Articles written in Journal of Earth System Science
Volume 124 Issue 7 October 2015 pp 1399-1415
The main goal of this study is to produce landslide susceptibility maps for the Qianyang County of Baoji city, China, using both certainty factor (CF) and index of entropy (IOE) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 81 landslide locations were detected. Out of these, 56 (70%) landslides were randomly selected as training data for building landslide susceptibility models and the remaining 25 (30%) were used for the validation purposes. Then, a total number of 15 landslide causative factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, the sediment transport index (STI), the stream power index (SPI), the topographic wetness index (TWI), geomorphology, lithology, and rainfall, were used in the analysis. The susceptibility maps produced using CF and IOE models had five different susceptibility classes such as very low, low, moderate, high, and very high. Finally, the output maps were validated using the validation data (i.e., 30% landslide location data that was not used during the model construction), using the area under the curve (AUC) method. The `success rate' curve showed that the area under the curve for CF and IOE models were 0.8433 (84.33%) and 0.8227 (82.27%) accuracy, respectively. Similarly, the validation result showed that the susceptibility map using CF model has the higher prediction accuracy of 82.32%, while for IOE model it was 80.88%. The results of this study showed that the two landslide susceptibility maps obtained were successful and can be used for preliminary land use planning and hazard mitigation purpose.
Volume 125 Issue 3 April 2016 pp 645-662
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.
Volume 129, 2020
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