GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China

• Published in: Journal of Earth System Science Vol. 124; no. 7; pp. 1399 - 1415
• Main Authors: Wang, Qiqing, Li, Wenping, Chen, Wei, Bai, Hanying
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01-10-2015; Springer Nature B.V
• Subjects: Accuracy; Aerial photographs; Aerial photography; Aerial surveys; Construction; Curvature; Distance; Earth and Environmental Science; Earth Sciences; Entropy; Gene mapping; Geographic information systems; Geographical information systems; Geomorphology; Hazard mitigation; Land use; Land use management; Land use planning; Landslides; Landslides & mudslides; Lithology; Mitigation; Rainfall; Risk assessment; Rivers; Sediment transport; Slopes; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Susceptibility; Wetness index; China; Landslide; susceptibility mapping; certainty factor (CF); index of entropy (IOE)
• ISSN: 0253-4126; 0973-774X
• DOI: 10.1007/s12040-015-0624-3

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.