1,284
Views
9
CrossRef citations to date
0
Altmetric
Research Article

Essential insights into decision mechanism of landslide susceptibility mapping based on different machine learning models

, , ORCID Icon, ORCID Icon, , , & show all
Pages 1-29 | Received 27 Jun 2022, Accepted 07 Nov 2022, Published online: 17 Nov 2022

References

  • Akinci H, Zeybek M. 2021. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Nat Hazards. 108(2):1515–1543.
  • Ali SA, Parvin F, Vojtekova J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigovic L, Ahmad A, Ghorbani MA. 2021. GIS-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front. 12(2):857–876.
  • Althuwaynee OF, Pradhan B, Park H-J, Lee JH. 2014. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 114:21–36.
  • Arora MK, Das Gupta AS, Gupta RP. 2004. An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens. 25(3):559–572.
  • Aslam B, Zafar A, Khalil U. 2022. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan. Environ Dev Sustain. 1–28.
  • Bakillah M, Liang S, Mobasheri A, Arsanjani JJ, Zipf A. 2014. Fine-resolution population mapping using OpenStreetMap points-of-interest. Int J Geogr Inf Sci. 28(9):1940–1963.
  • Basu T, Pal S. 2018. Identification of landslide susceptibility zones in Gish River basin, West Bengal, India. Georisk: Assess Manage Risk Eng Syst Geohazards. 12(1):14–28.
  • Chakraborty A, Goswami D. 2017. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci. 10(17):385.
  • Chen W, Chen X, Peng JB, Panahi M, Lee S. 2021. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front. 12(1):93–107.
  • de Oliveira GG, Ruiz LFC, Guasselli LA, Haetinger C. 2019. Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fao River Basin, Southern Brazil. Nat Hazards. 99(2):1049–1073.
  • Harmouzi H, Nefeslioglu HA, Rouai M, Sezer EA, Dekayir A, Gokceoglu C. 2019. Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN). Arab J Geosci. 12(22):696.
  • Hong HY, Shahabi H, Shirzadi A, Chen W, Chapi K, Bin Ahmad B, Roodposhti MS, Hesar AY, Tian YY, Bui DT. 2019. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards. 96(1):173–212.
  • Huang F, Yin K, Huang J, Gui L, Wang P. 2017. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol. 223:11–22.
  • Huang FM, Cao ZS, Guo JF, Jiang SH, Li S, Guo ZZ. 2020a. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580.
  • Huang FM, Cao ZS, Jiang SH, Zhou CB, Huang JS, Guo ZZ. 2020b. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides. 17(12):2919–2930.
  • Huang FM, Pan LH, Fan XM, Jiang SH, Huang JS, Zhou CB. 2022. The uncertainty of landslide susceptibility prediction modeling: suitability of linear conditioning factors. Bull Eng Geol Environ. 81(5):182.
  • Huang FM, Ye Z, Jiang SH, Huang JS, Chang ZL, Chen JW. 2021. Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. Catena. 202:105250.
  • Huang R, Li W. 2011. Formation, distribution and risk control of landslides in China. J Rock Mech Geotech Eng. 3(2):97–116.
  • Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S. 2017. 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 Haz Risk. 9(1):49–69.
  • Kavzoglu T, Sahin EK, Colkesen I. 2015. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol. 192:101–112.
  • Li C, Fu Z, Wang Y, Tang H, Yan J, Gong W, Yao W, Criss RE. 2019. Susceptibility of reservoir-induced landslides and strategies for increasing the slope stability in the Three Gorges Reservoir Area: Zigui Basin as an example. Eng Geol. 261:105279.
  • Liu R, Shi S, Sun D, Xu J. 2020a. Based on GIS and random forest model for landslide susceptibility mapping in Wushan county. J Chongqing Nor Univ (Nat Sci). 37:86–96.
  • Liu X, Jing R, Miao L, Han Y, Ddeng Z, Xiong C. 2020b. Reconstruction models and typical case analysis of the fluctuation belt of reservoir bank slopes in Wushan. Chin J Rock Mech Eng. 39:1321–1332.
  • Lombardo L, Mai PM. 2018. Presenting logistic regression-based landslide susceptibility results. Eng Geol. 244:14–24.
  • Lundberg SM, Lee S-I. 2017. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 30:1–10.
  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A. 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology. 94(3–4):379–400.
  • Mondal S, Mandal S. 2018. RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk: Assess Manage Risk Eng Syst Geohazards. 12(1):29–44.
  • Moosavi V, Niazi Y. 2016. Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides. 13(1):97–114.
  • Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Kariminejad N, Cerda A, Lee S. 2021. Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front. 12(2):505–519.
  • Pavel M, Nelson JD, Jonathan Fannin R. 2011. An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides. Comput Geosci. 37(4):554–566.
  • Petley D. 2012. Global patterns of loss of life from landslides. Geology. 40(10):927–930.
  • Pham BT, Trung NT, Qi CC, Phong TV, Dou J, Ho LS, Le HV, Prakash I. 2020. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena. 195:104805.
  • Pourghasemi HR, Mohammady M, Pradhan B. 2012. Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena. 97:71–84.
  • Pradhan B, Lee S. 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw. 25(6):747–759.
  • 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.
  • Sahin EK, Colkesen I, Kavzoglu T. 2018. A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int. 35(4):341–363.
  • Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C. 2019. A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors. 19(18):3940.
  • Sun D, Gu Q, Wen H, Xu J, Zhang Y, Shi S, Xue M, Zhou X. 2022a. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization.
  • Sun D, Wen H, Wang D, Xu J. 2020a. A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology. 362:107201.
  • Sun D, Wen H, Zhang Y, Xue M. 2020b. An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide. Nat Hazards. 105(2):1255–1279.
  • Sun DL, Gu QY, Wen HJ, Shi SX, Mi CL, Zhang FT. 2022b. A hybrid landslide warning model coupling susceptibility zoning and precipitation. Forests. 13(6):827.
  • Sun DL, Xu JH, Wen HJ, Wang DZ. 2021. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: a comparison between logistic regression and random forest. Eng Geol. 281:105972.
  • Sun DL, Xu JH, Wen HJ, Wang Y. 2020c. An optimized random forest model and its generalization ability in landslide susceptibility mapping: application in two areas of Three Gorges Reservoir, China. J Earth Sci. 31(6):1068–1086.
  • Tsangaratos P, Ilia I, Hong H, Chen W, Xu C. 2016. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides. 14(3):1091–1111.
  • Wang Y, Fang ZC, Hong HY. 2019a. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993.
  • Wang Y, Sun DL, Wen HJ, Zhang H, Zhang FT. 2020. Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). IJERPH. 17(12):4206.
  • Wang Y, Wen H, Sun D, Li Y. 2021. Quantitative assessment of landslide risk based on susceptibility mapping using random forest and GeoDetector. Remote Sens. 13(13):2625.
  • Wang Y, Wu X, Chen Z, Ren F, Feng L, Du Q. 2019b. Optimizing the predictive ability of machine learning methods for landslide susceptibility mapping using SMOTE for Lishui City in Zhejiang Province, China. IJERPH. 16(3):368.
  • Wen H. 2015. A susceptibility mapping model of earthquake-triggered slope geohazards based on geo-spatial data in mountainous regions. Georisk: Assess Manage Risk Eng Syst Geohazards. 9(1):25–36.
  • Xia H, Yin K, Liang X, Ma F. 2018. Landslide susceptibility assessment based on SVM-ANN Models: a case study for Wushan County in the Three Gorges Reservoir. Chin J Geol Haz Control. 29:13–19.
  • Xu CC, Sun Q, Yang XY. 2018. A study of the factors influencing the occurrence of landslides in the Wushan area. Environ Earth Sci. 77(11):406.
  • Yi YN, Zhang ZJ, Zhang WC, Jia HH, Zhang JQ. 2020. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. Catena. 195:104851.
  • Yu LB, Cao Y, Zhou C, Wang Y, Huo ZT. 2019. Landslide susceptibility mapping combining information gain ratio and support vector machines: a case study from Wushan Segment in the Three Gorges Reservoir Area, China. Applied Sciences-Basel. 9(22):4756.
  • Zhao QF, Chen W, Peng CH, Wang DZ, Xue WF, Bian HY. 2022. Modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree. Environ Earth Sci. 81(15):404.
  • 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.
  • Zhou X, Wen H, Li Z, Zhang H, Zhang W. 2022. An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost. Geocarto Int. DOI: 10.1080/10106049.2022.2076928.