1,802
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River

, , &
Article: 2212833 | Received 14 Mar 2023, Accepted 05 May 2023, Published online: 06 Jun 2023

References

  • Ado M, Amitab K, Maji AK, Jasińska E, Gono R, Leonowicz Z, Jasiński M. 2022. Landslide susceptibility mapping using machine learning: a literature survey. Remote Sens. 14(13):3029.
  • Al-Najjar HA, Pradhan B, Beydoun G, Sarkar R, Park H-J, Alamri A., 2022. A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset. Gondwana Res. doi:10.1016/j.gr.2022.08.004.
  • Amatya P, Kirschbaum D, Stanley T, Tanyas H. 2021. Landslide mapping using object-based image analysis and open source tools. Eng Geol. 282:106000.
  • Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
  • Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I. 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.
  • Chen TQ, Guestrin C. 2016. XGBoost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM; p. 785–794.
  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H. 2017. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Hazard Risk. 8(2):950–973.
  • Chen W, Yan XS, Zhao Z, Hong HY, Bui DT, Pradhan B. 2019. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naïve Bayes and RBFNetwork models for the Long County area. Bull Eng Geol Environ. 78(1):247–266.
  • Chen WW, Zhang S. 2021. GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. Catena. 203:105344.
  • Collini E, Palesi L, Nesi P, Pantaleo G, Nocentini N, Rosi A. 2022. Predicting and understanding landslide events with explainable AI. IEEE Access. 10:31175–31189.
  • Costantini M. 1998. A novel phase unwrapping method based on network programming. IEEE Trans Geosci Remote Sens. 36(3):813–821.
  • Dong J, Liao MS, Xu Q, Zhang L, Tang MG, Gong JY. 2018. 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. 240:95–109.
  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu ZF, Chen CW, Han Z, Pham BT. 2020. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides. 17(3):641–658.
  • Du J, Glade T, Woldai T, Chai B, Zeng B. 2020. Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Eng Geol. 270:105572.
  • Ekmekcioğlu Ö, Başakın EE, Özger M., 2022a. Developing meta-heuristic optimization based ensemble machine learning algorithms for hydraulic efficiency assessment of storm water grate inlets. Urban Water J. 19(10):1093–1108.
  • Ekmekcioğlu Ö, Koc K., 2022b. Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards. Catena. 216:106379.
  • Fan XM, Xu Q, Alonso-Rodriguez A, Subramanian SS, Li WL, Zheng G, Dong XJ, Huang RQ. 2019. Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response. Landslides. 16(5):1003–1020.
  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ. 2008. Guidelines for landslide susceptibility, hazard and risk-zoning for land use planning. Eng Geol. 102(3–4):85–98.
  • Ferretti A, Savio G, Barzaghi R, Borghi A, Musazzi S, Novali F, Prati C, Rocca F. 2007. Submillimeter accuracy of InSAR time series: experimental validation. IEEE Trans Geosci Remote Sens. 45(5):1142–1153.
  • Frattini P, Crosta G, Carrara A. 2010. Techniques for evaluating the performance of landslide susceptibility models. Eng Geol. 111(1–4):62–72.
  • Gao BH, He Y, Zhang LF, Yao S, Yang W, Chen Y, He X, Zhao ZA, Chen HS. 2023. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation: a case study of Liujiaxia reservoir. Chin J Rock Mech Eng. 42(2):450–465.
  • Goetz JN, Brenning A, Petschko H, Leopold P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci. 81:1–11.
  • Goldstein RM, Werner CL. 1998. Radar interferogram filtering for geophysical applications. Geophys Res Lett. 25(21):4035–4038.
  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology. 31(1–4):181–216.
  • Hanssen RF. 2001. Radar interferometry: data interpretation and error analysis (remote sensing and digital image processing). Netherlands: Springer.
  • Harp EL, Keefer DK, Sato HP, Yagi H. 2011. Landslide inventories: the essential part of seismic landslide hazard analyses. Eng Geol. 122(1–2):9–21.
  • Havenith HB, Torgoev I, Meleshko A, Alioshin Y, Torgoev A, Danneels G. 2006. Landslides in the Mailuu-Suu Valley, Kyrgyzstan-Hazards and impacts. Landslides. 3(2):137–147.
  • Hu GS, Tian SF, Chen NS, Liu M, Somos-Valenzuela M. 2020. An effectiveness evaluation method for debris flow control engineering for cascading hydropower stations along the Jinsha River, China. Eng Geol. 266:105472.
  • Hu J, Liu JH, Li ZW, Zhu JJ, Wu LX, Sun Q, Wu WQ. 2021. Estimating three-dimensional coseismic deformations with the SM-VCE method based on heterogeneous SAR observations: selection of homogeneous points and analysis of observation combinations. Remote Sens Environ. 255:112298.
  • Huang FM, Cao ZS, Guo JF, Jiang SH, Li S, Guo ZZ. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580.
  • Huang FM, Chen JW, Tang ZP, Fan XM, Huang JS, Zhou CB, Chang ZL. 2021. Uncertainties of landslide susceptibility prediction due to different spatial resolutions and different proportions of training and testing datasets. Chin J Rock Mech Eng. 40(6):1155–1169.
  • Huang L, Sun Q, Hu J. 2022. Landslide sensitivity assessment and error correction based on InSAR and random forest method. Bull Surv Mapp. 10:13–20.
  • Huang Y, Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. Catena. 165:520–529.
  • Kavzoglu T, Sahin EK, Colkesen I. 2014. Landslide susceptibility mapping using GIS based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. 11(3):425–439.
  • Lissak C, Bartsch A, De Michele M, Gomez C, Maquaire O, Raucoules D, Roulland T. 2020. Remote sensing for assessing landslides and associated hazards. Surv Geophys. 41(6):1391–1435.
  • Liu P, Wei YM, Wang QJ, Chen Y, Xie JJ. 2020. Research on post-earthquake landslide extraction algorithm based on improved U-Net model. Remote Sens. 12(5):894.
  • Liu XH, Yao X, Yao JM. 2022. Accelerated movements of Xiaomojiu landslide observed with SBAS-InSAR and three-dimensional measurements, Upper Jinsha River, Eastern Tibet. Appl Sci-Basel. 12(19):9758.
  • Liu XJ, Zhao CY, Zhang Q, Lu Z, Li ZH, Yang CS, Zhu W, Liu-Zeng J, Chen LQ, Liu CJ. 2021. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Eng Geol. 284:106033.
  • Luo XG, Lin FK, Zhu S, Yu ML, Zhang Z, Meng LS, Peng J. 2019. Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors. PLoS One. 14(4):e0215134.
  • Lyons S, Sandwell D. 2003. Fault creep along the southern San Andreas from interferometric synthetic aperture radar, permanent scatterers, and stacking. J Geophys Res Solid Earth. 108(B1):2047.
  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B. 2020. Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev. 207:103225.
  • Nishiguchi T, Tsuchiya S, Imaizumi F. 2017. Detection and accuracy of landslide movement by InSAR analysis using PALSAR-2 data. Landslides. 14(4):1483–1490.
  • Novellino A, Cesarano M, Cappelletti P, Di Martire D, Di Napoli M, Ramondini M, Sowter A, Calcaterra D., 2021. Slow-moving landslide risk assessment combining machine learning and InSAR techniques. Catena. 203:105317.
  • Othman AA, Gloaguen R. 2013. River courses affected by landslides and implications for hazard assessment: a high resolution remote sensing case study in NE Iraq-W Iran. Remote Sens. 5(3):1024–1044.
  • Park S, Kim J. 2019. Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl. Sci. 9(5):942.
  • Pradhan B. 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.
  • Probst P, Wright MN, Boulesteix AL. 2019. Hyperparameters and tuning strategies for random forest. WIREs Data Mining Knowl Discov. 9:e1301.
  • Sahin EK, Colkesen I, Acmali SS, Akgun A, Aydinoglu AC. 2020b. Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack. Comput Geosci. 144:104592.
  • Sahin EK, Colkesen I. 2021. Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto Int. 36(11):1253–1275.
  • Sahin EK. 2020a. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto Int. 37(9):2441–2465.
  • Santos V, Datia N, Pato MPM. 2014. Ensemble feature ranking applied to medical data. In Conference on Electronics, Telecommunications and Computers-Cetc. vol.17, p. 223–230.
  • Shafique M, van der Meijde M, Khan MA. 2016. A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing perspective. J Asian Earth Sci. 118:68–80.
  • Song DQ, Che AL, Chen Z, Ge XR. 2018. Seismic stability of a rock slope with discontinuities under rapid water drawdown and earthquakes in large-scale shaking table tests. Eng Geol. 245:153–168.
  • Song Y, Niu R, Xu S, Ye R, Peng L, Guo T, Li S, Chen T. 2018. Landslide susceptibility Mapping based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China). IJGI. 8(1):4.
  • Stefan S, Volkmar M, Christian K, Massimiliano P, Marc Z, Stefan S. 2021. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling-Benefits of exploring landslide data collection effects. Sci Total Environ. 776:145935.
  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology. 249:119–136.
  • Wang SB, Zhuang JQ, Mu JQ, Zheng J, Zhan JW, Wang J, Fu YT. 2022. Evaluation of landslide susceptibility of the Ya’an-Linzhi section of the Sichuan-Tibet Railway based on deep learning. Environ Earth Sci. 81(9):250.
  • Wang Y, Fang ZC, Hong HY. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993.
  • Wasowski J, Bovenga F. 2014. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: current issues and future perspectives. Eng Geol. 174:103–138.
  • Xu C, Dai FC, Xu XW, Lee YH. 2012. GIS-based support vector machine modelling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology. 145–146:70–80.
  • Xu WY, Zhang Q, Zhang JC, Wang RB, Wang RK. 2013. Deformation and control engineering related to huge landslide on left bank of Xiluodu reservoir, south-west China. Eur J Environ Civil Eng. 17(sup1):S249–S268.
  • Xu YK, George DL, Kim J, Lu Z, Riley M, Griffin T, de la Fuente J. 2021. Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: an application to the Gold Basin landslide complex, northern Washington. Landslides. 18(3):1131–1141.
  • Yao JM, Lan HX, Li LP, Cao YM, Wu YM, Zhang YX, Zhou CD. 2022a. Characteristics of a rapid landsliding area along Jinsha River revealed by multi-temporal remote sensing and its risks to Sichuan-Tibet railway. Landslides. 19(3):703–718.
  • Yao JM, Yao X, Liu XH. 2022b. Landslide detection and mapping based on SBAS-InSAR and PS-InSAR: A case study in Gongjue county, Tibet, China. Remote Sens. 14(19):4728.
  • Yao X, Li LJ, Zhang YS, Zhou ZK, Liu XH. 2017. Types and characteristics of slow-moving slope geo-hazards recognized by TS-InSAR along Xianshuihe active fault in the eastern Tibet Plateau. Nat Hazards. 88(3):1727–1740.
  • Yao X, Tham LG, Dai FC. 2008. Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China. Geomorphology. 101(4):572–582.
  • Zhang SH, Wang YW, Wu G. 2022. Earthquake-induced landslide susceptibility assessment using a novel model based on gradient boosting machine learning and class balancing methods. Remote Sens. 14(23):5945.
  • Zhang W, Chen JP, Wang Q, An YK, Qian X, Xiang LJ, He LX. 2013. Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods. Nat Hazards. 66(2):1073–1100.
  • Zhang Y, Meng XM, Dijkstra TA, Jordan CJ, Chen G, Zeng RQ, Novellino A. 2020. Forecasting the magnitude of potential landslides based on InSAR techniques. Remote Sens Environ. 241:111738.
  • Zhao CY, Kang Y, Zhang Q, Lu Z, Li B. 2018. Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method. Remote Sens. 10(7):993.
  • Zhao Y, Wang R, Jiang YJ, Liu HJ, Wei ZL. 2019. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Eng. Geol. 259:105147.
  • Zhao Z, Chen JH, Xu KH, Xie HW, Gan XX, Xu H. 2021. A spatial case-based reasoning method for regional landslide risk assessment. Int J Appl Earth Obs Geoinf. 102:102381.
  • Zhao Z, Chen JH, Yao JM, Xu KH, Liao YY, Xie HW, Gan XX. 2023. An improved spatial case-based reasoning considering multiple spatial drivers of geographic events and its application in landslide susceptibility mapping. Catena. 223:106940.
  • Zheng XX, He GJ, Wang SS, Wang Y, Wang GZ, Yang ZY, Yu JC, Wang N. 2021. Comparison of machine learning methods for potential active landslide hazards identification with multi-source data. IJGI. 10(4):253.