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Bagging-based machine learning algorithms for landslide susceptibility modeling

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Abstract

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874; however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

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References

  • Abuzied SM, Alrefaee HA (2019) Spatial prediction of landslide-susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis. Bull Eng Geol Env 78:2169–2195

    Article  Google Scholar 

  • Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111

    Article  Google Scholar 

  • Aertsen W, Kint V, van Orshoven J, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221:1119–1130

    Article  Google Scholar 

  • Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: in an artificial reservoir area at Northern Turkey. Arab J Geosci 9:165

    Article  Google Scholar 

  • Amiri M, Pourghasemi HR, Ghanbarian GA, Afzali SF (2019) Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma 340:55–69

    Article  Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K, Lee S, Sohrabi M (2019a) An ensemble model for landslide susceptibility mapping in a forested area. Geocarto Int 35:1–26

    Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K, Sohrabi M, Kalantari Z (2019b) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mount Sci 16:595–618

    Article  Google Scholar 

  • Armaş I (2012) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians. Romania Nat Haz 60:937–950

    Article  Google Scholar 

  • Binaghi E, Luzi L, Madella P, Pergalani F, Rampini A (1998) slope instability zonation: a comparison between certainty factor and fuzzy dempster-shafer approaches. Nat Hazards 17:77–97

    Article  Google Scholar 

  • Bourenane H, Bouhadad Y, Guettouche MS, Braham M (2015) GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Env 74:337–355

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Article  Google Scholar 

  • Chang K-T, Merghadi A, Yunus AP, Pham BT, Dou J (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9:12296

    Article  Google Scholar 

  • Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833

    Article  Google Scholar 

  • Chen W, Shahabi H, Zhang S, Khosravi K, Shirzadi A, Chapi K, Pham TB, Zhang T, Zhang L, Chai H, Ma J, Chen Y, Wang X, Li R, Ahmad BB (2018a) Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression. Appl Sci 8:2540–2562

    Article  Google Scholar 

  • Chen W, Zhang S, Li R, Shahabi H (2018b) 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

    Article  Google Scholar 

  • Chen W, Lei X, Chakrabortty R, Chandra Pal S, Sahana M, Janizadeh S (2021a) Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility. J Environ Manag 284:112015

    Article  Google Scholar 

  • Chen Y, Chen W, Chandra Pal S, Saha A, Chowdhuri I, Adeli B, Janizadeh S, Dineva AA, Wang X, Mosavi A (2021b) Evaluation efficiency of hybrid deep learning algorithms with neural network, decision tree and boosting methods for predicting groundwater potential. Geocarto Int 36:1–20

    Article  Google Scholar 

  • Chen Y, Che W, Janizadeh S, Bhunia GS, Bera A, Pham QB, Linh NTT, Balogun A-L, Wang X (2021c) Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region. Geocarto Int 36:1–27

    Article  Google Scholar 

  • Cheng M-Y, Hoang N-D (2015) Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards 78:1961–1978

    Article  Google Scholar 

  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

    Article  Google Scholar 

  • Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096

    Article  Google Scholar 

  • Clapuyt F, Vanacker V, Christl M, Van Oost K, Schlunegger F (2019) Spatio-temporal dynamics of sediment transfer systems in landslide-prone Alpine catchments. Solid Earth 10:1489–1503

    Article  Google Scholar 

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32:619–639

    Google Scholar 

  • Dou J, Yunus AP, Xu Y, Zhu Z, Chen C-W, Sahana M, Khosravi K, Yang Y, Pham BT (2019a) Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China. Nat Hazards 97:579–609

    Article  Google Scholar 

  • Dou J, Yunus PA, Tien Bui D, Sahana M, Chen C-W, Zhu Z, Wang W, Thai Pham B (2019b) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638–659

    Article  Google Scholar 

  • Du G, Zhang Y, Yang Z, Guo C, Yao X, Sun D (2019) Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods. Bull Eng Geol Env 78:4201–4215

    Article  Google Scholar 

  • Gama J (2001) Functional trees for classification, Proceedings 2001 IEEE International Conference on Data Mining, pp 147–154

  • Gama J (2004) Functional trees. Mach Learn 55:219–250

    Article  Google Scholar 

  • Haque U, da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, Yamamoto T, Keellings D, Wu J-H, Glass GE (2019) The human cost of global warming: deadly landslides and their triggers (1995–2014). Sci Total Environ 682:673–684

    Article  Google Scholar 

  • He Q, Xu Z, Li S, Li R, Zhang S, Wang N, Pham TB, Chen W (2019) Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy 21:106–130

    Article  Google Scholar 

  • Hong H, Pradhan B, Bui DT, Xu C, Youssef AM, Chen W (2017) Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomat Nat Haz Risk 8:544–569

    Article  Google Scholar 

  • Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu AX, Chen W, Ahmad BB (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Article  Google Scholar 

  • Hong H, Liu J, Zhu AX (2019) Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China). Environ Earth Sci 78:488

    Article  Google Scholar 

  • Hosseinalizadeh M, Kariminejad N, Chen W, Pourghasemi HR, Alinejad M, Behbahani AM, Tiefenbacher JP (2019) Gully headcut susceptibility modeling using functional trees, na ve Bayes tree, and random forest models. Geoderma 342:1–11

    Article  Google Scholar 

  • Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529

    Article  Google Scholar 

  • Huang H, Song K, Yi W, Long J, Liu Q, Zhang G (2019) Use of multi-source remote sensing images to describe the sudden Shanshucao landslide in the Three Gorges Reservoir, China. Bull Eng Geol Env 78:2591–2610

    Article  Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926

    Article  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

    Article  Google Scholar 

  • Kadavi P, Lee C-W, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10:1252–1274

    Article  Google Scholar 

  • Kadavi PR, Lee C-W, Lee S (2019) Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environ Earth Sci 78:116

    Article  Google Scholar 

  • Kasai M, Yamada T (2019) Topographic effects on frequency-size distribution of landslides triggered by the Hokkaido Eastern Iburi Earthquake in 2018. Earth, Planets Space 71:89–101

    Article  Google Scholar 

  • Kose DD, Turk T (2019) GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods. Phys Geogr 40:481–501

    Article  Google Scholar 

  • Kumar N, Reddy GPO, Chatterji S (2013) Evaluation of best first decision tree on categorical soil survey data for land capability classification. Int J Comput Appl 72:5–8

    Google Scholar 

  • Kumar A, Sharma RK, Bansal VK (2018) Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh, India. Environ Earth Sci 77:719

    Article  Google Scholar 

  • Kutlug Sahin E, Colkesen I (2019) Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto Int 34:1–23

    Google Scholar 

  • Lay SU, Pradhan B, Yusoff BZ, Abdallah FA, Aryal J, Park H-J (2019) Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR Data. Sensors 19:3451–3472

    Article  Google Scholar 

  • Lee C-F, Huang W-K, Chang Y-L, Chi S-Y, Liao W-C (2018a) Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan. Geomorphology 300:113–127

    Article  Google Scholar 

  • Lee J-H, Sameen MI, Pradhan B, Park H-J (2018b) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303:284–298

    Article  Google Scholar 

  • Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A (2020a) GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens 12:2478

    Article  Google Scholar 

  • Lei X, Chen W, Pham BT (2020b) Performance evaluation of GIS-based Artificial Intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int J Geo Inf 9:443

    Article  Google Scholar 

  • Lei X, Chen W, Panahi M, Falah F, Rahmati O, Uuemaa E, Kalantari Z, Sofia Santos Ferreira C, Rezaie F, Tiefenbacher JP, Lee S, Bian H (2021) Urban flood modeling using deep-learning approaches in Seoul, South Korea. J Hydrol 601:126684–126701

    Article  Google Scholar 

  • Li R, Wang N (2019) Landslide susceptibility mapping for the Muchuan County (China): a comparison between bivariate statistical models (WoE, EBF, and IoE) and their ensembles with logistic regression. Symmetry 11:762–784

    Article  Google Scholar 

  • Li Y, Chen W, Rezaie F, Rahmati O, Davoudi Moghaddam D, Tiefenbacher J, Panahi M, Lee M-J, Kulakowski D, Tien Bui D, Lee S (2021) Debris flows modeling using anthropogenic and geo-environmental factors: developing hybridized deep-learning algorithms. Geocarto Int 36:1–23

    Article  Google Scholar 

  • Loh W-Y (2011) Classification and regression trees. Wiley Interdiscip Rev Data MinKnowl Disc 1:14–23

    Article  Google Scholar 

  • Lombardo L, Mai PM (2018) Presenting logistic regression-based landslide susceptibility results. Eng Geol 244:14–24

    Article  Google Scholar 

  • Ma P, Peng J, Wang Q, Zhuang J, Zhang F (2019) The mechanisms of a loess landslide triggered by diversion-based irrigation: a case study of the South Jingyang Platform, China. Bull Eng Geol Environ 78:4954–4693

    Article  Google Scholar 

  • Mandal B, Mandal S (2018a) Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Adv Space Res 62:3114–3132

    Article  Google Scholar 

  • Mandal S, Mandal K (2018b) Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya, India. Spat Inf Res 26:59–75

    Article  Google Scholar 

  • Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens 39:2784–2817

    Article  Google Scholar 

  • McKenney DW, Pedlar JH (2003) Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. For Ecol Manage 175:497–507

    Article  Google Scholar 

  • Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng Comput 35:967–984

    Article  Google Scholar 

  • Mohammadi S, Taiebat H (2016) Finite element simulation of an excavation-triggered landslide using large deformation theory. Eng Geol 205:62–72

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Amiri M (2019) Land subsidence susceptibility assessment using random forest machine learning algorithm. Environ Earth Sci 78:1–12

    Article  Google Scholar 

  • Mokhtari M, Abedian S (2019) Spatial prediction of landslide susceptibility in Taleghan basin, Iran. Stoch Environ Res Risk Assess 33:1297–1325

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Naghibi AS, Vafakhah M, Hashemi H, Pradhan B, Alavi JS (2018) Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (Case study: Mashhad Plain, Iran). Water 10:1405–1605

    Article  Google Scholar 

  • Nguyen TP, Tuyen TT, Shirzadi A, Pham TB, Shahabi H, Omidvar E, Amini A, Entezami H, Prakash I, Phong VT, Vu BT, Thanh T, Saro L, Bui TD (2019a) Development of a novel hybrid intelligence approach for landslide spatial prediction. Appl Sci 9:2824–2850

    Article  Google Scholar 

  • Nguyen VV, Pham TB, Vu TB, Prakash I, Jha S, Shahabi H, Shirzadi A, Ba ND, Kumar R, Chatterjee MJ, Tien Bui D (2019b) Hybrid machine learning approaches for landslide susceptibility modeling. Forests 10:1–27

    Article  Google Scholar 

  • Palmisano F, Vitone C, Cotecchia F (2016) Methodology for landslide damage assessment. Procedia Engineering 161:511–515

    Article  Google Scholar 

  • Paranunzio R, Chiarle M, Laio F, Nigrelli G, Turconi L, Luino F (2019) New insights in the relation between climate and slope failures at high-elevation sites. Theoret Appl Climatol 137:1765–1784

    Article  Google Scholar 

  • Park HJ, Jang JY, Lee JH (2019) Assessment of rainfall-induced landslide susceptibility at the regional scale using a physically based model and fuzzy-based Monte Carlo simulation. Landslides 16:695–713

    Article  Google Scholar 

  • Peng J, Tong X, Wang S, Ma P (2018) Three-dimensional geological structures and sliding factors and modes of loess landslides. Environ Earth Sci 77:675

    Article  Google Scholar 

  • Pham BT, Prakash I (2019) A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment. Bull Eng Geol Env 78:1911–1925

    Article  Google Scholar 

  • Pham BT, Bui DT, Dholakia MB, Prakash I, Pham HV, Mehmood K, Le HQ (2017a) A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomat Nat Haz Risk 8:649–671

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia M (2017b) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoret Appl Climatol 128:255–273

    Article  Google Scholar 

  • Pham BT, Tien Bui D, Prakash I (2017c) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and j48 decision trees methods: a comparative study. Geotech Geol Eng 35:2597–2611

    Article  Google Scholar 

  • Pham BT, Nguyen MD, Bui K-TT, Prakash I, Chapi K, Bui DT (2019) A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. CATENA 173:302–311

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Pourghasemi HR, Rossi M (2017) Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoret Appl Climatol 130:609–633

    Article  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013a) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013b) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed. Iran Arab J Geosci 6:2351–2365

    Article  Google Scholar 

  • Pourghasemi HR, Teimoori Yansari Z, Panagos P, Pradhan B (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11:193

    Article  Google Scholar 

  • Pradhan B, Seeni MI, Kalantar B (2017) Performance evaluation and sensitivity analysis of expert-based, statistical, machine learning, and hybrid models for producing landslide susceptibility maps, Laser scanning applications in landslide assessment. Springer, pp 193–232

  • Rahmati O, Falah F, Naghibi SA, Biggs T, Soltani M, Deo RC, Cerdà A, Mohammadi F, Tien Bui D (2019) Land subsidence modelling using tree-based machine learning algorithms. Sci Total Environ 672:239–252

    Article  Google Scholar 

  • Regmi NR, McDonald EV, Rasmussen C (2019) Hillslope response under variable microclimate. Earth Surf Proc Land 44:2615–2627

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sarkar S, Raj R, Vinay S, Maiti J, Pratihar DK (2019) An optimization-based decision tree approach for predicting slip-trip-fall accidents at work. Saf Sci 118:57–69

    Article  Google Scholar 

  • Schifman LA, Prues A, Gilkey K, Shuster WD (2018) Realizing the opportunities of black carbon in urban soils: Implications for water quality management with green infrastructure. Sci Total Environ 644:1027–1035

    Article  Google Scholar 

  • Schmidt AH, Denn AR, Hidy AJ, Bierman PR, Tang Y (2019) Human and natural controls on erosion in the Lower Jinsha River, China. J Asian Earth Sci 170:351–359

    Article  Google Scholar 

  • Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Ahmad BB, Bui DT (2019) Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution. CATENA 178:172–188

    Article  Google Scholar 

  • Shou K-J, Lin JF (2020) Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale. Eng Geol 265:105434

    Article  Google Scholar 

  • Singh K, Kumar V (2017) Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arab J Geosci 10:539

    Article  Google Scholar 

  • Tien Bui D, Ho T-C, Pradhan B, Pham B-T, Nhu V-H, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75:1101

    Article  Google Scholar 

  • Tien Bui D, Shahabi H, Omidvar E, Shirzadi A, Geertsema M, Clague JJ, Khosravi K, Pradhan B, Pham TB, Chapi K, Barati Z, Bin Ahmad B, Gróf Rahmani H, Lee G (2019) Shallow landslide prediction using a novel hybrid functional machine learning algorithm. Remote Sens 11:931–953

    Article  Google Scholar 

  • Truong X, Mitamura M, Kono Y, Raghavan V, Yonezawa G, Truong X, Do T, Tien Bui D, Lee S (2018) Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Appl Sci 8:1046–1067

    Article  Google Scholar 

  • Wang J, Zhang D, Wang N, Gu T (2019a) Mechanisms of wetting-induced loess slope failures. Landslides 16:937–953

    Article  Google Scholar 

  • Wang K, Zhang S, DelgadoTéllez R, Wei F (2019b) A new slope unit extraction method for regional landslide analysis based on morphological image analysis. Bull Eng Geol Env 78:4139–4151

    Article  Google Scholar 

  • Wang X, Huang Z, Hong MM, Zhao YF, Ou YS, Zhang J (2019c) A comparison of the effects of natural vegetation regrowth with a plantation scheme on soil structure in a geological hazard-prone region. Eur J Soil Sci 70:674–685

    Article  Google Scholar 

  • Wang Y, Wu X, Chen Z, Ren F, Feng L, Du Q (2019d) Optimizing the predictive ability of machine learning methods for landslide susceptibility mapping using SMOTE for Lishui City in Zhejiang Province, China. Int J Environ Res Pub Health 16:368–400

    Article  Google Scholar 

  • Watakabe T, Matsushi Y (2019) Lithological controls on hydrological processes that trigger shallow landslides: observations from granite and hornfels hillslopes in Hiroshima, Japan. CATENA 180:55–68

    Article  Google Scholar 

  • Wu Y, Li W, Wang Q, Liu Q, Yang D, Xing M, Pei Y, Yan S (2016) Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arab J Geosci 9:84

    Article  Google Scholar 

  • Wu Z, Wu Y, Yang Y, Chen F, Zhang N, Ke Y, Li W (2017) A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models. Arab J Geosci 10:187

    Article  Google Scholar 

  • Xiao L, Zhang Y, Peng G (2018) Landslide susceptibility assessment using integrated deep learning algorithm along the China-Nepal Highway. Sensors 18:4436–4449

    Article  Google Scholar 

  • Yang Z, Cai H, Shao W, Huang D, Uchimura T, Lei X, Tian H, Qiao J (2019) Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis. Bull Eng Geol Env 78:2139–2150

    Article  Google Scholar 

  • Yue X-L, Wu S-H, Huang M, Gao J-B, Yin Y-H, Feng A-Q, Gu X-P (2018) Spatial association between landslides and environmental factors over Guizhou Karst Plateau, China. J Mt Sci 15:1987–2000

    Article  Google Scholar 

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Funding

This study is financially supported by Fundamental Research Funds for the Central Universities (300102351502), Shaanxi Province Youth Talent Support Program Project (2021-1-2), Shaanxi Land Construction-Xi'an Jiaotong University Land Engineering and Human Settlement Environment Tecnology Innovation Center Open Fund Project (2021WHZ0089) and Inner scientific research project of Shaanxi Land Engineering Construction Group (SXDJ2021-10, SXDJ2021-30, SXDJ2020-22). The author wish to express their sincere thanks to Chaohong Peng (Sichuang Institute of Geological Engineering Investigation Group Co.Ltd) for useful information provided.

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Authors and Affiliations

  1. Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Natural Resources, Xi’an, Shaanxi, China

    Tingyu Zhang & Huanyuan Wang

  2. Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi’an, Shaanxi, China

    Tingyu Zhang & Huanyuan Wang

  3. Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co., Ltd., Xi’an, Shaanxi, China

    Quan Fu & Fangfang Liu

  4. Hanzhong Branch of Shaanxi Land Engineering Construction Group Co., Ltd., Han Zhong, Shaanxi, China

    Hao Wang

  5. School of Land Engineering, Chang’an University, Xi’an, Shaanxi, China

    Ling Han

Authors
  1. Tingyu Zhang
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  2. Quan Fu
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  3. Hao Wang
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  4. Fangfang Liu
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  5. Huanyuan Wang
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Contributions

Tingyu Zhang: Conceptualization, Methodology, Writing-Review & Editing, Funding Acquisition Quan Fu: Resources, Software, Validation Fangfang Liu: Formal Analysis, Data Curation Hao Wang: Visualization Huanyuan Wang: Writing-Original Draft Preparation Ling Han: Supervision.

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Correspondence to Huanyuan Wang.

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Zhang, T., Fu, Q., Wang, H. et al. Bagging-based machine learning algorithms for landslide susceptibility modeling. Nat Hazards 110, 823–846 (2022). https://doi.org/10.1007/s11069-021-04986-1

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