Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China

References

• Abedini M, Tulabi S. 2018. Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran. Environ Earth Sci. 77(11):1–13. doi: 10.1007/s12665-018-7524-1.
   Web of Science ®Google Scholar
• Achu A, Aju C, Di Napoli M, Prakash P, Gopinath G, Shaji E, Chandra V. 2023a. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. Geosci Front. 14(6):101657. doi: 10.1016/j.gsf.2023.101657.
   Web of Science ®Google Scholar
• Achu AL, Thomas J, Aju CD, Remani PK, Gopinath G. 2023b. Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data. Earth Sci Inform. 16(1):1025–1039. doi: 10.1007/s12145-022-00910-8.
   Web of Science ®Google Scholar
• Aghdam IN, Pradhan B, Panahi M. 2017. Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environ Earth Sci. 76(6):237. doi: 10.1007/s12665-017-6558-0.
   Web of Science ®Google Scholar
• Agrawal N, Dixit J. 2022. Assessment of landslide susceptibility for Meghalaya (India) using bivariate (frequency ratio and Shannon entropy) and multi-criteria decision analysis (AHP and fuzzy-AHP) models. All Earth. 34(1):179–201. doi: 10.1080/27669645.2022.2101256.
   Web of Science ®Google Scholar
• Althuwaynee OF, Pradhan B, Park HJ, Lee JH. 2014. A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides. 11(6):1063–1078. doi: 10.1007/s10346-014-0466-0.
   Web of Science ®Google Scholar
• Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z. 2019. GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci. 16(3):595–618. doi: 10.1007/s11629-018-5168-y.
   Web of Science ®Google Scholar
• Arulbalaji P, Padmalal D, Sreelash K. 2019. GIS and AHP techniques based delineation of groundwater potential zones: a case study from southern Western Ghats, India. Sci Rep. 9(1):2082. doi: 10.1038/s41598-019-38567-x.
   PubMed Web of Science ®Google Scholar
• Bai S, Wang J, Zhang Z, Cheng C. 2012. Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena. 99:18–25. doi: 10.1016/j.catena.2012.06.012.
   Web of Science ®Google Scholar
• Belgiu M, Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens. 114:24–31. doi: 10.1016/j.isprsjprs.2016.01.011.
   Web of Science ®Google Scholar
• Botzen WJ, Aerts J, Van den Bergh J. 2013. Individual preferences for reducing flood risk to near zero through elevation. Mitig Adapt Strateg Glob Change. 18(2):229–244. doi: 10.1007/s11027-012-9359-5.
   Web of Science ®Google Scholar
• Bragagnolo L, Da Silva R, Grzybowski J. 2020. Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena. 184:104240. doi: 10.1016/j.catena.2019.104240.
   Web of Science ®Google Scholar
• Breiman L. 1996. Bagging predictors. Mach Learn. 24(2):123–140. doi: 10.1007/BF00058655.
   Web of Science ®Google Scholar
• Breiman L. 2001a. Random forests. Mach Learn. 45(1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Breiman L. 2001b. Using iterated bagging to debias regressions. Mach Learn. 45(3):261–277. doi: 10.1023/A:1017934522171.
   Web of Science ®Google Scholar
• Catani F, Lagomarsino D, Segoni S, Tofani V. 2013. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci. 13(11):2815–2831. doi: 10.5194/nhess-13-2815-2013.
   Web of Science ®Google Scholar
• Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. pp. 785–794. doi: 10.1145/2939672.2939785.
   Google Scholar
• Chen T, Zhu L, Niu RQ, Trinder CJ, Peng L, Lei T. 2020a. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J Mt Sci. 17(3):670–685. doi: 10.1007/s11629-019-5839-3.
   Web of Science ®Google Scholar
• Chen W, Chen Y, Tsangaratos P, Ilia I, Wang X. 2020b. Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens. 12(23):3854. doi: 10.3390/rs12233854.
   Google Scholar
• Chen W, Li Y. 2020c. GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena. 195:104777. doi: 10.1016/j.catena.2020.104777.
   Web of Science ®Google Scholar
• Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB. 2019. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena. 172:212–231. doi: 10.1016/j.catena.2018.08.025.
   Web of Science ®Google Scholar
• Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A-X, Pei X, Duan Z. 2018a. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ. 626:1121–1135. doi: 10.1016/j.scitotenv.2018.01.124.
   PubMed Web of Science ®Google Scholar
• Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu A-X. 2018b. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena. 164:135–149. doi: 10.1016/j.catena.2018.01.012.
   Web of Science ®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
• Chen W, Zhang S. 2021. GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. Catena. 203:105344. doi: 10.1016/j.catena.2021.105344.
   Web of Science ®Google Scholar
• Chen W, Zhang S, Li R, Shahabi H. 2018c. Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ. 644:1006–1018. doi: 10.1016/j.scitotenv.2018.06.389.
   PubMed Web of Science ®Google Scholar
• Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Das B, Roy P. 2020. Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci. 79(20):1–28. doi: 10.1007/s12665-020-09227-5.
   Web of Science ®Google Scholar
• Chung CJF, Fabbri AG. 2005. Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. Landslide hazard and risk. Hoboken (NJ): John Wiley & Sons, Ltd.; p. 139–174.
   Google Scholar
• Dai J, Xu Q. 2013. Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput. 13(1):211–221. doi: 10.1016/j.asoc.2012.07.029.
   Web of Science ®Google Scholar
• Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM. 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena. 135:122–148. doi: 10.1016/j.catena.2015.07.020.
   Web of Science ®Google Scholar
• Demir G. 2019. GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). Catena. 183:104211. doi: 10.1016/j.catena.2019.104211.
   Web of Science ®Google Scholar
• Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards. 65(1):135–165. doi: 10.1007/s11069-012-0347-6.
   Web of Science ®Google Scholar
• Di Napoli M, Carotenuto F, Cevasco A, Confuorto P, Di Martire D, Firpo M, Pepe G, Raso E, Calcaterra D. 2020. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides. 17(8):1897–1914. doi: 10.1007/s10346-020-01392-9.
   Web of Science ®Google Scholar
• Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT. 2019. 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. 662:332–346. doi: 10.1016/j.scitotenv.2019.01.221.
   PubMed Web of Science ®Google Scholar
• Fang Z, Wang Y, Peng L, Hong H. 2021. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geograph Inform Sci. 35(2):321–347. doi: 10.1080/13658816.2020.1808897.
   Web of Science ®Google Scholar
• Farrar DE, Glauber RR. 1967. Multicollinearity in regression analysis: the problem revisited. Rev Econ Statist. 49(1):92–107. doi: 10.2307/1937887.
   Web of Science ®Google Scholar
• Freund Y. 1995. Boosting a weak learning algorithm by majority. Inf Comput. 121(2):256–285. doi: 10.1006/inco.1995.1136.
   Web of Science ®Google Scholar
• Friedman JH. 2001. Greedy function approximation: a gradient boosting machine. Ann Statist. 29(5):1189–1232. http://www.jstor.org/stable/2699986
   Web of Science ®Google Scholar
• Gigović L, Drobnjak S, Pamučar D. 2019. The application of the hybrid GIS spatial multi-criteria decision analysis best–worst methodology for landslide susceptibility mapping. IJGI. 8(2):79. doi: 10.3390/ijgi8020079.
   Google Scholar
• Gislason PO, Benediktsson JA, Sveinsson JR. 2006. Random forests for land cover classification. Pattern Recog Lett. 27(4):294–300. doi: 10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Goetz J, Brenning A, Petschko H, Leopold P. 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci. 81:1–11. doi: 10.1016/j.cageo.2015.04.007.
   Web of Science ®Google Scholar
• Green IRA, Stephenson D. 1986. Criteria for comparison of single event models. Hydrol Sci J. 31(3):395–411.
   Google Scholar
• Guha-Sapir D, Below R, Hoyois P. 2019. EM-DAT. International disaster database. Brussels, Belgium: Université Catholique de Louvain.
   Google Scholar
• 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. doi: 10.1016/S0169-555X(99)00078-1.
   Web of Science ®Google Scholar
• Guzzetti F, Galli M, Reichenbach P, Ardizzone F, Cardinali M. 2006. Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Nat Hazards Earth Syst Sci. 6(1):115–131. doi: 10.5194/nhess-6-115-2006.
   Web of Science ®Google Scholar
• Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 72(1–4):272–299. doi: 10.1016/j.geomorph.2005.06.002.
   Web of Science ®Google Scholar
• Hanley JA, McNeil BJ. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1):29–36. doi: 10.1148/radiology.143.1.7063747.
   PubMed Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman JH, Friedman JH. 2009. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. Berlin, Germany: Springer.
   Google Scholar
• Hong H, Naghibi SA, Pourghasemi HR, Pradhan B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci. 9(2):1–26. doi: 10.1007/s12517-015-2094-y.
   Web of Science ®Google Scholar
• Hu Q, Zhou Y, Wang S, Wang F. 2020a. Machine learning and fractal theory models for landslide susceptibility mapping: case study from the Jinsha River Basin. Geomorphology. 351:106975. doi: 10.1016/j.geomorph.2019.106975.
   Web of Science ®Google Scholar
• Hu X, Mei H, Zhang H, Li Y, Li M. 2021. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China. Nat Hazards. 105(2):1663–1689. doi: 10.1007/s11069-020-04371-4.
   Web of Science ®Google Scholar
• Hu X, Zhang H, Mei H, Xiao D, Li Y, Li M. 2020b. Landslide susceptibility mapping using the stacking ensemble machine learning method in Lushui, Southwest China. Appl Sci. 10(11):4016. doi: 10.3390/app10114016.
   Google Scholar
• Huan Y, Song L, Khan U, Zhang B. 2023. Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China. Environ Earth Sci. 82(1):35. doi: 10.1007/s12665-022-10723-z.
   Web of Science ®Google Scholar
• Huang F, Cao Z, Guo J, Jiang SH, Li S, Guo Z. 2020a. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580. doi: 10.1016/j.catena.2020.104580.
   Web of Science ®Google Scholar
• Huang F, Chen J, Du Z, Yao C, Huang J, Jiang Q, Chang Z, Li S. 2020b. Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. IJGI. 9(6):377. doi: 10.3390/ijgi9060377.
   Google Scholar
• Hung LQ, Van NTH, Duc DM, Ha LTC, Van Son P, Khanh NH, Binh LT. 2016. Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam). Landslides. 13(5):1285–1301. doi: 10.1007/s10346-015-0657-3.
   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
• Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J. 2019. Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ. 653:801–814. doi: 10.1016/j.scitotenv.2018.10.431.
   PubMed Web of Science ®Google Scholar
• Kadavi PR, Lee CW, Lee S. 2019. Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environ Earth Sci. 78(4):1–17. doi: 10.1007/s12665-019-8119-1.
   Web of Science ®Google Scholar
• Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S. 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Hazard Risk. 9(1):49–69. doi: 10.1080/19475705.2017.1407368.
   Web of Science ®Google Scholar
• Khan A, Khan A, Khan MM, Farid K, Alam MM, Su’ud MBM. 2022. Cardiovascular and diabetes diseases classification using ensemble stacking classifiers with SVM as a meta classifier. Diagnostics. 12(11):2595. doi: 10.3390/diagnostics12112595.
   Web of Science ®Google Scholar
• Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT. 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ. 627:744–755. doi: 10.1016/j.scitotenv.2018.01.266.
   PubMed Web of Science ®Google Scholar
• Lai JS, Tsai F. (2019). Improving GIS-based landslide susceptibility assessments with multi-temporal remote sensing and machine learning. Sensors, 19(17), 3717. doi: 10.3390/s19173717.
   PubMed Web of Science ®Google Scholar
• Lin Q, Wang Y. 2018. Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides. 15(12):2357–2372. doi: 10.1007/s10346-018-1037-6.
   Web of Science ®Google Scholar
• Lin W, Yin K, Wang N, Xu Y, Guo Z, Li Y. 2021. Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China. Nat Hazards. 106(1):679–700. doi: 10.1007/s11069-020-04483-x.
   Web of Science ®Google Scholar
• Liu LL, Yang C, Huang FM, Wang XM. 2021a. Landslide susceptibility mapping by attentional factorization machines considering feature interactions. Geomatics Nat Hazards Risk. 12(1):1837–1861. doi: 10.1080/19475705.2021.1950217.
   Web of Science ®Google Scholar
• Liu LL, Yang C, Wang XM. 2021b. Landslide susceptibility assessment using feature selection-based machine learning models. Geomech Eng. 25(1):1–16.
   Web of Science ®Google Scholar
• Liu LL, Zhang YL, Xiao T, Yang C. 2022. A frequency ratio–based sampling strategy for landslide susceptibility assessment. Bull Eng Geol Environ. 81(9):360. doi: 10.1007/s10064-022-02836-3.
   Web of Science ®Google Scholar
• Mandal SP, Chakrabarty A, Maity P. 2018. Comparative evaluation of information value and frequency ratio in landslide susceptibility analysis along national highways of Sikkim Himalaya. Spat Inf Res. 26(2):127–141. doi: 10.1007/s41324-017-0160-0.
   Web of Science ®Google Scholar
• Masetic Z, Subasi A. 2016. Congestive heart failure detection using random forest classifier. Comput Methods Programs Biomed. 130:54–64. doi: 10.1016/j.cmpb.2016.03.020.
   PubMed Web of Science ®Google Scholar
• 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. doi: 10.1016/j.earscirev.2020.103225.
   Web of Science ®Google Scholar
• Mondal S, Mandal S. 2020. Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India. Geocarto Int. 35(8):818–856. doi: 10.1080/10106049.2018.1544288.
   Web of Science ®Google Scholar
• Nefeslioglu HA, Duman TY, Durmaz S. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology. 94(3–4):401–418. doi: 10.1016/j.geomorph.2006.10.036.
   Web of Science ®Google Scholar
• Nguyen VT, Tran TH, Ha NA, Ngo VL, Nadhir A-A, Tran VP, Duy Nguyen H, M. A M, Amini A, Prakash I, et al. 2019. GIS based novel hybrid computational intelligence models for mapping landslide susceptibility: a case study at da lat city, Vietnam. Sustainability. 11(24):7118. doi: 10.3390/su11247118.
   Web of Science ®Google Scholar
• Nhu VH, Janizadeh S, Avand M, Chen W, Farzin M, Omidvar E, Shirzadi A, Shahabi H, J. Clague J, Jaafari A, et al. 2020. GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models. Appl Sci. 10(6):2039. doi: 10.3390/app10062039.
   Google Scholar
• Omajali JB, Gomez-Bolivar J, Mikheenko IP, Sharma S, Kayode B, Al-Duri B, Banerjee D, Walker M, Merroun ML, Macaskie LE. 2019. Novel catalytically active Pd/Ru bimetallic nanoparticles synthesized by Bacillus benzeovorans. Sci Rep. 9(1):4715. doi: 10.1038/s41598-019-40312-3.
   PubMedGoogle Scholar
• Pal SC, Chakrabortty R, Malik S, Das B. 2018. Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: a case study of Sali watershed, West Bengal. Model Earth Syst Environ. 4(2):853–865. doi: 10.1007/s40808-018-0445-x.
   Web of Science ®Google Scholar
• Pal SC, Chowdhuri I. 2019. GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Appl Sci. 1(5):1–25. doi: 10.1007/s42452-019-0422-7.
   Web of Science ®Google Scholar
• Pei X, Cui S, Liang Y, Wang H. 2023. The multiple earthquakes induced progressive failure of the Xinmo landslide, China: based on shaking table tests. Environ Earth Sci. 82(20):465. doi: 10.1007/s12665-023-11148-y.
   Web of Science ®Google Scholar
• Pereira S, Santos P, Zêzere J, Tavares A, Garcia R, Oliveira S. 2020. A landslide risk index for municipal land use planning in Portugal. Sci Total Environ. 735:139463. doi: 10.1016/j.scitotenv.2020.139463.
   PubMed Web of Science ®Google Scholar
• Petley D. 2012. Global patterns of loss of life from landslides. Geology. 40(10):927–930. doi: 10.1130/G33217.1.
   Web of Science ®Google Scholar
• Pham BT, Bui DT, Prakash I, Dholakia M. 2017. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena. 149:52–63. doi: 10.1016/j.catena.2016.09.007.
   Web of Science ®Google Scholar
• Pham BT, Nguyen-Thoi T, Qi C, Van Phong T, Dou J, Ho LS, Van Le H, Prakash I. 2020a. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena. 195:104805. doi: 10.1016/j.catena.2020.104805.
   Web of Science ®Google Scholar
• Pham BT, Prakash I, Chen W, Ly HB, Ho LS, Omidvar E, Tran VP, Bui DT. (2019). A novel intelligence approach of a sequential minimal optimization-based support vector machine for landslide susceptibility mapping. Sustainability. 11(22), 6323. doi: 10.3390/su11226323.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Indra P, Dholakia M. 2015. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol. 4(11):338–344.
   Google Scholar
• Pham BT, Phong TV, Nguyen-Thoi T, Trinh PT, Tran QC, Ho LS, Singh SK, Duyen TTT, Nguyen LT, Le HQ, et al. 2020b. GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res. 66(6):1303–1320. doi: 10.1016/j.asr.2020.05.016.
   Web of Science ®Google Scholar
• Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci. 122(2):349–369. doi: 10.1007/s12040-013-0282-2.
   Web of Science ®Google Scholar
• Ravì D, Bober M, Farinella GM, Guarnera M, Battiato S. 2016. Semantic segmentation of images exploiting DCT based features and random forest. Pattern Recogn. 52:260–273. doi: 10.1016/j.patcog.2015.10.021.
   Web of Science ®Google Scholar
• Sadighi M, Motamedvaziri B, Ahmadi H, Moeini A. 2020. Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA. Environ Earth Sci. 79(24):1–14. doi: 10.1007/s12665-020-09294-8.
   Web of Science ®Google Scholar
• Sahin EK. 2020. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci. 2(7):1308. doi: 10.1007/s42452-020-3060-1.
   Web of Science ®Google Scholar
• Su Y, Zhang Y, Ji D, Wang Y, Wu H. 2013. Ensemble learning for sentiment classification. In Chinese Lexical Semantics: 13th Workshop, CLSW 2012; July 6–8, 2012; Wuhan, China. Revised Selected Papers 13. pp. 84–93. Springer Berlin Heidelberg.
   Google Scholar
• Sun D, Xu J, Wen H, Wang D. 2021. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol. 281:105972. doi: 10.1016/j.enggeo.2020.105972.
   Web of Science ®Google Scholar
• Sur U, Singh P, Meena SR. 2020. Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomatics Nat Hazards Risk. 11(1):2176–2209. doi: 10.1080/19475705.2020.1836038.
   Web of Science ®Google Scholar
• Thai Pham B, Tien Bui D, Prakash I. 2018. Landslide susceptibility modelling using different advanced decision trees methods. Civil Eng Environ Syst. 35(1–4):139–157. doi: 10.1080/10286608.2019.1568418.
   Web of Science ®Google Scholar
• Thien BB, Phuong VT. 2023. Using Landsat satellite imagery for assessment and monitoring of long-term forest cover changes in Dak Nong province, Vietnam. Geographic Pannonic. 27(1):69–82. doi: 10.5937/gp27-41813.
   Web of Science ®Google Scholar
• Tien Bui D, Shahabi H, Omidvar E, Shirzadi A, Geertsema M, Clague J, Khosravi K, Pradhan B, Pham B, Chapi K, et al. 2019. Shallow landslide prediction using a novel hybrid functional machine learning algorithm. Remote Sens. 11(8):931. doi: 10.3390/rs11080931.
   Google Scholar
• Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS. 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena. 118:124–135. doi: 10.1016/j.catena.2014.02.005.
   Web of Science ®Google Scholar
• Vapnik VN. 1995. The nature of statistical learning theory. Vol. 841. New York (NY): Springer-Verlag New York Inc.; p. 842.
   Google Scholar
• Wang Y, Feng L, Li S, Ren F, Du Q. 2020. A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China. Catena. 188:104425. doi: 10.1016/j.catena.2019.104425.
   Web of Science ®Google Scholar
• Webb GI. 2000. Multiboosting: a technique for combining boosting and wagging. Mach Learn. 40(2):159–196. doi: 10.1023/A:1007659514849.
   Web of Science ®Google Scholar
• Weiss A. 2001, July. Topographic position and landforms analysis. In Poster presentation at: ESRI User Conference; San Diego, CA. (Vol. 200).
   Google Scholar
• Wu X, Wang J. 2023. Application of bagging, boosting and stacking ensemble and easyensemble methods for landslide susceptibility mapping in the three gorges reservoir area of China. Int J Environ Res Public Health. 20(6):4977. doi: 10.3390/ijerph20064977.
   PubMedGoogle Scholar
• Yi Y, Zhang Z, Zhang W, Jia H, Zhang J. 2020. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. Catena. 195:104851. doi: 10.1016/j.catena.2020.104851.
   Web of Science ®Google Scholar
• Yuan X, Liu C, Nie R, Yang Z, Li W, Dai X, Cheng J, Zhang J, Ma L, Fu X, et al. 2022. A comparative analysis of certainty factor-based machine learning methods for collapse and landslide susceptibility mapping in Wenchuan County, China. Remote Sens. 14(14):3259. doi: 10.3390/rs14143259.
   Google Scholar
• Zhang W, He Y, Wang L, Liu S, Meng X. 2023. Landslide susceptibility mapping using random forest and extreme gradient boosting: a case study of Fengjie, Chongqing. Geol J. 58(6):2372–2387. doi: 10.1002/gj.4683.
   Web of Science ®Google Scholar
• Zhang Y, Ge T, Tian W, Liou YA. 2019. Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sens. 11(23):2801. doi: 10.3390/rs11232801.
   Google Scholar
• Zhao B, Ge Y, Chen H. 2021. Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models. Environ Earth Sci. 80(12):441. doi: 10.1007/s12665-021-09737-w.
   Web of Science ®Google Scholar
• Zhao X, Chen W. 2020. Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens. 12(14):2180. doi: 10.3390/rs12142180.
   Google Scholar
• Zhou K, Zhang J, Ren Y, Huang Z, Zhao L. 2020. A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification. Geophysics. 85(4):WA147–WA158. doi: 10.1190/geo2019-0429.1.
   Web of Science ®Google Scholar
• Zhou X, Wen H, Zhang Y, Xu J, Zhang W. 2021. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci Front. 12(5):101211. doi: 10.1016/j.gsf.2021.101211.
   Web of Science ®Google Scholar