Abstract
In recent years, numerous landslide susceptibility assessment studies using conventional machine learning models have been carried out, and a series of achievements have been made. To acquire better landslide susceptibility mapping results, various ensemble techniques have been adopted to construct strong classifiers for landslide susceptibility prediction. Generally, for the same base classifier, it is necessary to compare the effects of multiple ensemble techniques and determine the best one. In this paper, a naïve Bayes tree (NBTree) was employed as the base classifier, and three popular ensemble techniques, namely, Bagging (Bag), RandomSubSpace (RS), and MultiBoostAB (MB), were applied to build ensemble landslide susceptibility models for Jian’ge County, China. Herein, a total of 262 landslides were included in the landslide inventory map. Then, the training and validation datasets were randomly divided at a ratio of 70/30. Moreover, fifteen conditioning factors related to topography, geology, vegetation, and human activities were selected to train landslide susceptibility models. The correlations between conditioning factors and landslide occurrence were also measured by weights of evidence (WoE). Ultimately, the performance of each landslide susceptibility model was quantitatively evaluated by receiver operating characteristic (ROC) curves and areas under the curves (AUCs). The results show that all the ensemble models outperform the NBTree model with the validation datasets, and the Bag-NBTree model exhibits the best performance on the processing validation dataset (AUC = 0.852). Additionally, as landslide susceptibility levels are escalated, the corresponding frequency of landslide occurrence significantly increases, indicating that the landslide susceptibility maps (LSMs) produced by the four models are rational and effective. Overall, this study is of great significance to landslide prevention and mitigation in Jian’ge County.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abedini M, Ghasemian B, Shirzadi A, Shahabi H, Chapi K, Pham BT, Bin Ahmad B, Tien Bui D (2019) A novel hybrid approach of Bayesian Logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int 34:1427–1457. https://doi.org/10.1080/10106049.2018.1499820
Agterberg FP (1992) Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources 1:39–50. https://doi.org/10.1007/BF01782111
Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095. https://doi.org/10.1007/s10346-014-0521-x
Aktas H, San BT (2019) Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling. Comput Geosci 133:104329. https://doi.org/10.1016/j.cageo.2019.104329
Al-Najjar HAH, Pradhan B (2021) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front 12(2):625–637. https://doi.org/10.1016/j.gsf.2020.09.002
Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Linh NTT, Nguyen HQ, Ahmad A, Ghorbani MA (2020) Gis-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa Basin. Slovakia Eeol Indic 117:106620. https://doi.org/10.1016/j.ecolind.2020.106620
Alkhasawneh MS, Ngah UK, Tay LT, Isa NAM (2014) Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network. Environ Earth Sci 72:787–799. https://doi.org/10.1007/s12665-013-3003-x
Arabameri A, Chen W, Loche M, Zhao X, Li Y, Lombardo L, Cerda A, Pradhan B, Bui DT (2020) Comparison of machine learning models for gully erosion susceptibility mapping. Geosci Front 11(5):1609–1620. https://doi.org/10.1016/j.gsf.2019.11.009
Ávila FF, Alvalá RC, Mendes RM, Amore DJ (2021) The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models. Nat Hazards 105:1139–1161. https://doi.org/10.1007/s11069-020-04324-x
Bahrami Y, Hassani H, Maghsoudi A (2021) Landslide susceptibility mapping using ahp and fuzzy methods in the Gilan Province. Iran Geojournal 86:1797–1816. https://doi.org/10.1007/s10708-020-10162-y
Bai M, Du Y, Chen Y, Xing Y, Zhao P (2017) Risk assessment of long gas and oil pipeline projects inducing landslide disasters during construction. J Perform Constr Fac 31:04017063. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000986
Barbieri G, Cambuli P (2009) The weight of evidence statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). Univ Western Australia, Nedlands
Batar AK, Watanabe T (2021) Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (Woe) method in the Indian Himalayan Region: recent developments, gaps, and future directions. Isprs Int Geo-Inf 10(3):114. https://doi.org/10.3390/ijgi10030114
Breiman L (1996) Bagging Predictors Mlear 24:123–140
Bui DT, Tsangaratos P, Ngo P-TT, Pham TD, Pham BT (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054. https://doi.org/10.1016/j.scitotenv.2019.02.422
Bui DT, Tsangaratos P, Nguyen V-T, Liem NV, Trinh PT (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426. https://doi.org/10.1016/j.catena.2019.104426
Caiyan W, Jianping Q, Meng W (2006) Landslides and slope aspect in the Three Gorges Reservoir area based on Gis and information value model. Wuhan Univ J Nat Sci 11:773–779. https://doi.org/10.1007/BF02830163
Canavesi V, Segoni S, Rosi A, Ting X, Nery T, Catani F, Casagli N (2020) Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: a case study in Rio De Janeiro (Brazil). Remote Sens-Basel 12(11):1826. https://doi.org/10.3390/rs12111826
Capitani M, Ribolini A, Bini M (2013) The slope aspect: a predisposing factor for landsliding? C R Geosci 345:427–438. https://doi.org/10.1016/j.crte.2013.11.002
Chalkias C, Polykretis C, Karymbalis E, Soldati M, Ghinoi A, Ferentinou M (2020) Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression. B Eng Geol Environ 79:2799–2814. https://doi.org/10.1007/s10064-020-01733-x
Chawla A, Pasupuleti S, Chawla S, Rao ACS, Sarkar K, Dwivedi R (2019) Landslide susceptibility zonation mapping: a case study from Darjeeling District, Eastern Himalayas, India. J Indian Soc Remot 47:497–511. https://doi.org/10.1007/s12524-018-0916-6
Chen W, Chen Y, Tsangaratos P, Ilia I Wang X (2020) Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens-Basel 12(23):3854. https://doi.org/10.3390/rs12233854
Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N (2017) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomat Nat Haz Risk 8:1955–1977. https://doi.org/10.1080/19475705.2017.1401560
Chen W, Sun Z, Han J (2019) Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Appl Sci 9(1):171. https://doi.org/10.3390/app9010171
Chen X, Liu C, Chang Z, Zhou Q (2016) The relationship between the slope angle and the landslide size derived from limit equilibrium simulations. Geomorphology 253:547–550. https://doi.org/10.1016/j.geomorph.2015.01.036
Chen Y Chen W Chandra Pal S Saha A Chowdhuri I Adeli B Janizadeh S Dineva AA Wang X Mosavi A (2021a) Evaluation Efficiency of hybrid deep learning algorithms with neural network, decision tree and boosting methods for predicting groundwater potential. Geocarto Int:1–21.https://doi.org/10.1080/10106049.2021a
Chen Y, Chen W, Rahmati O, Falah F, Kulakowski D, Lee S, Rezaie F, Panahi M, Bahmani A, Darabi H, Torabi Haghighi A, Bian H (2021b) Toward the development of deep-learning analyses for snow avalanche releases in mountain regions. Geocarto Int:1–26. https://doi.org/10.1080/10106049.2021b
Dağ S, Akgün A, Kaya A, Alemdağ S, Bostancı HT (2020) Medium Scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey. Environ Earth Sci 79:468. https://doi.org/10.1007/s12665-020-09217-7
Dapporto S, Rinaldi M, Casagli N (2001) Failure mechanisms and pore water pressure conditions: analysis of a riverbank along the Arno River (Central Italy). Eng Geol 61:221–242. https://doi.org/10.1016/S0013-7952(01)00026-6
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. https://doi.org/10.1016/j.catena.2019.104211
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 65:135–165. https://doi.org/10.1007/s11069-012-0347-6
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 17:1897–1914. https://doi.org/10.1007/s10346-020-01392-9
Dikshit A, Pradhan B, Alamri AM (2021) Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Res 100:290–301. https://doi.org/10.1016/j.gr.2020.08.007
Do HM, Yin KL, Guo ZZ (2020) A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the flow-R model for landslide susceptibility assessment. Geomat Nat Haz Risk 11:2449–2485. https://doi.org/10.1080/19475705.2020.1846086
Elmahdy S, Ali T, Mohamed M (2020) Flash flood susceptibility modeling and magnitude index using machine learning and geohydrological models: a modified hybrid approach. Remote Sens-Basel 12(17):2695. https://doi.org/10.3390/rs12172695
Ercanoglu M, Kasmer O, Temiz N (2008) Adaptation and comparison of expert opinion to analytical hierarchy process for landslide susceptibility mapping. B Eng Geol Environ 67:565–578. https://doi.org/10.1007/s10064-008-0170-1
ESRI (2014) Arcgis Desktop: Release 10.2 Redlands, Ca: Environmental Systems Research Institute.
Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZJEg (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng Geol 102:99–111. https://doi.org/10.1016/j.enggeo.2008.03.014
Frank E, Hall A. M, Witten H. I (2016) The Weka Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition.
Garcia-Chevesich P, Wei X, Ticona J, Martínez G, Zea J, García V, Alejo F, Zhang Y, Flamme H, Graber A, Santi P, McCray J, Gonzáles E, Krahenbuhl R (2021) The impact of agricultural irrigation on landslide triggering: a review from Chinese, English, and Spanish literature. Water 13(1):10. https://doi.org/10.3390/w13010010
Gariano SL, Sarkar R, Dikshit A, Dorji K, Brunetti MT, Peruccacci S, Melillo M (2019) Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan. B Eng Geol Environ 78:4325–4332. https://doi.org/10.1007/s10064-018-1415-2
Goyes-Peñafiel P, Hernandez-Rojas A (2021) Landslide susceptibility index based on the integration of logistic regression and weights of evidence: a case study in Popayan. Colombia Eng Geol 280:105958. https://doi.org/10.1016/j.enggeo.2020.105958
Guido W, Gerald M (2008) Effect of cliff shape on internal stresses and rock slope stability. J Coastal Res 2008:43–50. https://doi.org/10.2112/05-0569.1
Gupta V, Kumar S, Kaur R, Tandon RS (2021) Regional-scale landslide susceptibility assessment for the Hilly State of Uttarakhand, Nw Himalaya. India J Earth Syst Sci 131:2. https://doi.org/10.1007/s12040-021-01746-4
Hao L, Rajaneesh A, van Westen C, Sajinkumar KS, Martha TR, Jaiswal P, McAdoo BG (2020) Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12:2899–2918. https://doi.org/10.5194/essd-12-2899-2020
He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, Wang X, Chapi K, Ahmad BB (2019a) Landslide spatial modelling using novel bivariate statistical based naïve Bayes, Rbf classifier, and Rbf network machine learning algorithms. Sci Total Environ 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
He Q, Xu Z, Li S, Li R, Zhang S, Wang N, Pham TB, Chen W (2019b) Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy 21(2):106. https://doi.org/10.3390/e21020106
Ho TK (1998) Random subspace method for constructing decision forests. Ieee T Pattern Anal 20:832–844. https://doi.org/10.1109/34.709601
Hong H, Kornejady A, Soltani A, Termeh SVR, Liu J, Zhu A, Hesar AY, Ahmad BB, Wang Y (2018) Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (Hand). Earth Sci Inform 11:605–622. https://doi.org/10.1007/s12145-018-0352-8
Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118
Hong H, Pradhan B, Xu C, Tien Bui D (2015) Spatial prediction of landslide hazard at the Yihuang Area (China) Using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA 133:266–281. https://doi.org/10.1016/j.catena.2015.05.019
Hong H, Shahabi H, Shirzadi A, Chen W, Chapi K, Ahmad BB, Roodposhti MS, Yari Hesar A, Tian Y, Tien Bui D (2019) Landslide susceptibility assessment at the Wuning Area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards 96:173–212. https://doi.org/10.1007/s11069-018-3536-0
Hu X, Zhang H, Mei H, Xiao D, Li Y, Li M (2020) Landslide susceptibility mapping using the stacking ensemble machine learning method in Lushui, Southwest China. Appl Sci 10(11):4016. https://doi.org/10.3390/app10114016
Huang F, Tao S, Chang Z, Huang J, Fan X, Jiang S-H, Li W (2021) Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments. Landslides 18:3715–3731. https://doi.org/10.1007/s10346-021-01756-9
Huang Q, Xu X, Kulatilake PHSW, Lin F (2020) Formation mechanism of a rainfall triggered complex landslide in Southwest China. J Mt Sci 17:1128–1142. https://doi.org/10.1007/s11629-019-5736-9
Huang R (2009) Some Catastrophic landslides since the twentieth century in the Southwest of China. Landslides 6:69–81. https://doi.org/10.1007/s10346-009-0142-y
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194
Ikram N, Basharat M, Ali A, Usmani NA, Gardezi SAH, Hussain ML, Riaz MT (2021) Comparison of landslide susceptibility models and their robustness analysis: a case study from the Nw Himalayas, Pakistan. Geocarto Int:1–38. https://doi.org/10.1080/10106049.2021.2017010
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. https://doi.org/10.1016/j.scitotenv.2018.10.431
Kanwal S, Atif S, Shafiq M (2017) Gis based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomat Nat Haz Risk 8:348–366. https://doi.org/10.1080/19475705.2016.1220023
Kohavi R (1996) Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. AAAI Press, Portland, Oregon. pp 202–207
Lee S, Lee MJ, Jung HS, Lee S (2020) Landslide susceptibility mapping using naïve Bayes and Bayesian network models in Umyeonsan, Korea. Geocarto Int 35:1665–1679. https://doi.org/10.1080/10106049.2019.1585482
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. https://doi.org/10.1016/j.jhydrol.2021.126684
Li D, Yan L, Wu L, Yin K, Leo C (2019) The Hejiapingzi landslide in Weining County, Guizhou Province, Southwest China: a recent slow-moving landslide triggered by reservoir drawdown. Landslides 16:1353–1365. https://doi.org/10.1007/s10346-019-01189-5
Li J, Wang X, Jia H, Liu Y, Zhao Y, Shi C, Zhang F, Wang K (2021a) Assessing the soil moisture effects of planted vegetation on slope stability in shallow landslide-prone areas. J Soils Sediments 21: 2551–2565. https://doi.org/10.1007/s11368-021-02957-4
Li M, Ma C, Du C, Yang W, Lyu L, Wang X (2021b) Landslide response to vegetation by example of July 25–26, 2013, extreme rainstorm, Tianshui, Gansu Province, China. B Eng Geol Environ 80:751–764. https://doi.org/10.1007/s10064-020-02000-9
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 (2021c) Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms. Geocarto Int:1–25. https://doi.org/10.1080/10106049.2021c
Liang T, Knappett JA, Bengough AG, Ke YX (2017) Small-scale modelling of plant root systems using 3d printing, with applications to investigate the role of vegetation on earthquake-induced landslides. Landslides 14:1747–1765. https://doi.org/10.1007/s10346-017-0802-2
Liu R, Peng J, Leng Y, Lee S, Panahi M, Chen W, Zhao X (2021) Hybrids of support vector regression with Grey Wolf optimizer and firefly algorithm for spatial prediction of landslide susceptibility. Remote Sensing 13(24):4966. https://doi.org/10.3390/rs13244966
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 26:127–141. https://doi.org/10.1007/s41324-017-0160-0
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234. https://doi.org/10.1016/j.enggeo.2011.09.006
McGuire LA, Rengers FK, Kean JW, Coe JA, Mirus BB, Baum RL, Godt JW (2016) Elucidating the role of vegetation in the initiation of rainfall-induced shallow landslides: insights from an extreme rainfall event in the Colorado Front Range. Geophys Res Lett 43:9084–9092. https://doi.org/10.1002/2016GL070741
Mohammadi S, Taiebat H (2016) Finite element simulation of an excavation-triggered landslide using large deformation theory. Eng Geol 205:62–72. https://doi.org/10.1016/j.enggeo.2016.02.012
Nakileza BR, Nedala S (2020) Topographic influence on landslides characteristics and implication for risk management in Upper Manafwa Catchment. Mt Elgon Uganda Geoenvironmental Disasters 7:27. https://doi.org/10.1186/s40677-020-00160-0
Nguyen TT, Liu C-C (2019) A new approach using ahp to generate landslide susceptibility maps in the Chen-Yu-Lan Watershed, Taiwan. Sensors-Basel 19(3):505. https://doi.org/10.3390/s19030505
Nguyen V-T, Tran TH, Ha NA, Ngo VL, Nadhir A-A, Tran VP, Duy Nguyen H, M. A M, Amini A, Prakash I, Ho LS, Pham BT (2019) Gis based novel hybrid computational intelligence models for mapping landslide susceptibility: a case study at Da Lat City, Vietnam. Sustainability 11(24):7118. https://doi.org/10.3390/su11247118
Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J, Luu C, Górski K, Pham BT, Nguyen HD, Ahmad BB (2020) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve Bayes tree, artificial neural network, and support vector machine algorithms. Int J Environ Res Public Health 17(8):2749. https://doi.org/10.3390/ijerph17082749
Nseka D, Kakembo V, Bamutaze Y, Mugagga F (2019) Analysis of topographic parameters underpinning landslide occurrence in Kigezi Highlands of Southwestern Uganda. Nat Hazards 99:973–989. https://doi.org/10.1007/s11069-019-03787-x
Nsengiyumva JB, Luo G, Amanambu AC, Mind’je R, Habiyaremye G, Karamage F, Ochege FU, Mupenzi C (2019) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Sci Total Environ 659:1457–1472. https://doi.org/10.1016/j.scitotenv.2018.12.248
Pham BT, Jaafari A, Prakash I, Bui DT (2019a) A novel hybrid intelligent model of support vector machines and the multiboost ensemble for landslide susceptibility modeling. B Eng Geol Environ 78:2865–2886. https://doi.org/10.1007/s10064-018-1281-y
Pham BT, Nguyen-Thoi T, Qi C, Phong TV, Dou J, Ho LS, Le HV, Prakash I (2020a) Coupling Rbf neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 195:104805. https://doi.org/10.1016/j.catena.2020.104805
Pham BT, Nguyen MD, Bui K-TT, Prakash I, Chapi K, Bui DT (2019b) 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. https://doi.org/10.1016/j.catena.2018.10.004
Pham BT, Phong TV, Nguyen Thoi T, Parial K K, Singh S, Ly HB, Nguyen KT, Ho LS, Le HV, Prakash I (2020b) Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int 1–23. https://doi.org/10.1080/10106049.2020.1737972
Pham BT, Prakash I (2019) A novel hybrid model of Bagging-based naïve Bayes trees for landslide susceptibility assessment. B Eng Geol Environ 78:1911–1925. https://doi.org/10.1007/s10064-018-1281-y
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2017) 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. Theor Appl Climatol 128:255–273. https://doi.org/10.1007/s00704-015-1702-9
Pham BT, Van Dao D, Acharya TD, Van Phong T, Costache R, Van Le H, Nguyen HBT, Prakash I (2021) Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis. Environ Earth Sci 80:686. https://doi.org/10.1007/s12665-021-09998-5
Polykretis C, Ferentinou M, Chalkias C (2015) A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River Catchments (Northern Peloponnesus, Greece). B Eng Geol Environ 74:27–45. https://doi.org/10.1007/s10064-014-0607-7
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. https://doi.org/10.1007/s12517-018-3531-5
Qiu H Regmi AD Cui P Hu S Wang Y He Y (2017) Slope aspect effects of loess slides and its spatial differentiation in different geomorphologic types. Arab J Geosci 10:344.https://doi.org/10.1007/s12517-017-3135-5
Rabby YW, Li Y (2020) Landslide susceptibility mapping using integrated methods: a case study in the Chittagong Hilly Areas, Bangladesh. Geosciences 10(12):483. https://doi.org/10.3390/geosciences10120483
Ram P, Gupta V, Devi M, Vishwakarma N (2020) Landslide susceptibility mapping using bivariate statistical method for the Hilly Township of Mussoorie and its surrounding areas, Uttarakhand Himalaya. J Earth Syst Sci 129:167. https://doi.org/10.1007/s12040-020-01428-7
Roy J, Saha S, Arabameri A, Blaschke T, Bui TD (2019) A novel ensemble approach for landslide susceptibility mapping (Lsm) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sens-Basel 11:2866. https://doi.org/10.3390/rs11232866
Sachdeva S, Bhatia T, Verma AK (2020) A novel voting ensemble model for spatial prediction of landslides using Gis. Int J Remote Sens 41:929–952. https://doi.org/10.1080/01431161.2019.1654141
Saha A, Saha S (2021) Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding Area of Darjeeling Himalayan, India: Rs-Gis approach. Environ Dev Sustain 23:4453–4483. https://doi.org/10.1007/s10668-020-00783-1
Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35:341–363. https://doi.org/10.1080/10106049.2018.1516248
Sameen MI, Sarkar R, Pradhan B, Drukpa D, Alamri AM, Park H-J (2020) Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests. Comput Geosci 134:104336. https://doi.org/10.1016/j.cageo.2019.104336
Sarkar S, Roy AK, Martha TR (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India 82:351–362. https://doi.org/10.1007/s12594-013-0162-z
Segoni S, Tofani V, Rosi A, Catani F, Casagli N (2018) Combination of rainfall thresholds and susceptibility maps for dynamic landslide hazard assessment at regional scale. Front Earth Sci 6:85. https://doi.org/10.3389/feart.2018.00085
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-Basel 19(18):3940. https://doi.org/10.3390/s19183940
Shao X, Ma S, Xu C, Zhou Q (2020) Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of Coseismic Landslides. Geomorphology 363:107222. https://doi.org/10.1016/j.geomorph.2020.107222
Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–135. https://doi.org/10.1007/s100440200011
Smith HG, Spiekermann R, Betts H, Neverman AJ (2021) Comparing methods of landslide data acquisition and susceptibility modelling: examples from New Zealand. Geomorphology 381:107660. https://doi.org/10.1016/j.geomorph.2021.107660
Sonker I, Tripathi JN, Singh AK (2021) Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya. Quaternary Sci Adv 4:100039. https://doi.org/10.1016/j.qsa.2021.100039
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. https://doi.org/10.1016/j.enggeo.2020.105972
Sun D, Xu J, Wen H, Wang Y (2020) 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-China 31:1068–1086. https://doi.org/10.1007/s12583-020-1072-9
Sun G, Yang Y, Cheng S, Zheng H (2016) Phreatic Line calculation and stability analysis of slopes under the combined effect of reservoir water level fluctuations and rainfall. Can Geotech J 54:631–645. https://doi.org/10.1139/cgj-2016-0315
Tang R-X, Kulatilake PHSW, Yan EC, Cai J-S (2020) Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. B Eng Geol Environ 79:2235–2254. https://doi.org/10.1007/s10064-019-01684-y
Thi Ngo PT, 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 12:505–519. https://doi.org/10.1016/j.gsf.2020.06.013
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
Tien Bui D, 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 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides, analysis and control, special report 176: Transportation research board. National Academy of Sciences, Washington, DC, pp 11–33
Wang J, Liang Y, Zhang H, Wu Y, Lin X (2014) A Loess landslide induced by excavation and rainfall. Landslides 11:141–152. https://doi.org/10.1007/s10346-013-0418-0
Webb GI (2000) Multiboosting: a technique for combining boosting and wagging. Mach Learn 40:159–196. https://doi.org/10.1023/a:1007659514849
Wei X, Zhang L, Luo J, Liu D (2021) A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping. Nat Hazards 109:471–497. https://doi.org/10.1007/s11069-021-04844-0
Wohlers A, Damm B (2021) Analysis of historical data for a better understanding of post-construction landslides at an artificial waterway. Earth Surf Proc Land 46:344–356. https://doi.org/10.1002/esp.5028
Wu R, Zhang Y, Guo C, Yang Z, Tang J, Su F (2020) Landslide susceptibility assessment in mountainous area: a case study of Sichuan–Tibet Railway, China. Environ Earth Sci 79:157. https://doi.org/10.1007/s12665-020-8878-8
Xiao T, Segoni S, Chen L, Yin K, Casagli N (2020) A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches. Landslides 17:627–640. https://doi.org/10.1007/s10346-019-01299-0
Yang Z-h, Lan H-x, Gao X, Li L-p, Meng Y-s, Wu Y-m (2015) Urgent Landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75:2467–2487. https://doi.org/10.1007/s11069-014-1441-8
Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth Area, Jizan, Kingdom of Saudi Arabia, using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci 73:8499–8518. https://doi.org/10.1007/s12665-014-4008-9
Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (Mlp) and radial basic function (Rbf) algorithms. Arab J Geosci 6:2873–2888. https://doi.org/10.1007/s12517-012-0610-x
Zhang M, Nie L, Xu Y, Dai S (2015) A thrust load-caused landslide triggered by excavation of the slope toe: a case study of the Chaancun Landslide in Dalian City, China. Arab J Geosci 8:6555–6565. https://doi.org/10.1007/s12517-014-1710-6
Zhang T-y, Han L, Zhang H, Zhao Y-h, Li X-a, Zhao L (2019) Gis-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. J Mt Sci 16:1275–1288. https://doi.org/10.1007/s11629-018-5337-z
Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens-Basel 12(14):2180. https://doi.org/10.3390/rs12142180
Zhou Z, Shen J-h, Li Y, Duan W-f, Yang R-c, Shu J-c, Li H-w, Tao S-y, Zheng S-z (2021) Mechanism of colluvial landslide induction by rainfall and slope construction: a case study. J Mt Sci 18:1013–1033. https://doi.org/10.1007/s11629-020-6048-9
Zhu AX, Miao Y, Wang R, Zhu T, Deng Y, Liu J, Yang L, Qin C-Z, Hong H (2018) A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. CATENA 166:317–327. https://doi.org/10.1016/j.catena.2018.04.003
Zhu AX, Wang R, Qiao J, Qin C-Z, Chen Y, Liu J, Du F, Lin Y, Zhu T (2014) An expert knowledge-based approach to landslide susceptibility mapping using Gis and fuzzy logic. Geomorphology 214:128–138. https://doi.org/10.1016/j.geomorph.2014.02.003
Zou Z, Tang H, Criss RE, Hu X, Xiong C, Wu Q, Yuan Y (2021) A model for interpreting the deformation mechanism of reservoir landslides in the Three Gorges Reservoir Area, China. Nat Hazards Earth Syst Sci 21:517–532. https://doi.org/10.5194/nhess-21-517-2021
Zhang T, Fu Q, Wang H, Liu F, Wang H, Han L (2022) Bagging-Based Machine Learning Algorithms for Landslide Susceptibility Modeling. Nat Hazards 110:823–846. https://doi.org/10.1007/s11069-021-04986-1
Acknowledgements
The authors wish to express their sincere thanks to Chaohong Peng (Sichuan Institute of Geological Engineering Investigation Group Co. Ltd) for the useful information provided.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Yan, H., Chen, W. Landslide susceptibility modeling based on GIS and ensemble techniques. Arab J Geosci 15, 762 (2022). https://doi.org/10.1007/s12517-022-09974-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-022-09974-8