Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms

References

• Abu El-Magd SA, Ali SA, Pham QB. 2021. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Sci Inform. 14(3):1227–1243. doi: 10.1007/s12145-021-00653-y.
   Web of Science ®Google Scholar
• Arabameri A, Chandra Pal S, Costache R, Saha A, Rezaie F, Seyed Danesh A, Pradhan B, Lee S, Hoang ND. 2021. Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomat Nat Haz Risk. 12(1):469–498. doi: 10.1080/19475705.2021.1880977.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Casagli N, Ermini L. 1999. Geomorphic analysis of landslide dams in the Northern Apennine. Chikei. 20:219–249.
   Google Scholar
• Cencetti C, Di Matteo L, Romeo S. 2017. Analysis of Costantino landslide dam evolution (Southern Italy) by means of satellite images, aerial photos, and climate data. Geosciences. 7(2):30. doi: 10.3390/geosciences7020030.
   Google Scholar
• Chai HJ, Liu HC, Zhang ZY. 1995. The catalog of Chinese landslide dam events. J Geol Hazards and Environ Preserv. 6(4):1–9. (in Chinese)
   Google Scholar
• Chen Z, Ma L, Yu S, Chen S, Zhou X, Sun P, Li X. 2015. Back analysis of the draining process of the Tangjiashan Barrier Lake. J Hydraul Eng. 141(4):05014011. doi: 10.1061/(ASCE)HY.1943-7900.0000965.
   Google Scholar
• Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA. 151:147–160. doi: 10.1016/j.catena.2016.11.032.
   Web of Science ®Google Scholar
• Costa JE, Schuster RL. 1988. The formation and failure of natural dams. Geol Soc Am Bull. 100(7):1054–1068. doi: 10.1130/0016-7606(1988)100<1054:TFAFON>2.3.CO;2.
   Web of Science ®Google Scholar
• Costa JE, Schuster RL. 1991. Documented historical landslide dams from around the world. U.S. Geological Survey Open-File Report 91-239. 486 pp. Vancouver, Washington. doi: 10.3133/ofr91239.
   Google Scholar
• Cui Y, Bao P, Xu C, Fu G, Jiao Q, Luo Y, Shen L, Xu X, Liu F, Lyu Y, et al. 2020. A big landslide on the Jinsha River, Tibet, China: geometric characteristics, causes, and future stability. Nat Hazards. 104(3):2051–2070. doi: 10.1007/s11069-020-04261-9.
   Web of Science ®Google Scholar
• Cui P, Zhu YY, Han YS, Chen XQ, Zhuang JQ. 2009. The 12 May Wenchuan earthquake-induced landslide lakes: distribution and preliminary risk evaluation. Landslides. 6(3):209–223. doi: 10.1007/s10346-009-0160-9.
   Web of Science ®Google Scholar
• Das I, Stein A, Kerle N, Dadhwal VK. 2012. Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology. 179:116–125. doi: 10.1016/j.geomorph.2012.08.004.
   Web of Science ®Google Scholar
• Domingos P. 2012. A few useful things to know about machine learning. Commun ACM. 55(10):78–87. doi: 10.1145/2347736.2347755.
   Web of Science ®Google Scholar
• Dong JJ, Li YS, Kuo CY, Sung RT, Li MH, Lee CT, Chen CC, Lee WR. 2011. The formation and breach of a short-lived landslide dam at Hsiaolin village, Taiwan—part I: post-event reconstruction of dam geometry. Eng Geol. 123(1–2):40–59. doi: 10.1016/j.enggeo.2011.04.001.
   Web of Science ®Google Scholar
• Dong JJ, Tung YH, Chen CC, Liao JJ, Pan YW. 2011. Logistic regression model for predicting the failure probability of a landslide dam. Eng Geol. 117(1–2):52–61. doi: 10.1016/j.enggeo.2010.10.004.
   Web of Science ®Google Scholar
• Ermini L, Casagli N. 2003. Prediction of the behaviour of landslide dams using a geomorphological dimensionless index. Earth Surf Process Landforms. 28(1):31–47. doi: 10.1002/esp.424.
   Web of Science ®Google Scholar
• Fan X, Dufresne A, Siva Subramanian S, Strom A, Hermanns R, Tacconi Stefanelli C, Hewitt K, Yunus AP, Dunning S, Capra L, et al. 2020. The formation and impact of landslide dams – State of the art. Earth-Sci Rev. 203:103116. doi: 10.1016/j.earscirev.2020.103116.
   Web of Science ®Google Scholar
• Fan X, Dufresne A, Whiteley J, Yunus AP, Subramanian SS, Okeke CA, Pánek T, Hermanns RL, Ming P, Strom A, et al. 2021. Recent technological and methodological advances for the investigation of landslide dams. Earth-Sci Rev. 218:103646. doi: 10.1016/j.earscirev.2021.103646.
   Web of Science ®Google Scholar
• Fang K, Tang H, Li C, Su X, An P, Sun S. 2023. Centrifuge modelling of landslides and landslide hazard mitigation: a review. Geosci Front. 14(1):101493. doi: 10.1016/j.gsf.2022.101493.
   Web of Science ®Google Scholar
• Fan X, Yang F, Siva Subramanian S, Xu Q, Feng Z, Mavrouli O, Peng M, Ouyang C, Jansen JD, Huang R. 2020. Prediction of a multi-hazard chain by an integrated numerical simulation approach: the Baige landslide, Jinsha River, China. Landslides. 17(1):147–164. doi: 10.1007/s10346-019-01313-5.
   Web of Science ®Google Scholar
• Havenith HB, Fan X, Torgoev A. 2015b. Hazard and risk related to earthquake-triggered landslides. In: Lollino G, Giordan D, Crosta GB, Corominas J, Azzam R, Wasowski J, Sciarra N, edtiors. Proceedings of the Engineering Geology for Society and Territory - Volume 2. Cham: Springer.
   Google Scholar
• Havenith HB, Strom A, Torgoev I, Torgoev A, Lamair L, Ischuk A, Abdrakhmatov K. 2015a. Tien Shan geohazards database: earthquakes and landslides. Geomorphology. 249:16–31. doi: 10.1016/j.geomorph.2015.01.037.
   Web of Science ®Google Scholar
• He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, et al. 2019. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ. 663:1–15. doi: 10.1016/j.scitotenv.2019.01.329.
   PubMed Web of Science ®Google Scholar
• Huang Y, Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. CATENA. 165:520–529. doi: 10.1016/j.catena.2018.03.003.
   Web of Science ®Google Scholar
• Hungr O, Leroueil S, Picarelli L. 2014. The Varnes classification of landslide types, an update. Landslides. 11(2):167–194. doi: 10.1007/s10346-013-0436-y.
   Web of Science ®Google Scholar
• Karir D, Ray A, Kumar Bharati A, Chaturvedi U, Rai R, Khandelwal M. 2022. Stability prediction of a natural and man-made slope using various machine learning algorithms. Transp Geotech. 34:100745. doi: 10.1016/j.trgeo.2022.100745.
   Web of Science ®Google Scholar
• Korup O. 2004. Geomorphometric characteristics of New Zealand landslide dams. Eng Geol. 73(1-2):13–35. doi: 10.1016/j.enggeo.2003.11.003.
   Web of Science ®Google Scholar
• Liao HM, Yang XG, Lu GD, Tao J, Zhou JW. 2022. A geotechnical index for landslide dam stability assessment. Geomat Nat Haz Risk. 13(1):854–876. doi: 10.1080/19475705.2022.2048906.
   Web of Science ®Google Scholar
• Li D, Nian T, Wu H, Wang F, Zheng L. 2020. A predictive model for the geometry of landslide dams in V-shaped valleys. Bull Eng Geol Environ. 79(9):4595–4608. doi: 10.1007/s10064-020-01828-5.
   Web of Science ®Google Scholar
• Li MH, Sung RT, Dong JJ, Lee CT, Chen CC. 2011. The formation and breaching of a short-lived landslide dam at Hsiaolin Village, Taiwan—Part II: simulation of debris flow with landslide dam breach. Eng Geol. 123(1–2):60–71. doi: 10.1016/j.enggeo.2011.05.002.
   Web of Science ®Google Scholar
• Liu N. 2014. Hongshiyan landslide dam danger removal and coordinated management. Front Eng. 1(3):308–317. doi: 10.15302/J-FEM-2014041.
   Google Scholar
• Lombardo L, Mai PM. 2018. Presenting logistic regression-based landslide susceptibility results. Eng Geol. 244:14–24. doi: 10.1016/j.enggeo.2018.07.019.
   Web of Science ®Google Scholar
• Nian TK, Wu H, Chen GQ, Zheng DF, Zhang YJ, Li DY. 2018. Research progress on stability evaluation method and disaster chain effect of landslide dam. Chinese J Rock Mech Engin. 37(8):1796–1812. (in Chinese)
   Google Scholar
• Nian TK, Wu H, Li DY, Zhao W, Takara K, Zheng DF. 2020. Experimental investigation on the formation process of landslide dams and a criterion of river blockage. Landslides. 17(11):2547–2562. doi: 10.1007/s10346-020-01494-4.
   Web of Science ®Google Scholar
• Nian TK, Wu H, Takara K, Li D-y, Zhang Y-j 2021. Numerical investigation on the evolution of landslide-induced river blocking using coupled DEM-CFD. Comput Geotech. 134:104101. doi: 10.1016/j.compgeo.2021.104101.
   Web of Science ®Google Scholar
• Nibigira L, Havenith HB, Archambeau P, Dewals B. 2018. Formation, breaching and flood consequences of a landslide dam near Bujumbura, Burundi. Nat Hazards Earth Syst Sci. 18(7):1867–1890. doi: 10.5194/nhess-18-1867-2018.
   Web of Science ®Google Scholar
• Oppikofer T, Hermanns RL, Jakobsen VU, Böhme M, Nicolet P, Penna I. 2020. Semi-empirical prediction of dam height and stability of dams formed by rock slope failures in Norway. Nat Hazards Earth Syst Sci. 20(11):3179–3196. doi: 10.5194/nhess-20-3179-2020.
   Web of Science ®Google Scholar
• Peng M, Zhang LM. 2012. Breaching parameters of landslide dams. Landslides. 9(1):13–31. doi: 10.1007/s10346-011-0271-y.
   Web of Science ®Google Scholar
• Pham K, Kim D, Park S, Choi H. 2021. Ensemble learning-based classification models for slope stability analysis. CATENA. 196:104886. doi: 10.1016/j.catena.2020.104886.
   Web of Science ®Google Scholar
• Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat. 2019. Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204. doi: 10.1038/s41586-019-0912-1.
   PubMed Web of Science ®Google Scholar
• Risley JC, Walder JS, Denlinger RP. 2006. Usoi dam wave overtopping and flood routing in the Bartang and Panj Rivers, Tajikistan. Nat Hazards. 38(3):375–390. doi: 10.1007/s11069-005-1923-9.
   Web of Science ®Google Scholar
• Safran EB, O'Connor JE, Ely LL, House PK, Grant G, Harrity K, Croall K, Jones E. 2015. Plugs or flood-makers? The unstable landslide dams of eastern Oregon. Geomorphology. 248:237–251. doi: 10.1016/j.geomorph.2015.06.040.
   Web of Science ®Google Scholar
• Saito H, Nakayama D, Matsuyama H. 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan. Geomorphology. 109(3–4):108–121. doi: 10.1016/j.geomorph.2009.02.026.
   Web of Science ®Google Scholar
• Schuster RL, Alford D. 2004. Usoi landslide dam and lake Sarez, Pamir Mountains, Tajikistan. Environ Eng Geosci. 10(2):151–168. doi: 10.2113/10.2.151.
   Web of Science ®Google Scholar
• Shan Y, Chen S, Zhong Q. 2020. Rapid prediction of landslide dam stability using the logistic regression method. Landslides. 17(12):2931–2956. doi: 10.1007/s10346-020-01414-6.
   Web of Science ®Google Scholar
• Shen D, Shi Z, Peng M, Zhang L, Jiang M. 2020. Longevity analysis of landslide dams. Landslides. 17(8):1797–1821. doi: 10.1007/s10346-020-01386-7.
   Web of Science ®Google Scholar
• Tacconi Stefanelli C, Catani F, Casagli N. 2015. Geomorphological investigations on landslide dams. Geoenviron Disasters. 2(1):21. doi: 10.1186/s40677-015-0030-9.
   Google Scholar
• Tacconi Stefanelli C, Segoni S, Casagli N, Catani F. 2016. Geomorphic indexing of landslide dams evolution. Eng Geol. 208:1–10. doi: 10.1016/j.enggeo.2016.04.024.
   Web of Science ®Google Scholar
• Tacconi Stefanelli C, Vilímek V, Emmer A, Catani FJL. 2018. Morphological analysis and features of the landslide dams in the Cordillera Blanca, Peru. Landslides. 15(3):507–521. doi: 10.1007/s10346-017-0888-6.
   Web of Science ®Google Scholar
• Tsangaratos P, Ilia I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides. 13(2):305–320. doi: 10.1007/s10346-015-0565-6.
   Web of Science ®Google Scholar
• Ullah I, Aslam B, Shah SHIA, Tariq A, Qin S, Majeed M, Havenith HB. 2022. An integrated approach of machine learning, remote sensing, and GIS data for the landslide susceptibility mapping. Land. 11(8):1265. doi: 10.3390/land11081265.
   Web of Science ®Google Scholar
• Wu H, Nian TK, Chen GQ, Zhao W, Li DY. 2020. Laboratory-scale investigation of the 3-D geometry of landslide dams in a U-shaped valley. Eng Geol. 265:105428. doi: 10.1016/j.enggeo.2019.105428.
   Web of Science ®Google Scholar
• Wu H, Nian TK, Shan Z, Li DY, Guo XS, Jiang XG. 2023. Rapid prediction models for 3D geometry of landslide dam considering the damming process. J Mt Sci. 20(4):928–942. doi: 10.1007/s11629-022-7906-z.
   Web of Science ®Google Scholar
• Wu H, Trigg MA, Murphy W, Fuentes R. 2022. A new global landslide dam database (RAGLAD) and analysis utilizing auxiliary global fluvial datasets. Landslides. 19(3):555–572. doi: 10.1007/s10346-021-01817-z.
   Web of Science ®Google Scholar
• Wu H, Zheng DF, Zhang YJ, Li DY, Nian TK. 2020. A photogrammetric method for laboratory-scale investigation on 3D landslide dam topography. Bull Eng Geol Environ. 79(9):4717–4732. doi: 10.1007/s10064-020-01870-3.
   Web of Science ®Google Scholar
• Zhang L, Peng M, Chang D, Xu Y. 2015. Dam failure mechanisms and risk assessment. Singapore: John Wiley & Sons. doi: 10.1002/9781118558522.
   Google Scholar
• Zheng H, Shi Z, Shen D, Peng M, Hanley KJ, Ma C, Zhang L. 2021. Recent advances in stability and failure mechanisms of landslide dams. Front Earth Sci. 9:201. doi: 10.3389/feart.2021.659935.
   Web of Science ®Google Scholar