Abstract
The rainfall can contribute significantly to landslide events, especially in hilly areas. The landslide susceptibility map (LSM) usually helps to mitigate disasters. However, how to accurately predict the susceptibility of landslides is still a difficult point in the field of disaster research. In this study, five advanced machine learning technologies (MLTs), including the Light Gradient Boosting Machine, extreme gradient boost, categorical boosting (CatBoost), support vector machine, and random forest, are utilized to landslide susceptibility modeling and their capabilities are compared through evaluation indicators. The northern part of Yanping, Fujian Province, China, is selected as the research object, because this area experienced mass landslide events due to extremely heavy rainfall in June 2010, resulting in many casualties and a large number of public facilities destroyed. The influencing factors for landslides, namely topographic, hydrological, geologic and human activities, are prepared from various data sources based on the availability. Through the analysis of the actual situation in the study area, 13 suitable landslide condition factors are considered and the availability of relevant factors is checked according to the multicollinearity test. The landslide inventory including 631 samples in this study area is obtained from historical information, satellite data in Google earth and performed field surveys. The landslide inventory is randomly divided into two datasets for model training and testing with a 7:3 ratio. The area under the curve of ROC, accuracy rate, Kappa index and F1 score are applied to compare the MLTs capabilities. In this paper, the results of factor importance analysis show that the first three important condition factors are the distance to faults, the distance to drainages and the slope. According to the LSMs, in the study area, the central and western regions are at high and very high landslide susceptibility levels, while almost all the eastern and northeastern regions are at medium and low landslide susceptibility levels. The CatBoost model is a very promising technology in landslide research according to the evaluation results, which means that for landslide susceptibility research, gradient boosting algorithms may get more accurate results and show better prospects in the future. Finally, the results of this paper will contribute to environmental protection to a certain extent.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Are K, Tien Bui D, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52:394–403. https://doi.org/10.1016/j.ecolind.2014.12.028
Avand M, Kuriqi A, Khazaei M, Ghorbanzadeh O (2022) DEM resolution effects on machine learning performance for flood probability mapping. J Hydro-Environ Res 40:1–16. https://doi.org/10.1016/j.jher.2021.10.002
Avand M, Moradi H, Lasboyee MR (2021) Spatial prediction of future flood risk: an approach to the effects of climate change. Geosciences 11(1):25–45. https://doi.org/10.3390/geosciences11010025
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81. https://doi.org/10.1007/s10346-003-0006-9
Azizi V, Hu GP (2020) Multi-product pickup and delivery supply chain design with location-routing and direct shipment. Int J Prod Econ 226:107648. https://doi.org/10.1016/j.ijpe.2020.107648
Beguería S (2006) Validation and evaluation of predictive models in hazard and risk assessment. Nat Hazards 37:315–329. https://doi.org/10.1007/s11069-005-5182-6
Belsley D (1991) A guide to using the collinearity diagnostics. Comput Sci Econ Manage 4:33–50. https://doi.org/10.1007/BF00426854
Booth GD, Niccolucci MJ, Schuster EG (1994) Identifying proxy sets in multiple linear regression:an aid to better coefficient interpretation. US Dept of Agric For Serv Ogden. https://doi.org/10.2170/jjphysiol.50.463
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Camilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSOpenalized Generalized Linear Model. Environ Model Softw 97:145–156. https://doi.org/10.1016/j.envsoft.2017.08.003
Carrara A, Pike RJ (2008) GIS technology and models for assessing landslide hazard and risk. Geomorphology 94:257–260. https://doi.org/10.1016/j.geomorph.2006.07.042
Cao J, Zhang Z, Du J, Zhang LL, Song Y, Sun G (2020) Multi-geohazards susceptibility mapping based on machine learning-a case study in Jiuzhaigou. China Nat Hazards 102(3):851–871. https://doi.org/10.1007/s11069-020-03927-8
Cheng S, Zhang S, Li L, Zhang D (2018) Water quality monitoring method based on TLD 3D fish tracking and XGBoost. Math Probl Eng 7:1–12. https://doi.org/10.1155/2018/5604740
Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64. https://doi.org/10.1016/j.jafrearsci.2016.02.019
Comert R, Avdan U, Gorum T, Nefeslioglu HA (2019) Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data. Eng Geol 3:105264. https://doi.org/10.1016/j.enggeo.2019.105264
Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gomez-Gutierrez A, Rotigliano E, Agnesi V (2015) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology 242:49–64. https://doi.org/10.1016/j.geomorph.2014.09.020
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406. https://doi.org/10.1007/s12665-010-0724-y
Costache R, Țîncu R, Elkhrachy I, Pham QB, Popa MC, Diaconu DC, Avand M, Costache I, Arabameri A, Tien Bui D (2020) New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping. Hydrol Sci J 65(16):2816–2837. https://doi.org/10.1080/02626667.2020.1842412
Cross M (2002) Landslide susceptibility mapping using the matrix assessment approach: a Derbyshire case study. Eng Geol Spec Publ 15:247–261. https://doi.org/10.1144/GSL.ENG.1998.015.01.26
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. https://doi.org/10.1007/s11069-012-0347-6
Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2012) Collinearity: a reviewof methods to deal with it and a simulation study evaluating their performance. Ecography (Cop) 36:27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Dou J, Yunus AP, Tien Bui D (2019a) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638. https://doi.org/10.3390/rs11060638
Dou J, Yunus AP, Tien Bui D, Merghadi A, Mehebub S, Zhu ZF, Chen CW, Khosravi K, Yang Y, Pham BT (2019b) 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. https://doi.org/10.1016/j.scitotenv.2019.01.221
Dou J, Yunus AP, Merghadi A, Shirzadi A, Nguyen H, Hussain Y, Avtar R, Chen YL, Pham BT, Yamagishi H (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 720:137720. https://doi.org/10.1016/j.scitotenv.2020.137320
Drucker H, Burges CJ, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. In: Advances in Neural Information Processing Systems, pp 155–161
Duna CR, D’Arcy M, McDonald J, Whittaker CA (2018) Lithological controls on hillslope sediment supply: Insights from landslide activity and grain size distributions. Earth Surf Process Landf. https://doi.org/10.1002/esp.4281
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250. https://doi.org/10.1016/j.enggeo.2004.06.001
Evans IS (1979) An integrated system of terrain analysis and slope mapping. Zeitschrift Fur Geomorphologie 36:274–295. https://doi.org/10.3987/R-1985-01-0033
Feizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster-Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin. Iran Int J Digital Earth 7(8):688–708. https://doi.org/10.1080/17538947.2012.749950
Fernández T, Irigaray C, El Hamdouni R, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa Area (Granada, Spain). Nat Hazards 30:297–308. https://doi.org/10.1023/B:NHAZ.0000007092.51910.3f
Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181. https://doi.org/10.5194/nhess-18-2161-2018
Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modelling and spatial prediction of soil attributes. Int J Geogr Inf Syst 9:421–432
Ghasemian B, Asl DT, Pham BT, Avand M, Nguyen HD, Janizadeh S (2020) Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam J Earth Sci 42(3):208–227. https://doi.org/10.15625/0866-7187/42/3/14952
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Rem Sens. 11(2):196–217. https://doi.org/10.3390/rs11020196
Godt JW, Baum RL, Savage WZ (2008) Transient deterministic shallow landslide modeling: requirements for susceptibility and hazard assessments in a GIS framework. Eng Geol 102:214–226. https://doi.org/10.1016/j.enggeo.2008.03.019
Gokceoglu C, Aksoy H (1996) Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol 44:147–161. https://doi.org/10.1016/S0013-7952(97)81260-4
Guillard C, Zezere J (2012) Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: the case of Loures Municipality. Environ Manage 50:721–735. https://doi.org/10.1007/s00267-012-9921-7
Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorol Atmos Phys 98(3):239–267. https://doi.org/10.1007/s00703-007-0262-7
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K (2012) Landslide inventory maps: new tools for an old problem. Earth-Science Rev 112:42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Hadji R, Boumazbeur AE, Limani Y, Baghem M, Chouabi AEM, Demdoum A (2013) Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quat Int 302:224–237. https://doi.org/10.1016/j.quaint.2012.11.027
Haigh M, Rawat JS (2012) Landslide disasters: seeking causes–a case study from Uttarakhand, India. Management of Mountain Watersheds, pp 218–253. https://doi.org/10.1007/978-94-007-2476-1-18
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36. https://doi.org/10.1148/radiology.143.1.7063747
He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Hong HY, Pourhasemi 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. https://doi.org/10.1016/j.geomorph.2016.02.012
Hong HY, Tsangaratos P, Ilia I, Liu J, Zhu AX, Chen W (2018) Application of fuzzy weight of evidence and data mining techniques in the construction of flood susceptibility map of Poyang County, China. Sci Total Environ, https://doi.org/10.1016/j.scitotenv
Hosmer DW, Lemeshow S (2000) Applied Logistic Regression, 2nd edn. Wiley-Blackwell, Hoboken, NJ, USA
Huang G, Wu L, Ma X, Zhang W, Fan J, Yu X, Zeng W, Zhou H (2019) Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. J Hydrol 574:1029–1041. https://doi.org/10.1016/j.jhydrol.2019.04.085
Irigaray C, Fernández T, El Hamdouni R, Chacón J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41:61–79. https://doi.org/10.1007/s11069-006-9027-8
Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165. https://doi.org/10.1016/j.rse.2014.05.013
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 Hazards Risk 9:49–69. https://doi.org/10.1080/19475705.2017.1407368
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3):347–366. https://doi.org/10.1016/j.enggeo.2006.03.004
Keesstra SD, Quinton JN, van der Putten WH, Bardgett RD, Fresco LO (2016) The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2(2):111–128. https://doi.org/10.5194/soil-2-111-2016
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30:3146–3154
Kim JC, Lee S, Jung HS, Lee S (2018) Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto Int 33:1000–1015. https://doi.org/10.1080/10106049.2017.1323964
Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. CATENA 152:144–162. https://doi.org/10.1016/j.catena.2017.01.010
Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30(1):26–33. https://doi.org/10.1111/gto.12034
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region. Uttarakhand J Geol Soc India 87:271286. https://doi.org/10.1007/s12594-016-0395-8
Lagomarsino D, Tofani V, Segoni S, Catani F, Casagli NA (2017) Tool for classification and regression using random forest methodology: applications to landslide susceptibility mapping and soil thickness modeling. Environ Model Assess 22:201–214. https://doi.org/10.1007/s10666-016-9538-y
Lee S, Chwae U, Min K (2002) Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area. Korea Geomorphol 46(3–4):149–162. https://doi.org/10.1016/S0169-555X(02)00057-0
Lee S, Hong SM, Jung HS (2017) A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability 9(1):48–63. https://doi.org/10.3390/su9010048
Liao D, Valliant R (2012) Variance inflation factors in the analysis of complex survey data. Surv Methodol 38:53–62
Li H, Xu Y, Zhou J, Wang X, Yamagishi H, Dou J (2020a) Preliminary analyses of a catastrophic landslide occurred on July 23, 2019, in Guizhou Province, China. Landslides 17:719–724. https://doi.org/10.1007/s10346-019-01334-0
Li Y, Liu X, Han Z, Dou J (2020b) Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: a case study of Qingchuan area. China Appl Sci 10:1107. https://doi.org/10.3390/app10031107
Lombardo L, Cama M, Conoscenti C, Märker M, Rotigliano E (2015) Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Nat Hazards 79:1621–1648. https://doi.org/10.1007/s11069-015-1915-3
Magliulo P, DiLisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47:411–435. https://doi.org/10.1007/s11069-008-9230-x
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslides, earthquakes, and erosion. Earth Planet Sci Lett 229:45–59. https://doi.org/10.1016/j.epsl.2004.10.018
Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics(ROC) and relative operating levels (ROL) curves: statistical significance andinterpretation. Q J R Meteorol Soc 128:2145–2166. https://doi.org/10.1256/003590002320603584
Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6(1):17–26. https://doi.org/10.1007/s10346-008-0138-z
Ma X, Sha J, Wang D, Yu Y, Yang Q, Niu X (2018) Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron Commer Res Appl 31:24–39. https://doi.org/10.1016/j.elerap.2018.08.002
Merghadi A, Abderrahmane B, Tien Bui D (2018) Landslide susceptibility assessment at Mila Basin (Algeria): a comparative assessment of prediction capability of advanced machine learning methods. ISPRS Int J Geo-Inf 7(7):268–298. https://doi.org/10.3390/ijgi7070268
Moore ID, Burch GJ (1986) Physical basis of length–slope factor in the universal soil loss equation. Soil Sci Soc Am J 50:1294–1298. https://doi.org/10.2136/sssaj1986.03615995005000050042x
Moradi H, Avand M, Janizadeh S (2019) Landslid susceptibility survey using modeling methods. Spat Model GIS R Earth Environ Sci 2019:259–275. https://doi.org/10.1016/B978-0-12-815226-3.00011-9
O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690. https://doi.org/10.1007/s11135-006-9018-6
Park NW (2015) Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environ Earth Sci 73(3):937–949. https://doi.org/10.1007/s12665-014-3442-z
Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT, Le TM, Van Phong T, Khoi DK, Shirzadi A, Tien Bui D (2019) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int. https://doi.org/10.1080/10106049.2018.1559885
Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Tran T, Tien Bui D (2019b) Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175:203–218. https://doi.org/10.1016/j.catena.2018.12.018
Pham BT, Phong TV, Avand M, Al-Ansari N, Singh SK, Le HV, Prakash I (2020) Improving voting feature intervals for spatial prediction of landslides. Math Problems Eng. https://doi.org/10.1155/2020/4310791
Pradhan AMS, Kim YT (2019) Rainfall-induced shallow landslide susceptibility mapping at two adjacent catchments using advanced machine learning algorithms. ISPRS Int J Geo-Inf 9(10):569–591. https://doi.org/10.3390/ijgi9100569
Pourghasemi HR, Goli Jirandeh A, 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. https://doi.org/10.1007/s12040-013-0282-2
Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192. https://doi.org/10.1016/j.catena.2017.11.022
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: unbiased boosting with categorical features. Adv Neural Inf Process Syst 31:6638–6648
Quan H, Lee B (2012) GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju(Korea). KSCE J Civil Eng 16:1258–1266. https://doi.org/10.1007/s12205-012-1242-0
Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 115:172–187. https://doi.org/10.1016/j.geomorph.2009.10.002
Saber M, Boulmaiz T, Guermoui M, Abdrado KI, Kantoush SA, Sumi T, Boutaghane H, Nohara D, Mabrouk E (2021) Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction. Geocarto Int 2021:1974595. https://doi.org/10.1080/10106049.2021.1974959
Sassa K, Guzzetti F, Yamagishi H, Arbanas Ž, Casagli N, Tiwari B, Liu KF, Strom A, McSaveney M, McSaveney E, Dang K, Setiawan H (2017) Landslide dynamics: ISDR-ICL landslide interactive teaching tools (LITT). Advancing Culture of Living With Landslides. Springer International Publishing Cham, pp193–218. https://doi.org/10.1007/978-3-319-59469-9_17
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 Random For Sens 19(18):3940. https://doi.org/10.3390/s19183940
Shrestha S, Kang TS, Suwal M (2017) An ensemble model for co-seismic landslide susceptibility using GIS and random forest method. ISPRS Int J Geo-Inf 6:365–387. https://doi.org/10.3390/ijgi6110365
Song Y, Niu R, Xu S, Ye R, Peng L, Guo T, Li S, Chen T (2019) Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the three gorges reservoir area (China). ISPRS Int J Geo-Inf 8(1):4–23. https://doi.org/10.3390/ijgi8010004
Suzen ML, Doyuran V (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment. Turkey Eng Geol 71(3–4):303–321. https://doi.org/10.1016/S0013-7952(03)00143-1
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using random forest. Big Earth Data 00:1–20. https://doi.org/10.1080/20964471.2018.1472392
Tien Bui D, Tuan TA, Hoang ND, Thanh NQ, Nguyen DB, Liem NV, Pradhan B (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14(2):447–458. https://doi.org/10.1007/s10346-016-0711-9
Tien Bui D, Shirzadi A, Shahabi H, Geertsema M, Omidvar E, Clague JJ, Thai Pham B, Dou J, Talebpour Asl D, Bin Ahmad B, Lee S (2019) New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests 10(9):743–765. https://doi.org/10.3390/f10090743
Tseng CM, Lin CW, Hsieh WD (2015) Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Nat Hazard Earth Syst 3(2):1137–1173. https://doi.org/10.5194/nhessd-3-1137-2015
Van Den Eeckhaut M, Moeyersons J, Nyssen J, Abraha A, Poesen J, Haile M, Deckers J (2009) Spatial patterns of old, deep-seated landslides: a case study in the northern Ethiopian highlands. Geomorphology 105:239–252. https://doi.org/10.1016/j.geomorph.2008.09.027
Wang F, Fan X, Yunus AP, Siva Subramanian S, Alonso-Rodriguez A, Dai L, Xu Q, Huang R (2019) Coseismic landslides triggered by the 2018 Hokkaido, Japan (Mw 6.6), earthquake: spatial distribution, controlling factors, and possible failure mechanism. Landslides 16:1551–1566. https://doi.org/10.1007/s10346-019-01187-7
Wang Y, Song C, Lin Q, Li J (2016) Occurrence probability assessment of earthquaketriggered landslides with Newmark displacement values and logistic regression: the Wenchuan earthquake, China. Geomorphology 258:108–119. https://doi.org/10.1016/j.geomorph.2016.01.004
Wang Z, Hong T, Piette MA (2020) Building thermal load prediction through shallow machine learning and deep learning. Appl Energy 263:114683. https://doi.org/10.1016/j.apenergy.2020.114683
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014
Yang CF (2016) Critical rainfall amount of geological hazards at Northern Mountains in Fujian Province (Yanping District). J Water Resour Archit Eng 14(5):219–223. https://doi.org/10.3969/j.issn.1672-1144
Yariyan P, Avand M, Abbaspour RA, Haghighi AT, Costache R, Ghorbanzadeh O, Janizadeh S, Blaschke T (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Haz Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582. https://doi.org/10.1016/j.geomorph.2008.02.011
Yousefi S, Avand M, Yariyan P, Pourghasemi HR, Keesstra S, Tavangar S, Tabibian S (2020) A novel GIS-based ensemble technique for rangeland downward trend mapping as an ecological indicator change. Ecol Ind 117:106591. https://doi.org/10.1016/j.ecolind.2020.106591
Youssef AM (2016) 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
Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geosci Front 12:639–655. https://doi.org/10.1016/j.gsf.2020.05.010
Yunus AP, Fan X, Tang X, Jie D, Xu Q, Huang R (2020) Decadal vegetation succession from MODIS reveals the spatio-temporal evolution of post-seismic landsliding after the 2008 Wen chuan earthquake. Remote Sens Environ 236:111476. https://doi.org/10.1016/j.rse.2019.111476
Zhang RH, Wu CZ, Goh ATC, Böhlke T, Zhang WG (2020a) Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning. Geosci Front 12(2021):365–373
Zhang WG, Zhang RH, Wu CZ, Goh ATC, Lacasse S, Liu ZQ, Liu HL (2019a) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11:1095–1106. https://doi.org/10.1016/j.gsf.2020.03.003
Zhang Y, Zhao Z, Zheng J (2020b) CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. J Hydrol 588:125087. https://doi.org/10.1016/j.jhydrol.2020.125087
Zhang YH, Ge TT, Tian W, Liou YA (2019b) Debris flow susceptibility mapping using machine-learning techniques in Shigatse Area, China. Remote Sens Basel 11(23):2801–2827. https://doi.org/10.3390/rs11232801
Acknowledgements
This work is supported by the National Natural Science Foundation of China (NSFC, contract number: U21A2032) and Fujian Science and technology disaster prevention project (Fujian Department of Natural Resources [2020], No. 46).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ye, P., Yu, B., Chen, W. et al. Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China. Nat Hazards 113, 965–995 (2022). https://doi.org/10.1007/s11069-022-05332-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-022-05332-9