@Article{ZhangFLLWHQCL:2022:MoLaSu,
               author = "Zhang, Tingyu Y. and Fu, Quan and Li, Chao and Liu, Fangfang and 
                         Wang, Huanyuan and Han, Ling and Quevedo, Renata Pacheco and Chen, 
                         Tianqing and Lei, Na",
          affiliation = "{Key Laboratory of Degraded and Unused Land Consolidation 
                         Engineering} and {Shaanxi Provincial Land Engineering Construction 
                         Group Land Survey Planning and Design Institute Co. Ltd} and 
                         {Shaanxi Land Engineering Construction Group Co. Ltd} and {Shaanxi 
                         Provincial Land Engineering Construction Group Land Survey 
                         Planning and Design Institute Co. Ltd} and {Chang’an University} 
                         and {Chang’an University} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Key Laboratory of Degraded and Unused Land 
                         Consolidation Engineering} and {Key Laboratory of Degraded and 
                         Unused Land Consolidation Engineering}",
                title = "Modeling landslide susceptibility using data mining techniques of 
                         kernel logistic regression, fuzzy unordered rule induction 
                         algorithm, SysFor and random forest",
              journal = "Natural Hazards",
                 year = "2022",
               volume = "114",
               number = "3",
                pages = "3327--3358",
                month = "Dec.",
             keywords = "Landslide susceptibility, Kernel logistic regression, Fuzzy 
                         unordered rule induction algorithm, Systematically developed 
                         forest of multiple trees, Random forest.",
             abstract = "This paper introduces four advanced intelligent algorithms, namely 
                         kernel logistic regression, fuzzy unordered rule induction 
                         algorithm, systematically developed forest of multiple decision 
                         trees and random forest (RF), to perform the landslide 
                         susceptibility mapping in Jian'ge County, China, as well as well 
                         study of the connection between landslide occurrence and regional 
                         geo-environment characteristics. To start with, 262 landslide 
                         events were determined, and the proportion of randomly generated 
                         training data is 70%, while the proportion of randomly generated 
                         validation data is 30%, respectively. Then, through the 
                         comprehensive consideration of local geo-environment 
                         characteristics and relevant studies, fifteen conditioning factors 
                         were prepared, such as slope angle, slope aspect, altitude, 
                         profile curvature, plan curvature, sediment transport index, 
                         topographic wetness index, stream power index, distance to rivers, 
                         distance to roads, distance to lineaments, soil, land use, 
                         lithology and NDVI. Next, frequency ratio model was utilized to 
                         identify the corresponding relations for conditioning factors and 
                         landslides distribution. In addition, four data mining techniques 
                         were conducted to implement the landslide susceptibility research 
                         and generated landslide susceptibility maps. In order to examine 
                         and compare model performance, receiver operating characteristic 
                         curve was brought for judging accuracy of those four models. 
                         Finally, the results indicated that a traditional model, namely RF 
                         model, acquired the highest AUC value (0.859). Last but gained a 
                         lot of attention, the results can provide references for land use 
                         management and landslide prevention.",
                  doi = "10.1007/s11069-022-05520-7",
                  url = "http://dx.doi.org/10.1007/s11069-022-05520-7",
                 issn = "0921-030X",
             language = "en",
           targetfile = "s11069-022-05520-7.pdf",
        urlaccessdate = "30 jun. 2024"
}