-1141-9
Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188. https://doi.org/10.1109/78.978374
Casagli N, Catani F, Ventisette CD et al (2010) Monitoring, prediction, and early warning using ground-based radar interferometry. Landslides 7(3):291–301. https://doi.org/10.1007/s10346-010-0215-y
Corominas J, Moya J, Ledesma A, Lloret A, Gili JA (2005) Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain). Landslides 2:83–96. https://doi.org/10.1007/s10346-005-0049-1
Daley R (1991) Atmospheric Data Analysis. Cambridge Atmospheric and Space Science Series, Cambridge University
Desai CS, Samtani NC, Vulliet L (1995) Constitutive modeling and analysis of creeping slopes. J Geotech Eng 122(1):43–56. https://doi.org/10.1061/(ASCE)0733-9410(1995)121:1(43)
Eicker A, Schumacher M, Kusche J, Döll P, Schmied HM (2014) Calibration/data assimilation approach for integrating GRACE data into the WaterGAP Global Hydrology Model (WGHM) using an ensemble Kalman Filter: first results. Surv Geophys 35:1285–1309. https://doi.org/10.1007/s10712-014-9309-8
Entekhabi D, Nakamura H, Njoku EG (1994) Solving the inverse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed observations. IEEE Trans Geosci Remote Sens 32(2):438–448. https://doi.org/10.1109/36.295058
Evensen G (2009) Data Assimilation—The Ensemble Kalman Filter, 2nd ed. Springer: Berlin, Germany
Frattini P, Crosta GB (2013) The role of material properties and landscape morphology on landslide size distributions. Earth Planet Sci Lett 361:310–319. https://doi.org/10.1016/j.epsl.2012.10.029
Han X, Li X (2008) An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sens Environ 112(4):1434–1449. https://doi.org/10.1016/j.rse.2007.07.008
Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Wea Rev 126:796–811. https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
Huang CL, Li X, Lu L et al (2008) Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens Environ 112(3):888–900. https://doi.org/10.1016/j.rse.2007.06.026
Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186. https://doi.org/10.1016/j.enggeo.2017.01.016
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y
Intrieri E, Gigli G, Casagli N, Nadim F (2013) Brief communication “Landslide Early Warning System: toolbox and general concepts”. Nat Hazards Earth Syst Sci 13:85–90. https://doi.org/10.5194/nhess-13-85-2013
Kuriqi A (2016) Assessment and quantification of meteorological data for implementation of weather radar in mountainous regions. Mausam J Meteorol Dep India 67(4):789–802
Kuriqi A, Kocileri G, Ardiclioglu M (2020) Potential of Meyer-Peter and Müller approach for estimation of bed-load sediment transport under different hydraulic regimes. Model Earth Syst Environ 6:129–137. https://doi.org/10.1007/s40808-019-00665-0
Liu Z, Shao J, Xu W, Chen H, Shi C (2014) Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides 11:889–896. https://doi.org/10.1007/s10346-013-0443-z
Luo J (2015) Research on deformation mechanism and disastrous factors of Xishan landslide in Li county. Chengdu Univerisity of Technology, Sichuan
Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arab J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5
Margulis SA, McLaughlin D, Entekhabi D et al (2002) Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment. Water Resour Res 38(12):35-1–35-18. https://doi.org/10.1029/2001WR001114
Mclaughlin D (2002) An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering. Adv Water Res 25(8-12):1275–1286. https://doi.org/10.1016/S0309-1708(02)00055-6
Miao S, Hao X, Guo X, Wang Z, Liang M (2017) Displacement and landslide forecast based on an improved version of Saito’s method together with the Verhulst-Grey model. Arab J Geosci 10:53. https://doi.org/10.1007/s12517-017-2838-y
Nakano S, Ueno G, Higuchi T (2007) Merging particle filter for sequential data assimilation. Nonlin Process Geophys 14:395–408. https://doi.org/10.5194/npg-14-395-2007
Olivier LE, Huang B, Craig IK (2012) Dual particle filters for state and parameter estimation with application to a run-of-mine ore mill. J Process Control 22(4):710–717. https://doi.org/10.1016/j.jprocont.2012.02.009
Qiang X, Huang RQ, Li XZ (2004) Research progress in time forecast and prediction of landslides. Adv Earth Sci 19(3):478–483
Qin J, Liang S, Yang K, Kaihotsu I, Liu R, Koike T (2009) Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal. J Geophys Res 114:D15103. https://doi.org/10.1029/2008JD011358
Reichle RH, Mclaughlin DB, Entekhabi D (2002) Hydrologic data assimilation with the ensemble Kalman filter. Mon Weather Rev 130(1):103–114. https://doi.org/10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2
Robinson AR, Lermusiaux PFJ (2001) Data assimilation in models. Encyclopedia of Ocean Sciences 623–634. https://doi.org/10.1006/rwos.2001.0404
Rodell M, Houser PR, Jambor U, Gottschalck J et al (2004) The global land data assimilation system. Bull Amer Meteor Soc 85:381–394. https://doi.org/10.1175/BAMS-85-3-381
Runqiu H (2009) Some catastrophic landslides since the twentieth century in the southwest of China. Landslides 6:69–81. https://doi.org/10.1007/s10346-009-0142-y
Schuster RL, Highland LM. (2001) Socioeconomic and environmental impacts of landslides in the Western Hemisphere
Song D, Chen J, Cai J (2018) Deformation monitoring of rock slope with weak bedding structural plane subject to tunnel excavation. Arab J Geosci 11:251. https://doi.org/10.1007/s12517-018-3602-7
Sun ZL, Choi TM, Au KF, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419. https://doi.org/10.1016/j.dss.2008.07.009
Talagrand O (1997) Assimilation of observations, an introduction. J Meteorol Soc Jpn 75:81–99
Thiebes B, Bell R, Glade T, Jäger S, Mayer J, Anderson M, Holcombe L (2014) Integration of a limit-equilibrium model into a landslide early warning system. Landslides 11:859–875. https://doi.org/10.1007/s10346-013-0416-2
Wen T, Tang H, Wang Y, Lin C, Xiong C (2017) Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China. Nat Hazards Earth Syst Sci 17:2181–2198. https://doi.org/10.5194/nhess-17-2181-2017
Wikle CK (2005) Atmospheric Modeling, Data Assimilation, and Predictability. Technometrics 47:521–521. https://doi.org/10.1198/tech.2005.s326
Yin XG, Yu WD (2007) The virtual manufacturing model of the worsted yarn based on artificial neural networks and grey theory. Appl Math Comput 185(1):322–332. https://doi.org/10.1016/j.amc.2006.06.117
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Wang, J., Nie, G. & Xue, C. Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors. Arab J Geosci 13, 460 (2020). https://doi.org/10.1007/s12517-020-05452-1
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DOI: https://doi.org/10.1007/s12517-020-05452-1