Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Abstract

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions, which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes. This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest (RF), logical regression (LR), and gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology and comparing them to their respective original models. The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false positive errors. The LR-InSAR model showed the best performance in dynamic landslide susceptibility assessment at both regional and smaller scale, particularly when identifying areas of high and very high susceptibility. Modeling results were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. This study is of great significance to accurately assess dynamic landslide susceptibility and to help reduce and prevent landslide risk.

Keywords

Landslide susceptibility / Machine learning models / InSAR / Dynamic assessment

CRediT authorship contribution statement

Yingdong Wei: Writing – original draft, Visualization, Methodology, Formal analysis. Haijun Qiu: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. Zijing Liu: Methodology, Investigation. Wenchao Huangfu: Validation, Resources. Yaru Zhu: Resources, Data curation. Ya Liu: Resources, Methodology. Dongdong Yang: Visualization, Validation. Ulrich Kamp: Writing – review & editing, Formal analysis.

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

This work was funded by the National Natural Science Foundation of China (Grant No. 42271078), and Key Research and Development Program of Shaanxi (2024SF-YBXM-669).

References

A.M. Al-Abadi, N.A. Al-Najar. Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness. Nat. Hazards, 100 (2) (2019), pp. 461-491,

H. An, T.T. Viet, G. Lee, Y. Kim, M. Kim, S. Noh, J. Noh. Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow. Environ. Modell. Softw., 85 (2016), pp. 172-183,

A. Arabameri, S. Saha, J. Roy, W. Chen, T. Blaschke, D. Tien Bui. Landslide susceptibility evaluation and management using different machine learning methods in The Gallicash River Watershed. Iran. Remote Sens., 12 (2020), p. 475,

L. Ayalew, H. Yamagishi. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains Central Japan. Geomorphology, 65 (1–2) (2005), pp. 15-31,

S. Baharvand, J. Rahnamarad, S. Soori, N. Saadatkhah. Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS. Environ. Earth Sci., 79 (10) (2020), pp. 1-10,

P. Berardino, G. Fornaro, R. Lanari, E. Sansosti. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sensing, 40 (11) (2002), pp. 2375-2383,

L. Breiman. Random forests. Mach. Learn., 45 (1) (2001), pp. 5-32

C. Cao, K. Zhu, P. Xu, B. Shan, G. Yang, S. Song. Refined landslide susceptibility analysis based on InSAR technology and UAV multi-source data. J. Clean. Prod., 368 (2022), Article 133146,

Z. Chang, F. Huang, J. Huang, S.H. Jiang, Y. Liu, S.R. Meena, F. Catani. An updating of landslide susceptibility prediction from the perspective of space and time. Geosci. Front., 14 (5) (2023), Article 101619,

A. Ciampalini, F. Raspini, D. Lagomarsino, F. Catani, N. Casagli. Landslide susceptibility map refinement using PSInSAR data. Remote Sens. Environ., 184 (2016), pp. 302-315,

M. Crosetto, O. Monserrat, M. Cuevas-González, N. Devanthéry, B. Crippa. Persistent Scatterer Interferometry: A review. ISPRS-J. Photogramm. Remote Sens., 115 (2016), pp. 78-89,

M. Devara, A. Tiwari, R. Dwivedi. Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis. Geomat. Nat. Hazards Risk, 12 (1) (2021), pp. 675-693,

J. Dong, M. Liao, Q. Xu, L. Zhang, M. Tang, J. Gong. Detection and displacement characterization of landslides using multi-temporal satellite SAR interferometry: A case study of Danba County in the Dadu River Basin. Eng. Geol., 240 (2018), pp. 95-109,

J. Dou, A.P. Yunus, D. Tien Bui, A. Merghadi, M. Sahana, Z. Zhu, C.W. Chen, K. Khosravi, Y. Yang, B.T. Pham. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island. Japan. Sci. Total Environ., 662 (2019), pp. 332-346,

J. Du, Z. Li, C. Song, W. Zhu, Y. Ji, C. Zhang, B. Chen, S. Su. InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 16 (2023), pp. 3819-3830,

R. Fell, J. Corominas, C. Bonnard, L. Cascini, E. Leroi, W.Z. Savage. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng. Geol., 102 (3–4) (2008), pp. 85-98,

A. Ferretti, C. Prati, F. Rocca. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sensing, 39 (1) (2001), pp. 8-20,

P. Frattini, G. Crosta, A. Carrara. Techniques for evaluating the performance of landslide susceptibility models. Eng. Geol., 111 (1–4) (2010), pp. 62-72,

J.H. Friedman. Greedy function approximation: A gradient boosting machine. Ann. Stat., 29 (5) (2001), pp. 1189-1232,

M.J. Froude, D.N. Petley. Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci., 18 (8) (2018), pp. 2161-2181,

Z. Fu, C. Li, W. Yao. Landslide susceptibility assessment through TrAdaBoost transfer learning models using two landslide inventories. Catena, 222 (2023), Article 106799,

S. Gantimurova, A. Parshin, V. Erofeev. GIS-Based Landslide Susceptibility Mapping of the Circum-Baikal Railway in Russia Using UAV Data. Remote Sens., 13 (18) (2021), p. 3629,

S.L. Gariano, F. Guzzetti. Landslides in a changing climate. Earth Sci. Rev., 162 (2016), pp. 227-252,

S.K. Gupta, D.P. Shukla. Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin North-Western Himalayas. Landslides, 20 (5) (2023), pp. 933-949,

Q. He, H. Shahabi, A. Shirzadi, S. Li, W. Chen, N. Wang, H. Chai, H. Bian, J. Ma, Y. Chen, X. Wang, K. Chapi, B.B. Ahmad. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci. Total Environ., 663 (2019), pp. 1-15,

H. Hong. Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model. Ecol. Ind., 147 (2023), Article 109968,

A. Hooper. A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys. Res. Lett., 35 (16) (2008), p. L16302,

F. Huang, Z. Cao, J. Guo, S.H. Jiang, S. Li, Z. Guo. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena, 191 (2020), Article 104580,

F. Huang, J. Yan, X. Fan, C. Yao, J. Huang, W. Chen, H. Hong. Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions. Geosci. Front., 13 (2) (2022), Article 101317,

S. Hussain, S. Hongxing, M. Ali, M. Ali. PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan. Geocarto Int., 37 (13) (2021), pp. 3941-3962,

G.F. Jenks. The data model concept in statistical mapping. Int. Yearb. Cartogr., 7 (1967), pp. 186-190

Z. Jiang, M. Wang, K. Liu. Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu. Remote Sens., 15 (3) (2023), p. 798,

V.R. Kohestani, M. Hassanlourad, A. Ardakani. Evaluation of liquefaction potential based on CPT data using random forest. Nat. Hazards, 79 (2) (2015), pp. 1079-1089,

C. Kumar, G. Walton, P. Santi, C. Luza. An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru. Remote Sens., 15 (5) (2023), p. 1376,

Y. Li, H. Hong. Modelling flood susceptibility based on deep learning coupling with ensemble learning models. J. Environ. Manage., 325 (2023), Article 116450,

M. Li, L. Zhang, C. Ding, W. Li, H. Luo, M. Liao, Q. Xu. Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event. Remote Sens. Environ., 240 (2020), Article 111695,

Z. Liang, C. Wang, Z. Duan, H. Liu, X. Liu, U.J. Khan, K.. A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping. Remote Sens., 13 (8) (2021), p. 1464,

Y. Liu, H. Qiu, U. Kamp, N. Wang, J. Wang, C. Huang, B. Tang. Higher temperature sensitivity of retrogressive thaw slump activity in the Arctic compared to the Third Pole. Sci. Total Environ., 914 (2024), Article 170007,

P. Ma, W. Wang, B. Zhang, J. Wang, G. Shi, G. Huang, F. Chen, L. Jiang, H. Lin. Remotely sensing large- and small-scale ground subsidence: A case study of the Guangdong-Hong Kong-Macao Greater Bay Area of China. Remote Sens. Environ., 232 (2019), Article 111282,

P. Ma, Y. Cui, W. Wang, H. Lin, Y. Zhang. Coupling InSAR and numerical modeling for characterizing landslide movements under complex loads in urbanized hillslopes. Landslides, 18 (5) (2021), pp. 1611-1623,

S. Ma, H. Qiu, Y. Zhu, D. Yang, B. Tang, D. Wang, L. Wang, M. Cao. Topographic Changes, Surface Deformation and Movement Process before, during and after a Rotational Landslide. Remote Sens., 15 (3) (2023), p. 662,

V. Medina, M. Hürlimann, Z. Guo, A. Lloret, J. Vaunat. Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. Catena, 201 (2021), Article 105213,

F. Miao, Q. Ruan, Y. Wu, Z. Qian, Z. Kong, Z. Qin. Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model. Remote Sens., 15 (22) (2023), p. 5427,

Y. Morishita, M. Lazecky, T. Wright, J. Weiss, J. Elliott, A. Hooper. LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. Remote Sens., 12 (3) (2020), p. 424,

A. Novellino, M. Cesarano, P. Cappelletti, D. Di Martire, M. Di Napoli, M. Ramondini, A. Sowter, D. Calcaterra. Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques. Catena, 203 (2021), Article 105317,

C. Noviello, S. Verde, V. Zamparelli, G. Fornaro, A. Pauciullo, D. Reale, G. Nicodemo, S. Ferlisi, G. Gulla, D. Peduto. Monitoring Buildings at Landslide Risk With SAR: A Methodology Based on the Use of Multipass Interferometric Data. IEEE Geosci. Remote Sens. Mag., 8 (1) (2020), pp. 91-119,

K. Pawluszek, A. Borkowski. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake. Poland. Nat. Hazards, 86 (2) (2017), pp. 919-952,

D. Petley. Global patterns of loss of life from landslides. Geology, 40 (10) (2012), pp. 927-930,

B.T. Pham, A. Shirzadi, D. Tien Bui, I. Prakash, M. Dholakia. A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area. India. Int. J. Sediment Res., 33 (2) (2018), pp. 157-170,

H. Qiu, L. Su, B. Tang, D. Yang, M. Ullah, Y. Zhu, U. Kamp. The effect of location and geometric properties of landslides caused by rainstorms and earthquakes. Earth Surf. Proc. Land., 1–13 (2024),

D. Raucoules, M. de Michele, J.P. Malet, P. Ulrich. Time-variable 3D ground displacements from high-resolution synthetic aperture radar (SAR). application to La Valette landslide (South French Alps). Remote Sens. Environ., 139 (2013), pp. 198-204,

P. Reichenbach, M. Rossi, B.D. Malamud, M. Mihir, F. Guzzetti. A review of statistically-based landslide susceptibility models. Earth Sci. Rev., 180 (2018), pp. 60-91,

T. Ren, L. Gao, W. Gong. An ensemble of dynamic rainfall index and machine learning method for spatiotemporal landslide susceptibility modeling. Landslides, 21 (2) (2023), pp. 257-273,

N. Shahzad, X. Ding, S. Wu, H. Liang. Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR. Remote Sens., 12 (20) (2020), p. 3442,

Y. Shan, Z. Xu, S. Zhou, H. Lu, W. Yu, Z. Li, X. Cao, P. Li, W. Li. Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province Southwestern China. Remote Sens., 16 (1) (2023), p. 99,

N. Sharma, M. Saharia, G. Ramana. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena, 235 (2024), Article 107653,

C. Shen, Z. Feng, C. Xie, H. Fang, B. Zhao, W. Ou, Y. Zhu, K. Wang, H. Li, H. Bai, A. Mannan, P. Chen. Refinement of Landslide Susceptibility Map Using Persistent Scatterer Interferometry in Areas of Intense Mining Activities in the Karst Region of Southwest China. Remote Sens., 11 (23) (2019), p. 2821,

X. Shi, C. Yang, L. Zhang, H. Jiang, M. Liao, L. Zhang, X. Liu. Mapping and characterizing displacements of active loess slopes along the upstream Yellow River with multi-temporal InSAR datasets. Sci. Total Environ., 674 (2019), pp. 200-210,

L. Solari, S. Bianchini, R. Franceschini, A. Barra, O. Monserrat, P. Thuegaz, D. Bertolo, M. Crosetto, F. Catani. Satellite interferometric data for landslide intensity evaluation in mountainous regions. Int. J. Appl. Earth Obs. Geoinf., 87 (2020), Article 102028,

S. Steger, V. Mair, C. Kofler, M. Pittore, M. Zebisch, S. Schneiderbauer. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling-Benefits of exploring landslide data collection effects. Sci. Total Environ., 776 (2021), Article 145935,

H. Sun, W. Li, M. Scaioni, J. Fu, X. Guo, J. Gao. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas. Geomorphology, 433 (2023), Article 108723,

N. Wang, H. Zhang, A. Dahal, W. Cheng, M. Zhao, L. Lombardo. On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values. Geosci. Front., 15 (4) (2024), Article 101800,

J. Wasowski, F. Bovenga. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Eng. Geol., 174 (2014), pp. 103-138,

Z. Xie, G. Chen, X. Meng, Y. Zhang, L. Qiao, L. Tan. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin China. Environ. Earth Sci., 76 (8) (2017), pp. 1-19,

T. Yao, L. Thompson, W. Yang, W. Yu, Y. Gao, X. Guo, X. Yang, K. Duan, H. Zhao, B. Xu, J. Pu, A. Lu, Y. Xiang, D.B. Kattel, D. Joswiak. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Chang., 2 (9) (2012), pp. 663-667,

B. Ye, H. Qiu, B. Tang, Y. Liu, Z. Liu, X. Jiang, D. Yang, M. Ullah, Y. Zhu, U. Kamp. Creep deformation monitoring of landslides in a reservoir area. J. Hydrol., 632 (2024), Article 130905,

Y. Yi, Z. Zhang, W. Zhang, H. Jia, J. Zhang. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region. Catena, 195 (2020), Article 104851,

A.M. Youssef, H.R. Pourghasemi, Z.S. Pourtaghi, M.M. Al-Katheeri. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides, 13 (5) (2015), pp. 839-856,

K. Youssef, K. Shao, S. Moon, L.S. Bouchard. Landslide susceptibility modeling by interpretable neural network. Commun. Earth Environ., 4 (1) (2023), p. 162,

G. Zhang, S. Wang, Z. Chen, Y. Liu, Z. Xu, R. Zhao. Landslide susceptibility evaluation integrating weight of evidence model and InSAR results, west of Hubei Province, China. Egypt. J. Remote Sens. Space Sci., 26 (1) (2023), pp. 95-106,

W. Zhang, C. Wu, H. Zhong, Y. Li, L. Wang. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci. Front., 12 (1) (2021), pp. 469-477,

Z. Zhao, J. Chen, J. Yao, K. Xu, Y. Liao, H. Xie, X. Gan. An improved spatial case-based reasoning considering multiple spatial drivers of geographic events and its application in landslide susceptibility mapping. Catena, 223 (2023), Article 106940,

C. Zhou, Y. Cao, X. Hu, K. Yin, Y. Wang, F. Catani. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area. Landslides, 19 (7) (2022), pp. 1585-1597,

Y. Zhu, H. Qiu, P. Cui, Z. Liu, B. Ye, D. Yang, U. Kamp. Early detection of potential landslides along high-speed railway lines: A pressing issue. Earth Surf. Proc. Land., 48 (15) (2023), pp. 3302-3314,