Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China

References

• Ahmadlou M, Ebrahimian Ghajari Y, Karimi M. 2022. Enhanced classification and regression tree (CART) by genetic algorithm (GA) and grid search (GS) for flood susceptibility mapping and assessment. Geocarto Int. :1–20.
   Web of Science ®Google Scholar
• Akinci H, Zeybek M. 2021. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin). Nat Hazards. 108(2):1515–1543.
   Web of Science ®Google Scholar
• Arab Amiri M, Conoscenti C. 2017. Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran. Nat Hazards. 89(1):255–273.
   Web of Science ®Google Scholar
• Arabameri A, Chandra Pal S, Rezaie F, Chakrabortty R, Saha A, Blaschke T, Napoli D, Ghorbanzadeh O, Thi Ngo PT. 2021. Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto Int. 37(16):1–35.
   Web of Science ®Google Scholar
• Arabameri A, Pradhan B, Lombardo L. 2019. Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling. Catena. 183:104223.
   Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Biçici S, Zeybek M. 2021. Effectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle-based point cloud. Transp Res Rec. 2675(12):401–418.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Machine Learning. 45(1):5–32.
   Web of Science ®Google Scholar
• Brenning A. 2012. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package sperrorest. In 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE; p. 5372–5375..
   Google Scholar
• Centre for Research on the Epidemiology of Disasters–CRED. 2022. EM-DAT: the International Disaster Database [accessed 15 February 2022]. https://public.emdat.be/data/.
   Google Scholar
• Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB. 2019. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena. 172:212–231.
   Web of Science ®Google Scholar
• Cortes C, Vapnik V. 1995. Support-vector networks. Mach Learn. 20(3):273–297.
   Web of Science ®Google Scholar
• Dietterich TG. 2000. Ensemble methods in machine learning. In International workshop on multiple classifier systems. Berlin: Springer; p. 1–15.
   Google Scholar
• Dorogush AV, Ershov V, Gulin A. 2018. CatBoost: gradient boosting with categorical features support. arXiv Preprint arXiv. doi:10.48550/arXiv.1810.11363.
   Google Scholar
• Fausett LV. 2006. Fundamentals of neural networks: architectures, algorithms and applications. India: Pearson Education India.
   Google Scholar
• Frattini P, Crosta G, Carrara A. 2010. Techniques for evaluating the performance of landslide susceptibility models. Eng Geol. 111(1-4):62–72.
   Web of Science ®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
• Garosi Y, Sheklabadi M, Pourghasemi HR, Besalatpour AA, Conoscenti C, Van Oost K. 2018. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma. 330:65–78.
   Web of Science ®Google Scholar
• Haque U, Da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, et al. 2019. The human cost of global warming: deadly landslides and their triggers (1995–2014). Sci Total Environ. 682:673–684.
   PubMed Web of Science ®Google Scholar
• Hartmann J, Moosdorf N. 2012. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem Geophys Geosyst. 13(12). doi:10.1029/2012GC004370
   Google Scholar
• Hassangavyar MB, Damaneh HE, Pham QB, Linh NTT, Tiefenbacher J, Bach QV. 2022. Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility. Geocarto Int. 37(10):2772–2794.
   Web of Science ®Google Scholar
• Huang F, Cao Z, Guo J, Jiang SH, Li S, Guo Z. 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena. 191:104580.
   Web of Science ®Google Scholar
• Huang CM, Lee CT, Jian LX, Wei LW, Chu WC, Lin HH. 2022. Using fuzzy neural networks to model landslide susceptibility at the Shihmen Reservoir catchment in Taiwan. Water. 14(8):1196.
   Web of Science ®Google Scholar
• Hürlimann M, Guo Z, Puig-Polo C, Medina V. 2022. Impacts of future climate and land cover changes on landslide susceptibility: regional scale modelling in the Val d’Aran region (Pyrenees, Spain). Landslides. 19(1):99–118.
   Web of Science ®Google Scholar
• Hussain MA, Chen Z, Kalsoom I, Asghar A, Shoaib M. 2022. Landslide susceptibility mapping using machine learning algorithm: a case study along Karakoram highway (KKH), Pakistan. J Indian Soc Remote Sens. 50(5):849–866.
   Web of Science ®Google Scholar
• IPCC. 2022. Climate change 2022: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. UK: Cambridge University Press. In Press.
   Google Scholar
• Kaiser M, Günnemann S, Disse M. 2022. Regional-scale prediction of pluvial and flash flood susceptible areas using tree-based classifiers. J Hydrol. 612:128088.
   Web of Science ®Google Scholar
• Kavzoglu T, Teke A. 2022. Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arab J Sci Eng. 47(6):7367–7385.
   Web of Science ®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
• Kim HG, Lee DK, Park C, Kil S, Son Y, Park JH. 2015. Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios. Environ Earth Sci. 73(3):1385–1400.
   Web of Science ®Google Scholar
• Kotsiantis SB, Zaharakis I, Pintelas P. 2007. Supervised machine learning: a review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering. 160(1):3–24.
   Google Scholar
• 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(15):1665–1679.
   Web of Science ®Google Scholar
• Linke S, Lehner B, Ouellet Dallaire C, Ariwi J, Grill G, Anand M, Beames P, Burchard-Levine V, Maxwell S, Moidu H, et al. 2019. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci Data. 6(1):1–15.
   PubMedGoogle Scholar
• Lin Q, Lima P, Steger S, Glade T, Jiang T, Zhang J, Liu TX, Wang Y. 2021. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data. Geosci Front. 12(6):101248.
   Web of Science ®Google Scholar
• Lin Q, Steger S, Pittore M, Zhang J, Wang L, Jiang T, Wang Y. 2022. Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change. Sci Total Environ. 850:158049.
   PubMed Web of Science ®Google Scholar
• Lin Q, Wang Y. 2018. Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides. 15(12):2357–2372.
   Web of Science ®Google Scholar
• Lin Q, Wang Y, Liu T, Zhu Y, Sui Q. 2017. The vulnerability of people to landslides: a case study on the relationship between the casualties and volume of landslides in China. Int J Environ Res Public Health 14(2):212.
   PubMed Web of Science ®Google Scholar
• Lombardo L, Mai PM. 2018. Presenting logistic regression-based landslide susceptibility results. Eng Geol. 244:14–24.
   Web of Science ®Google Scholar
• Mehrabi M. 2022. Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy. Nat Hazards. 111(1):901–937.
   Web of Science ®Google Scholar
• Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B. 2020. Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev. 207:103225.
   Web of Science ®Google Scholar
• Miao F, Zhao F, Wu Y, Li L, Török Á. 2022. Landslide susceptibility mapping in three gorges reservoir area based on GIS and boosting decision tree model. doi:10.21203/rs.3.rs-1838482/v1.
   Google Scholar
• Naghibi SA, Pourghasemi HR, Dixon B. 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess. 188(1):1–27.
   PubMed Web of Science ®Google Scholar
• National Bureau of Statistics of China. 2021. China statistical yearbook 2021. China: China Statistics Press.
   Google Scholar
• Nhu VH, Hoang ND, Nguyen H, Ngo PTT, Bui TT, Hoa PV, Samui P, Bui DT. 2020. Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena. 188:104458.
   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
• Orhan O, Bilgilioglu SS, Kaya Z, Ozcan AK, Bilgilioglu H. 2020. Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto Int. 37(10):2795–2820.
   Web of Science ®Google Scholar
• Paola JD, Schowengerdt RA. 1995. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Trans Geosci Remote Sens. 33(4):981–996.
   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
• Phong TV, Phan TT, Prakash I, Singh SK, Shirzadi A, Chapi K, Ly H, Ho LS, Quoc NK, Pham BT. 2021. Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto Int. 36(15):1685–1708.
   Web of Science ®Google Scholar
• Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B. 2017. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology. 298:118–137.
   Web of Science ®Google Scholar
• Reichenbach P, Busca C, Mondini AC, Rossi M, 2014. The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage. 54(6):1372–1384.
   PubMed Web of Science ®Google Scholar
• Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F. 2018. A review of statistically-based landslide susceptibility models. Earth Sci Rev. 180:60–91.
   Web of Science ®Google Scholar
• Roccati A, Paliaga G, Luino F, Faccini F, Turconi L. 2021. GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land. 10(2):162.
   Web of Science ®Google Scholar
• Sadiki M, Aghad M, Batchi M, Al Karkouri J. 2022. Landslide susceptibility mapping using machine learning techniques and comparison of their performance at Ziz upper watershed, Southeastern Morocco. doi:10.21203/rs.3.rs-1534262/v1
   Google Scholar
• Sahin EK. 2022. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto Int. 37(9):2441–2465.
   Web of Science ®Google Scholar
• Saito H, Nakayama D, Matsuyama H. 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology. 109(3-4):108–121.
   Web of Science ®Google Scholar
• Sarwar MS, Fatima SA, Aman M, Shoaib A, Aslam B, Zafar A. 2022. Landslide susceptibility mapping for Muzaffarabad region using machine learning algorithms. doi:10.21203/rs.3.rs-1504882/v1.
   Google Scholar
• Shahabi H, Hashim M. 2015. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep. 5(1):1–15.
   Web of Science ®Google Scholar
• Sheng M, Zhou J, Chen X, Teng Y, Hong A, Liu G. 2022. Landslide susceptibility prediction based on frequency ratio method and C5.0 decision tree model. Front Earth Sci. 10:918386.
   Web of Science ®Google Scholar
• Steger S, Brenning A, Bell R, Glade T. 2017. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements. Landslides. 14(5):1767–1781.
   Web of Science ®Google Scholar
• Trabucco A, Zomer RJ. 2010. Global soil water balance geospatial database. CGIAR Consortium for Spatial Information. http://www.cgiar-csi.org
   Google Scholar
• Wang G, Chen X, Chen W. 2020a. Spatial prediction of landslide susceptibility based on GIS and discriminant functions. Int J Geoinf. 9(3):144.
   Google Scholar
• Wang N, Cheng W, Wang B, Liu Q, Zhou C. 2020b. Geomorphological regionalization theory system and division methodology of China. J Geogr Sci. 30(2):212–232.
   Web of Science ®Google Scholar
• Wang D, Hao M, Chen S, Meng Z, Jiang D, Ding F. 2021c. Assessment of landslide susceptibility and risk factors in China. Nat Hazards. 108(3):3045–3059.
   Web of Science ®Google Scholar
• Wang C, Lin Q, Wang L, Jiang T, Su B, Wang Y, Mondal S, Huang J, Wang Y. 2022. The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling: a case study of Anhui Province in China. Nat Hazards. 112(3):1967–1988.
   Web of Science ®Google Scholar
• Wang N, Lombardo L, Gariano SL, Cheng W, Liu C, Xiong J, Wang R. 2021a. Using satellite rainfall products to assess the triggering conditions for hydro-morphological processes in different geomorphological settings in China. Int J Appl Earth Obs Geoinf. 102:102350.
   Web of Science ®Google Scholar
• Wang H, Zhang L, Luo H, He J, Cheung RWM. 2021b. AI-powered landslide susceptibility assessment in Hong Kong. Eng Geol. 288:106103.
   Web of Science ®Google Scholar
• Xiong K, Adhikari BR, Stamatopoulos CA, Zhan Y, Wu S, Dong Z, Di B. 2020. Comparison of different machine learning methods for debris flow susceptibility mapping: a case study in the Sichuan Province, China. Remote Sens. 12(2):295.
   Google Scholar
• Ye P, Yu B, Chen W, Liu K, Ye L. 2022. Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China. Nat Hazards. 113(2):965–995.
   Web of Science ®Google Scholar
• 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(2):639–655.
   Web of Science ®Google Scholar
• Zeybek M. 2021. Classification of UAV point clouds by random forest machine learning algorithm. Turk J Eng. 5(2):48–57.
   Google Scholar
• Zhang H, Song Y, Xu S, He Y, Li Z, Yu X, Liang Y, Wu W, Wang Y. 2022. Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: a case study of Wanzhou section of the Three Gorges Reservoir, China. Comput. Geosci. 158:104966.
   Web of Science ®Google Scholar
• Zhao G, Pang B, Xu Z, Yue J, Tu T. 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ. 615:1133–1142.
   PubMed Web of Science ®Google Scholar