Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning

Abstract

With the development of landslide monitoring system, many attempts have been made to predict landslide failure-time utilizing monitoring data of displacements. Classical models (e.g., Verhulst, GM (1,1), and Saito models) that consider the characteristics of landslide displacement to determine the failure-time have been investigated extensively. In practice, monitoring is continuously implemented with monitoring data-set updated, meaning that the predicted landslide life expectancy (i.e., the lag between the predicted failure-time and time node at each instant of conducting the prediction) should be re-evaluated with time. This manner is termed “dynamic prediction”. However, the performances of the classical models have not been discussed in the context of the dynamic prediction yet. In this study, such performances are investigated firstly, and disadvantages of the classical models are then reported, incorporating the monitoring data from four real landslides. Subsequently, a more qualified ensemble model is proposed, where the individual classical models are integrated by machine learning (ML)-based meta-model. To evaluate the quality of the models under the dynamic prediction, a novel indicator termed “discredit index (β)” is proposed, and a higher value of β indicates lower prediction quality. It is found that Verhulst and Saito models would produce predicted results with significantly higher β, while GM (1,1) model would indicate results with the highest mean absolute error. Meanwhile, the ensemble models are found to be more accurate and qualified than the classical models. Here, the performance of decision tree regression-based ensemble model is the best among the various ML-based ensemble models.

Keywords

Dynamic prediction / Landslide life expectancy / Machine learning / Ensemble system

CRediT authorship contribution statement

Shailesh Kumar Yadav: Conceptualization, Data curation, Visualization, Writing – original draft. AL. Ramanathan: Resources, Supervision, Writing – review & editing. Chidambaram Sabarathinam: Writing – review & editing. Alok Kumar: Writing – review & editing. Manoj Kumar: Writing – review & editing. Anshula Dhiman: Investigation, Visualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The author is grateful to the CSIR (Government of India) for offering the CSIR-SR Fellowship. Author is also thankful to IUAC for extending the XRD and XRF facility, funded by the Ministry of Earth Sciences (MoES) under the Geochronology project [MoES/P.O.(Seismic)8(09)-Geochron/2012]. The authors are thankful to the CIF (Central Instrumentation Facility), School of Environmental Sciences, and AIRF (Advanced Instrumentation Research Facility), Jawaharlal Nehru University, New Delhi, India, for providing additional support and instrumentation facility. This research was primarily supported by the CSIR fellowship. XRD and XRF analyses were conducted at the IUAC, under UGC, allowing access to facilities without any charge. Other analyses were performed internally at Jawaharlal Nehru University.

References

A. Abdollahi, B. Pradhan. Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model. Sci. Total Environ., 879 (2023), Article 163004

S. Abdollahizad, M.A. Balafar, B. Feizizadeh, A.B. Sangar, K. Samadzamini. Stacking ensemble approach in data mining methods for landslide prediction. J. Supercomput., 79 (8) (2023), pp. 8583-8610

H.A.H. Al-Najjar, B. Pradhan, G. Beydoun, R. Sarkar, H.-J. Park, A. Alamri. A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset. Gondwana Res., 123 (2023), pp. 107-124

R.L. Baum, J.W. Godt. Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides, 7 (3) (2010), pp. 259-272

Z.L. Cai, W.Y. Xu, Y.D. Meng, C. Shi, R.B. Wang. Prediction of landslide displacement based on GA-LSSVM with multiple factors. Bull. Eng. Geol. Environ., 75 (2) (2016), pp. 637-646

T. Carlà, E. Intrieri, F. Di Traglia, T. Nolesini, G. Gigli, N. Casagli. Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses. Landslides, 14 (2) (2017), pp. 517-534

B.G. Chae, H.J. Park, F. Catani, A. Simoni, M. Berti. Landslide prediction, monitoring and early warning: a concise review of state of the art. Geosci. J., 21 (6) (2017), pp. 1033-1070

M. Davawala, T. Joshi, M. Shah. Compressive strength prediction of high-strength concrete using machine learning. Emergent Mater., 6 (1) (2023), pp. 321-335

A. Dikshit, B. Pradhan. Interpretable and explainable AI (XAI) model for spatial drought prediction. Sci. Total Environ., 801 (2021), Article 149797

J. Du, K.L. Yin, S. Lacasse. Displacement prediction in colluvial landslides, ThreeGorges Reservoir, China. Landslides, 10 (2013), pp. 203-218

X.M. Fan, Q. Xu, J. Liu, S.S. Subramanian, C.Y. He, X. Zhu. Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China. Landslides, 16 (4) (2019), pp. 2445-2457

Q. Ge, H. Sun, Z. Liu, X. Wang. A data-driven intelligent model for landslide displacement prediction. Geol. J., 58 (6) (2023), pp. 2211-2230

A. Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media Inc, Sebastopol, CA, USA (2017), p. 450

X. Gu, L. Wang, Q. Ou, W. Zhang. Efficient stochastic analysis of unsaturated slopes subjected to various rainfall intensities and patterns. Geosci. Front., 14 (1) (2023), Article 101490

Z. Guo, L. Chen, L. Gui, J. Du, K. Yin, H.M. Do. Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model. Landslides, 17 (3) (2020), pp. 567-583

X.D. Hu, H. Zhang, H.B. Mei, D.H. Xiao, Y.Y. Li, M.D. Li. Landslide susceptibility mapping using the stacking ensemble machine learning method in Lushui, Southwest China. Appl. Sci., 10 (11) (2020), p. 4016

C.J. Huang, Y.Z. Cao, L. Zhou. Application of optimized GM (1,1) model based on EMD in landslide deformation prediction. Comput. Appl. Math., 40 (8) (2021), p. 261

F.M. Huang, J.S. Huang, S.H. Jiang, C.B. Zhou. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng. Geol., 218 (2017), pp. 173-186

E. Intrieri, T. Carlà, G. Gigli. Forecasting the time of failure of landslides at slope-scale: A literature review. Earth-Sci. Rev., 193 (2019), pp. 333-349

N. Kardani, A. Zhou, M. Nazem, S.L. Shen. Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotech. Geol. Eng., 38 (2) (2020), pp. 2271-2291

N. Kardani, A. Zhou, M. Nazem, S.L. Shen. Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J. Rock Mech. Geotech. Eng., 13 (1) (2021), pp. 188-201

Y.M. Lau, K.L. Wang, Y.H. Wang, W.H. Yiu, G.H. Ooi, P.S. Tan. Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system. Landslides, 20 (2) (2023), pp. 271-296

T. Li, M. Chen, L. Wang, Y. Zhou. Time prediction of landslides using Verhulst inverse-function model. Proceedings of 7th international symposium on landslides, Trondheim (1996), pp. 1289-1293

Z.C. Li, P.F. Cheng, J.J. Zheng. Prediction of time to slope failure based on a new model. Bull. Eng. Geol. Environ., 80 (7) (2021), pp. 5279-5291

X.Z. Li, J.M. Kong. Application of GA–SVM method with parameter optimization for landslide development prediction. Nat. Hazards Earth Syst. Sci., 14 (3) (2014), pp. 525-533

C. Lian, Z.G. Zeng, W. Yao, H.M. Tang. Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stoch. Environ. Res. Risk Assess., 28 (8) (2014), pp. 1957-1972

Z.B. Liu, J.F. Shao, W.Y. Xu, H.J. Chen, C. Shi. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides, 11 (5) (2014), pp. 889-896

S.F. Liu, Y. Yang. A brief introduction to grey systems theory. Grey Syst., 2 (2011), pp. 89-104

J.W. Ma, H.M. Tang, X. Liu, X.L. Hu, M.J. Sun, Y.J. Song. Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China. Landslides, 14 (3) (2017), pp. 1275-1281

A. Manconi, D. Giordan. Landslide failure forecast in near-real-time. Geomat. Nat. Hazards Risk, 7 (2) (2016), pp. 639-648

S.J. Miao, X. Hao, X.L. Guo, Z.M. Wang, M.C. Liang. Displacement and landslide forecast based on an improved version of Saito’s method together with the Verhulst-Grey model. Arab. J. Geosci., 10 (3) (2017), p. 53

F.S. Miao, Y.P. Wu, Y.H. Xie, Y.N. Li. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides, 15 (3) (2018), pp. 475-488

A. Mufundirwa, Y. Fujii, J. Kodama. A new practical method for prediction of geomechanical failure-time. Int. J. Rock Mech. Min. Sci., 47 (7) (2010), pp. 1079-1090

B.Q.V. Nguyen, Y.T. Kim. Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches. Bull. Eng. Geol. Environ., 80 (6) (2021), pp. 4291-4321

B.T. Pham, A. Jaafari, T.V. Phong, H.P.H. Yen, T.T. Tuyen, V.V. Luong. Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geosci. Front., 12 (3) (2021), Article 101105

X. Qi, X. Zhu, Q. Xu, K.Y. Zhao, D.D. Huo, H. Wang. Improvement and application of landslide proximity time prediction method based on saito model (China). J. Eng. Geol., 28 (4) (2020), pp. 832-839

R. Ray, D. Kumar, P. Samui, L.B. Roy, A.T.C. Goh, W.G. Zhang. Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. Geosci. Front., 12 (1) (2021), pp. 375-383

J. Rodriguez, E. Deane, M.T. Hendry, R. Macciotta, T. Evans, C. Gräpel. Practical evaluation of single-frequency dGNSS for monitoring slow-moving landslides. Landslides, 18 (11) (2021), pp. 3671-3684

G. Rong, K. Li, Z. Tong, X. Liu, J. Zhang, Y. Zhang. Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation. Geosci. Front., 14 (3) (2023), Article 101541

N.D. Rose, O. Hungr. Forecasting potential rock slope failure in open pit mines using the inverse-velocity method. Int. J. Rock Mech. Min. Sci., 44 (2) (2007), pp. 308-320

S. Saha, J. Roy, T.K. Hembram, B. Pradhan, A. Dikshit, K.N. Abdul Maulud. Comparison between deep learning and tree-based machine learning approaches for landslide susceptibility mapping. Water, 13 (19) (2021), p. 2664

M. Saito. Forecasting time of slope failure by tertiary creep. Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering, Mexico City (1969), pp. 677-683

A. Segalini, A. Valletta, A. Carri. Landslide time-of-failure forecast and alert threshold assessment: A generalized criterion. Eng. Geol., 245 (2018) (2018), pp. 72-80

A.R.M. Towfiqul Islam, S. Talukdar, S. Mahato, S. Kundu, K.U. Eibek, Q.B. Pham. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci. Front., 12 (3) (2021), Article 101075

Z.Y. Wang. Application of renewal gray GM (1,1) model to prediction of landslide deformation. J. Geosci. Environ. Prot., 05 (2017), pp. 148-154

S.L. Xu, R.Q. Niu. 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 (2018), pp. 87-96

Q. Xu, Y. Yuan, Y.P. Zeng, R. Hack. Some new pre-warning criteria for creep slope failure. Sci. China-Technol. Sci., 54 (1) (2011), pp. 210-220

C. Yang, L.L. Liu, F.M. Huang, L. Huang, X.M. Wang. Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples. Gondwana Res., 123 (2022), pp. 198-216

T.R. Zeng, H.G. Jiang, Q.L. Liu, K.L. Yin. Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model. Stoch. Environ. Res. Risk Assess., 36 (5) (2022), pp. 1353-1372

L.G. Zhang, X.Q. Chen, Y.G. Zhang, F.W. Wu, F. Chen, W.T. Wang. Application of GWO-ELM model to prediction of Caojiatuo landslide displacement in the Three Gorge Reservoir area. Water, 2020 (12) (2020), pp. 1-22

Y.M. Zhang, X.L. Hu, D.D. Tannant, G.C. Zhang, F.L. Tan. Field monitoring and deformation characteristics of a landslide with piles in the Three Gorges Reservoir area. Landslides, 15 (3) (2018), pp. 581-592

J. Zhang, Z. Wang, J. Hu, S. Xiao, W. Shang. Bayesian machine learning-based method for prediction of slope failure time. J. Rock Mech. Geotech. Eng., 14 (4) (2022), pp. 1188-1199

Q. Zhou. Deformation prediction of slopes based on grey verhulst model. Adv. Mater. Res., 594–597 (2012), pp. 347-351

X.P. Zhou, L.J. Liu, C. Xu. A modified inverse-velocity method for predicting the failure time of landslides. Eng. Geol., 268 (2020), Article 105521

X. Zhou, H. Wen, Z. Li, H. Zhang, W. Zhang. An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost. Geocarto Internat., 37 (26) (2022), pp. 13419-13450

C. Zhou, K.L. Yin, Y. Cao, B. Ahmed. Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng. Geol., 204 (2016), pp. 108-120