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      https://www.ias.ac.in/article/fulltext/jess/125/03/0645-0662

    • Keywords

       

      Landslide; susceptibility mapping; evidential belief function (EBF); weights of evidence (WoE); geographic information system (GIS), China

    • Abstract

       

      The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (DaguanCounty, China) by evidential belief function (EBF) model and weights of evidence (WoE) model tocompare the results obtained. For this purpose, a landslide inventory map was constructed mainly basedon earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70%(136 landslides) for training the models and the remaining 30% (58 landslides) was used for validationpurpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, generalcurvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distancefrom faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI),stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently,landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validationof landslide susceptibility map was accomplished with the area under the curve (AUC) method. Thesuccess rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map usingEBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results ofthis study showed that both landslide susceptibility maps obtained were successful and would be usefulfor regional spatial planning as well as for land cover planning.

    • Author Affiliations

       

      Qiqing Wang1 Wenping Li1 Yanli Yanli Wu1 Yabing Pei1 Maolin Xing1 Dongdong Yang1

      1. School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China.
    • Dates

       
  • Journal of Earth System Science | News

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