@Article{ZhangQWFLWOGR:2022:ImTrMa,
               author = "Zhang, Tingyu and Quevedo, Renata Pacheco and Wang, Huanyuan and 
                         Fu, Quan and Luo, Dan and Wang, Tao and Oliveira, Guilherme Garcia 
                         de and Guasselli, Laurindo Antonio and Renn{\'o}, Camilo 
                         Daleles",
          affiliation = "{Key Laboratory of Degraded and Unused Land Consolidation 
                         Engineering} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Key Laboratory of Degraded and Unused Land 
                         Consolidation Engineering} and Shaanxi Provincial Land Engineering 
                         Construction Group Land Survey Planning, Design Institute Co and 
                         Shaanxi Provincial Land Engineering Construction Group Land Survey 
                         Planning, Design Institute Co and Shaanxi Provincial Land 
                         Engineering Construction Group Land Survey Planning, Design 
                         Institute Co and {Universidade Federal do Rio Grande do Sul 
                         (UFRGS)} and {Universidade Federal do Rio Grande do Sul (UFRGS)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improved tree-based machine learning algorithms combining with 
                         bagging strategy for landslide susceptibility modeling",
              journal = "Arabian Journal of Geosciences",
                 year = "2022",
               volume = "15",
               number = "2",
                pages = "183",
             keywords = "Landslide susceptibility · Decision tree · Logistic model tree · 
                         Reduced error pruning tree · Hybrid models ·, Bagging strategy.",
             abstract = "Landslide is considered one of the most dangerous natural hazards. 
                         Reasonable landslide susceptibility mapping can aid decision 
                         makers in landslide prevention. For this reason, based on the feld 
                         survey data of landslide in Chenggu County, Shaanxi Province, 
                         China, 15 conditioning factors (altitude, slope, aspect, plan 
                         curvature, profle curvature, SPI, TWI, distance to roads, distance 
                         to rivers, distance to faults, rainfall, NDVI, soil, lithology, 
                         and land use) were selected and quantifed by the certainty factor 
                         index. Then, 184 landslides data were divided into training and 
                         validation datasets according to the ratio of 7/3. Based on the 
                         GIS platform, three hybrid tree-based models, namely decision tree 
                         (DT), logistic model tree (LMT), and reduced error pruning tree 
                         (REPT), were established. Additionally, the bagging method was 
                         applied to build three baghybrid tree-based models: Bag-DT, 
                         Bag-LMT, and Bag-REPT. Finally, the landslide susceptibility maps 
                         were produced, and statistical indexes, seed cell area index and 
                         the ROC curve, were used for model validation and comparison. The 
                         results showed that the bagging method can signifcantly improve 
                         the classifcation ability of hybrid models. Furthermore, the 
                         BagREPT presented the best performance, with an accuracy value of 
                         92.5%, being a suitable model for landslide susceptibility mapping 
                         in the study area.",
                  doi = "10.1007/s12517-022-09488-3",
                  url = "http://dx.doi.org/10.1007/s12517-022-09488-3",
                 issn = "1866-7511",
                label = "lattes: 7712719010541171 9 ZhangQWFLWOGR:2022:ImTrMa",
             language = "en",
           targetfile = "Zhang2022_Article_ImprovedTree-basedMachineLearn.pdf",
        urlaccessdate = "25 jun. 2024"
}