Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique

References

• Akinci H, Zeybek M. 2021. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey. Nat Hazards. 108(2):1515–1543. doi:10.1007/s11069-021-04743-4.
   Web of Science ®Google Scholar
• Cao Y, Yin K, Alexande DE, et al. 2016. Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides. 13:725–736.
   Web of Science ®Google Scholar
• Dou H, He J, Huang S, Jian W, Guo C. 2023. Influences of non-landslide sample selection strategies on landslide susceptibility mapping by machine learning. Geomat Nat Hazard Risk. 14(1):2285719. doi:10.1080/19475705.2023.2285719.
   Web of Science ®Google Scholar
• Fu Z, Wang F, Dou J, Nam K, Ma H. 2023. Enhanced absence sampling technique for data-driven landslide susceptibility mapping: a case study in Songyang County, China. Remote Sens. 15(13):3345. doi:10.3390/rs15133345.
   Google Scholar
• Gameiro S, Riffel ES, de Oliveira GG, Guasselli LA. 2021. Artificial neural networks applied to landslide susceptibility: the effect of sampling areas on model capacity for generalization and extrapolation. Appl Geogr. 137:102598. doi:10.1016/j.apgeog.2021.102598.
   Web of Science ®Google Scholar
• Gardner MW, Dorling SR. 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ. 32(14-15):2627–2636. doi:10.1016/S1352-2310(97)00447-0.
   Web of Science ®Google Scholar
• Gentilucci M, Pelagagge N, Rossi A, Domenico A, Pambianchi G. 2023. Landslide susceptibility using climatic–environmental factors using the weight-of-evidence method—a study area in Central Italy. Appl Sci. 13(15):8617. doi:10.3390/app13158617.
   Google Scholar
• Hong H. 2023. Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model. Ecol Indic. 147:109968. doi:10.1016/j.ecolind.2023.109968.
   Web of Science ®Google Scholar
• Hong H. 2023. Assessing landslide susceptibility based on hybrid multilayer perceptron with ensemble learning. Bull Eng Geol Environ. 82(10):382. doi:10.1007/s10064-023-03409-8.
   Web of Science ®Google Scholar
• Hong H, Wang D, Zhu AX, Wang Y. 2024. Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample. Expert Syst Appl. 243:122933. doi:10.1016/j.eswa.2023.122933.
   Web of Science ®Google Scholar
• Hu X, Mei H, Zhang H, Li Y, Li M. 2021. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping County, Southwest China. Nat Hazards. 105(2):1663–1689. doi:10.1007/s11069-020-04371-4.
   Web of Science ®Google Scholar
• Hu J, Motagh M, Wang J, Qin F, Zhang J, Wu W, Han Y. 2021. Karst collapse risk zonation and evaluation in Wuhan, China based on analytic hierarchy process, logistic regression, and InSAR angular distortion approaches. Remote Sensing. 13(24):5063. doi:10.3390/rs13245063.
   Web of Science ®Google Scholar
• Kavzoglu T, Sahin EK, Colkesen I. 2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. 11(3):425–439. doi:10.1007/s10346-013-0391-7.
   Web of Science ®Google Scholar
• Liu B, Guo H, Li J, Ke X, He X. 2024. Application and interpretability of ensemble learning for landslide susceptibility mapping along the Three Gorges Reservoir area, China. Nat Hazards. 1–32. doi:10.1007/s11069-023-06374-3.
   Web of Science ®Google Scholar
• Long J, Liu Y, Li C, Fu Z, Zhang H.,. 2021. A novel model for regional susceptibility mapping of rainfall-reservoir induced landslides in Jurassic slide-prone strata of western Hubei Province, Three Gorges Reservoir area. Stoch Environ Res Risk Assess. 35(7):1403–1426. doi:10.1007/s00477-020-01892-z.
   Web of Science ®Google Scholar
• Lucchese LV, de Oliveira GG, Pedrollo OC. 2021. Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks. Catena. 198:105067. doi:10.1016/j.catena.2020.105067.
   Web of Science ®Google Scholar
• Meena SR, Puliero S, Bhuyan K, Floris M, Catani F. 2022. Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy). Nat Hazards Earth Syst Sci. 22(4):1395–1417. doi:10.5194/nhess-22-1395-2022.
   Web of Science ®Google Scholar
• Mohammed A, Kora R. 2023. A comprehensive review on ensemble deep learning: opportunities and challenges. J King Saud Univ Comput Inform Sci. 35(2):757–774.
   Web of Science ®Google Scholar
• Pham BT, Jaafari A, Nguyen-Thoi T, Van Phong T, Nguyen HD, Satyam N, Masroor M, Rehman S, Sajjad H, Sahana M, et al. 2021. Ensemble machine learning models based on reduced error pruning tree for prediction of rainfall-induced landslides. Int J Digital Earth. 14(5):575–596. doi:10.1080/17538947.2020.1860145.
   Web of Science ®Google Scholar
• Qazi A, Singh K, Vishwakarma DK, Abdo HG. 2023. GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India. Bull Eng Geol Environ. 82(8):332. doi:10.1007/s10064-023-03344-8.
   Web of Science ®Google Scholar
• Roy P, Martha TR, Vinod Kumar K, Chauhan P, Rao VV. 2023. Cluster landslides and associated damage in the Dima Hasao district of Assam, India due to heavy rainfall in May 2022. Landslides. 20(1):97–109. doi:10.1007/s10346-022-01977-6.
   Web of Science ®Google Scholar
• Sabokbar HF, Roodposhti MS, Tazik E. 2014. Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology. 226:15–24. doi:10.1016/j.geomorph.2014.07.026.
   Web of Science ®Google Scholar
• Sharma N, Saharia M, Ramana GV. 2024. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena. 235:107653. doi:10.1016/j.catena.2023.107653.
   Web of Science ®Google Scholar
• Song J, Wang Y, Fang Z, Peng L, Hong H. 2020. Potential of ensemble learning to improve tree-based classifiers for landslide susceptibility mapping. IEEE J Sel Top Appl Earth Observations Remote Sensing. 13:4642–4662. doi:10.1109/JSTARS.2020.3014143.
   Web of Science ®Google Scholar
• Sun D, Gu Q, Wen H, Xu J, Zhang Y, Shi S, Xue M, Zhou X. 2022. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization. Gondwana Res. 123:89–106. doi:10.1016/j.gr.2022.07.013.
   Web of Science ®Google Scholar
• Tang M, Xu Q, Yang H, Li S, Iqbal J, Fu X, Huang X, Cheng W. 2019. Activity law and hydraulics mechanism of landslides with different sliding surface and permeability in the Three Gorges Reservoir Area, China. Eng Geol. 260:105212. doi:10.1016/j.enggeo.2019.105212.
   Web of Science ®Google Scholar
• Tanyu BF, Abbaspour A, Alimohammadlou Y, Tecuci G. 2021. Landslide susceptibility analyses using Random Forest, C4. 5, and C5. 0 with balanced and unbalanced datasets. Catena. 203:105355. doi:10.1016/j.catena.2021.105355.
   Web of Science ®Google Scholar
• Wang C, Lin Q, Wang L, Jiang T, Su B, Wang Y, Mondal SK, 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. doi:10.1007/s11069-022-05252-8.
   Web of Science ®Google Scholar
• Yavuz Ozalp A, Akinci H, Zeybek M. 2023. Comparative analysis of tree-based ensemble learning algorithms for landslide susceptibility mapping: a case study in Rize, Turkey. Water. 15(14):2661. doi:10.3390/w15142661.
   Web of Science ®Google Scholar
• Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides. 13(5):839–856. doi:10.1007/s10346-015-0614-1.
   Web of Science ®Google Scholar
• Yu L, Cao Y, Zhou C, Wang Y, Huo Z. 2019. Landslide susceptibility mapping combining information gain ratio and support vector machines: a case study from Wushan Segment in the Three Gorges Reservoir Area, China. Appl Sci. 9(22):4756. doi:10.3390/app9224756.
   Google Scholar
• Yu L, Zhou C, Wang Y, Cao Y, Peres DJ. 2022. Coupling data-and knowledge-driven methods for landslide susceptibility mapping in human-modified environments: a case study from Wanzhou County, Three Gorges Reservoir Area, China. Remote Sensing. 14(3):774. doi:10.3390/rs14030774.
   Web of Science ®Google Scholar
• Zhou C, Cao Y, Hu X, Yin K, Wang Y, Catani F. 2022. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area. Landslides. 19(7):1585–1597. doi:10.1007/s10346-021-01796-1.
   Web of Science ®Google Scholar
• Zhou C, Cao Y, Yin K, Intrieri E, Catani F, Wu L. 2022. Characteristic comparison of seepage-driven and buoyancy-driven landslides in Three Gorges Reservoir area, China. Eng Geol. 301:106590. doi:10.1016/j.enggeo.2022.106590.
   Web of Science ®Google Scholar
• Zhou C, Yin KL, Cao Y, Li Y. 2020a. Landslide susceptibility assessment by applying the coupling method of radial basis neural network and adaboost: A case study from the Three Gorges Reservoir area. Earth Sci. 45(6):1865–1876. doi:10.3390/rs14030774
   Google Scholar
• Zhou C, Cao Y, Yin K, Wang Y, Shi X, Catani F, Ahmed B. 2020b. Landslide characterization applying sentinel-1 images and InSAR technique: the Muyubao landslide in the three Gorges Reservoir Area, China. Remote Sensing. 12(20):3385. doi:10.3390/rs12203385.
   Web of Science ®Google Scholar
• Zhou C, Yin K, Cao Y, Ahmed B. 2016. Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng Geol. 204:108–120. doi:10.1016/j.enggeo.2016.02.009.
   Web of Science ®Google Scholar
• Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR.,. 2018. Landslide susceptibility modelling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci. 112:23–37. doi:10.1016/j.cageo.2017.11.019.
   Web of Science ®Google Scholar
• Zhu AX, Miao Y, Liu J, Bai S, Zeng C, Ma T, Hong H. 2019. A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods. Catena. 183:104188. doi:10.1016/j.catena.2019.104188.
   Web of Science ®Google Scholar