Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model

References

• Adnan MSG, Rahman MS, Ahmed N, Ahmed B, Rabbi MF, Rahman RM. 2020. Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sens. 12(20):3347.
   Google Scholar
• Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigović 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.
   Web of Science ®Google Scholar
• Barančoková M, Kenderessy P. 2014. Assessment of landslide risk using GIS and statistical methods in Kysuce region. Ekológia (Bratislava). 33(1):26–35.
   Google Scholar
• Barella CF, Sobreira FG, Zêzere JL. 2019. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil. Bull Eng Geol Environ. 78(5):3205–3221.
   Web of Science ®Google Scholar
• Bochníček O, Borsányi P, Čepčeková E, Faško P, Chmelík M, Jančovičová Ľ, Kapolková H, Labudová L, Mikulová K, Mišaga O. 2015. Climate atlas of Slovakia. Bratislava: Slovak Hydrometeorological Institute.
   Google Scholar
• Breiman L. 2001. Random forests. Mach. Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Bui DT, 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(2):361–378. http://dx.doi.org/10.1007/s10346-015-0557-6.
   Web of Science ®Google Scholar
• Büttner G. 2014. CORINE land cover and land cover change products. In: Manakos I, Braun M, editors. Land use and land cover mapping in Europe remote sensing and digital image processing. Vol. 18. 18th ed. Dordrecht: Springer; p. 55–74.
   Google Scholar
• Carrión-Mero P, Montalván-Burbano N, Morante-Carballo F, Quesada-Román A, Apolo-Masache B. 2021. Worldwide Research Trends in Landslide Science. Int J Environ Res Public Health. 18(18):1–24.
   Web of Science ®Google Scholar
• Catani F, Lagomarsino D, Segoni S, Tofani V. 2013. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci. 13(11):2815–2831. https://nhess.copernicus.org/articles/13/2815/2013/.
   Web of Science ®Google Scholar
• 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. Bull Eng Geol Environ. 79(6):2799–2814. http://link.springer.com/10.1007/s10064-020-01733-x.
   Web of Science ®Google Scholar
• Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H. 2021. Xgboost: extreme gradient boosting. R package version 1321.
   Google Scholar
• Chen W, Yan X, Zhao Z, Hong H, Bui DT, Pradhan B. 2019. Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bull Eng Geol Environ. 78(1):247–266. http://link.springer.com/10.1007/s10064-018-1256-z.
   Web of Science ®Google Scholar
• Crosetto M, Tarantola S, Saltelli A. 2000. Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agric. Ecosyst. Environ. 81(1):71–79. https://linkinghub.elsevier.com/retrieve/pii/S0167880900001699.
   Web of Science ®Google Scholar
• 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 Fão River Basin, Southern Brazil. Nat Hazards. 99(2):1049–1073.
   Web of Science ®Google Scholar
• Dias Tardelli Uehara T, Paes Leme Passos Corrêa S, Pacheco Quevedo R, Sehn Körting T, Vieira Dutra L, Daleles Rennó C. 2020. Landslide scars detection using remote sensing and pattern recognition techniques: comparison among artificial neural networks, Gaussian maximum likelihood, random forest, and support vector machine classifiers. Rev Bras Cartogr. 72(4):665–680. http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/54037.
   Google Scholar
• Du Z, Wang Z, Wu S, Zhang F, Liu R. 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. Int. J. Geogr. Inform. Sci. 34(7):1353–1377. https://www.tandfonline.com/doi/abs/10.1080/13658816.2019.1707834.
   Web of Science ®Google Scholar
• Erener A, Düzgün HSB. 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides. 7(1):55–68.
   Web of Science ®Google Scholar
• Fawcett T. 2006. An introduction to ROC analysis. Pattern Recog Lett. 27(8):861–874. https://linkinghub.elsevier.com/retrieve/pii/S016786550500303X.
   Web of Science ®Google Scholar
• Feizizadeh B, Blaschke T. 2014. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. Int J Geogr Inf Sci. 28(3):610–638. http://www.tandfonline.com/doi/full/10.1080/13658816.2013.869821.
   PubMed Web of Science ®Google Scholar
• Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J. 2017. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci. 10(5):122.
   Web of Science ®Google Scholar
• Feuillet T, Coquin J, Mercier D, Cossart E, Decaulne A, Jónsson HP, Saemundsson þ. 2014. Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland: do paraglacial factors vary over space? Prog Phys Geogr. 38(3):354–377.
   Web of Science ®Google Scholar
• Florinsky IV. 2016. Topographic surface and its characterization. In: Digital terrain analysis in soil science and geology. Amsterdam: Elsevier; p. 7–76.
   Google Scholar
• Fotheringham AS, Brunsdon C, Charlton M. 2002. Geographically weighted regression: The analysis of spatially varying relationships. Hoboken: Wiley.
   Google Scholar
• Friedman JH. 2001. Greedy function approximation: a gradient boosting machine. Ann. Stat. 53(5):1189–1232.
   Google Scholar
• Froude MJ, Petley DN. 2018. Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. 18(8):2161–2181.
   Web of Science ®Google Scholar
• Georganos S, Grippa T, Niang Gadiaga A, Linard C, Lennert M, Vanhuysse S, Mboga N, Wolff E, Kalogirou S. 2021. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int. 36(2):121–136.
   Web of Science ®Google Scholar
• Georganos S, Grippa T, Gadiaga A, Vanhuysse S, Kalogirou S, Lennert M, Linard C. 2019. An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery. In: 2019 Joint Urban Remote Sensing Event (JURSE). IEEE; p. 1–4. https://ieeexplore.ieee.org/document/8809049/.
   Google Scholar
• Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT. 2012. Landslide inventory maps: new tools for an old problem. Earth Sci Rev. 112(1–2):42–66.
   Web of Science ®Google Scholar
• Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 72(1–4):272–299.
   Web of Science ®Google Scholar
• Hengl T, Nussbaum M, Wright MN, Heuvelink GBM, Gräler B. 2018. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ. 2018(8):e5518.
   Google Scholar
• Hong H, Pradhan B, Sameen MI, Chen W, Xu C. 2017. Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China). Geomatics Nat Hazards Risk. 8(2):1997–2022.
   Web of Science ®Google Scholar
• Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA. 191(March):104580.
   Google Scholar
• Huang Y, Zhao L. 2018. Review on landslide susceptibility mapping using support vector machines. Catena. 165(March):520–529.
   Google Scholar
• Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, van Westen C, Bălteanu D. 2016. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology. 253:508–523.
   Web of Science ®Google Scholar
• Kadavi PR, Lee CW, Lee S. 2019. Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environ Earth Sci. 78(4):0. http://dx.doi.org/10.1007/s12665-019-8119-1.
   Web of Science ®Google Scholar
• Kalogirou S, Georganos S. 2019. SpatialML: Spatial Machine Learning.
   Google Scholar
• 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(9):1000–1015.
   Web of Science ®Google Scholar
• Korup O, Stolle A. 2014. Landslide prediction from machine learning. Geol. Today. 30(1):26–33.
   Google Scholar
• Krušić J, Marjanović M, Samardžić-Petrović M, Abolmasov B, Andrejev K, Miladinović A. 2017. Comparison of expert, deterministic and machine learning approach for landslide susceptibility assessment in Ljubovija Municipality, Serbia. Geofizika. 34(2):251–273. http://geofizika-journal.gfz.hr/vol_34/No2/34-2_Krusic_et_al.pdf.
   Web of Science ®Google Scholar
• Kumar D, Thakur M, Dubey CS, Shukla DP. 2017. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology. 295(June):115–125.
   Google Scholar
• Lapin M, Faško P, Melo M, Šťastný P, Tomlain J. 2002. Climatic regions. In: Hrnčiarová T, editor, Landscape of the Slovak Republic. Bratislava, Banská Bystrica: MŽP SR, SAŽP.
   Google Scholar
• Li Y, Liu X, Han Z, Dou J. 2020. Spatial proximity-based geographically weighted regression model for landslide susceptibility assessment: a case study of Qingchuan area, China. Appl Sci (Switzerland). 10(3):1107.
   Google Scholar
• Merghadi A, Yunus AP, Dou J, Whiteley J, Pham BT, Bui DT, Avtar R, Abderrahmane B. 2020. Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev . 207(September 2019):103225.
   Google Scholar
• Minár J, Barka I, Jakál J, Stankoviansky M, Trizna M, Urbánek J. 2006. Geomorphological hazards in Slovakia. Studia Geomorphologica Carpatho-Balcanica. 40:61–78.
   Google Scholar
• Mokhtari M, Abedian S. 2019. Spatial prediction of landslide susceptibility in Taleghan basin, Iran. Stoch Environ Res Risk Assess. 33(7):1297–1325.
   Web of Science ®Google Scholar
• Moore ID, Grayson RB, Ladson AR. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process. 5(1):3–30. https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.3360050103.
   Web of Science ®Google Scholar
• Nhu VH, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Clague JJ, Jaafari A, Chen W, Nguyen H. 2020. Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. IJERPH. 17(14):4933.
   Web of Science ®Google Scholar
• O’Brien RM. 2007. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 41(5):673–690.
   Web of Science ®Google Scholar
• Park SJ, Lee CW, Lee S, Lee MJ. 2018. Landslide susceptibility mapping and comparison using decision tree models: a case study of Jumunjin Area, Korea. Remote Sens. 10(10):1545.
   Web of Science ®Google Scholar
• Petley D. 2012. Global patterns of loss of life from landslides. Geology. 40(10):927–930.
   Web of Science ®Google Scholar
• Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, et al. 2021. A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomat Nat Hazards Risk. 12(1):1741–1777.
   Web of Science ®Google Scholar
• Pham BT, Tien Bui D, Prakash I, Dholakia MB. 2016. Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards. 83(1):97–127.
   Web of Science ®Google Scholar
• Qin CZ, Zhu AX, Pei T, Li BL, Scholten T, Behrens T, Zhou CH. 2011. An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agric. 12(1):32–43. https://link.springer.com/article/10.1007/s11119-009-9152-y.
   Web of Science ®Google Scholar
• Quevedo RP, Guasselli LA, Oliveira GGD, Ruiz LFC. 2019. Modelagem de áreas suscetíveis a movimentos de massa_ avaliação comparativa de técnicas de amostragem, aprendizado de máquina e modelos digitais de elevação. Geociências UNESP. 38(3):781–795.
   Google Scholar
• Quevedo RP, Oliveira GG, De Guasselli LA. 2020. Mapeamento de Suscetibilidade a Movimentos de Massa a partir de Redes Neurais Artificiais. Anu Inst Geociênc. 43:128–138.
   Google Scholar
• R Core Team. 2014. R: A language and environment for statistical computing.
   Google Scholar
• Rabby YW, Hossain MB, Abedin J. 2021. Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods. Geocarto Int. 1–27. https://www.tandfonline.com/doi/abs/10.1080/10106049.2020.1864026.
   Web of Science ®Google Scholar
• Rahmati O, Kornejady A, Deo RC. 2021. Spatial prediction of landslide susceptibility using random forest algorithm. Singapore: Springer; p. 281–292. https://link.springer.com/chapter/10.1007/978-981-15-5772-9_15.
   Google Scholar
• Rasyid AR, Bhandary NP, Yatabe R. 2016. Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters. 3(1):19. http://dx.doi.org/10.1186/s40677-016-0053-x.
   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(November 2017):60–91.
   Google Scholar
• Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S. 2010. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology. 114(3):129–142.
   Web of Science ®Google Scholar
• Sahin EK. 2020. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci. 2(7):1308.
   Web of Science ®Google Scholar
• Shahri AA, Spross J, Johansson F, Larsson S. 2019. Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena. 183(August):104225.
   Google Scholar
• Simekova J, Liscak P, Janova V, Martincekova T. 2014. Atlas of slope stability maps of the Slovak Republic at Scale 1 : 50,000 – its results and use in practice. Slovak Geol Mag. 14(1):19–30.
   Google Scholar
• Šimeková J, Martinčeková T, Abrahám P, Gejdoš T, Grencíková A, Grman D, Hrašna M, Jadroň D, Záthurecký A, Kotrčková E, et al. 2006. Atlas of slope stability maps of the Slovak Republic (1: 50,000). Sro B, editor. Žilina: Publ. Ministry of the Environment of the Slovak Republic.
   Google Scholar
• Skilodimou HD, Bathrellos GD, Koskeridou E, Soukis K, Rozos D. 2018. Physical and anthropogenic factors related to landslide activity in the northern Peloponnese, Greece. Land. 7(3):85.
   Web of Science ®Google Scholar
• Smith MD, Goodchild MF, Longley P. 2007. Geospatial analysis: a comprehensive guide to principles, techniques and software tools. Leicester: Troubador Publishing Ltd.
   Google Scholar
• Sterlacchini S, Ballabio C, Blahut J, Masetti M, Sorichetta A. 2011. Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology. 125(1):51–61.
   Web of Science ®Google Scholar
• Sun D, Wen H, Wang D, Xu J. 2020. A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology. 362:107201.
   Web of Science ®Google Scholar
• Taalab K, Cheng T, Zhang Y. 2018. Mapping landslide susceptibility and types using random forest. Big Earth Data. 2(2):159–178. https://www.tandfonline.com/doi/full/10.1080/20964471.2018.1472392.
   Google Scholar
• Tsangaratos P, Ilia I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides. 13(2):305–320.
   Web of Science ®Google Scholar
• Uehara TDT, Corrêa SPLP, Quevedo RP, Körting TS, Dutra LV, Rennó CD. 2020. Landslide scars detection using remote sensing and pattern recognition techniques: Comparison among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers. Revista Brasileira de Cartografia. 72(4):665–680.
   Google Scholar
• Vakhshoori V, Zare M. 2018. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomatics Nat Hazards Risk. 9(1):249–266.
   Web of Science ®Google Scholar
• Vico G, Porporato A. 2009. Probabilistic description of topographic slope and aspect. J Geophys Res. 114(F1):F01011. http://doi.wiley.com/10.1029/2008JF001038.
   Google Scholar
• Vojteková J, Vojtek M. 2019. GIS-based landscape stability analysis: A comparison of overlay method and fuzzy model for the case study in Slovakia. Prof Geogr. 71(4):631–644.
   Web of Science ®Google Scholar
• Vojteková J, Vojtek M. 2020. Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia. Geomat Nat Hazards Risk. 11(1):131–148.
   Web of Science ®Google Scholar
• Wang Y, Feng L, Li S, Ren F, Du Q. 2020. A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China. Catena. 188(December 2019):104425.
   Google Scholar
• Wilcoxon F. 1945. Individual comparisons by ranking methods. Biometrics Bull. 1(6):80.
   Web of Science ®Google Scholar
• Zhang T, Mao Z, Wang T. 2020. GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units. J Mt Sci. 17(12):2929–2941. http://link.springer.com/10.1007/s11629-020-6393-8.
   Web of Science ®Google Scholar