Landslide Susceptibility Analysis Using Open Geo-spatial Data and Frequency Ratio Technique

Abstract: Landslide susceptibility maps are useful for spatial decision-making to minimize the lossof lives and properties. There are many studies related to the development of landslidesusceptibility maps using various methods such as Analytic Hierarchy Process, Weight ofEvidence and Logistic Regression. Commonly, the geospatial data required for such analysis(such as land cover and soil type maps) are only locally available and pertinent to smallcase studies. Transferable and scalable approaches utilizing publicly available, large scaledatasets (ie., global or continental) are necessary to develop susceptibility maps in areaswhere local data is not available or when large-scale analysis is required. To develop suchapproaches, a systematic comparison between locally available, fine resolution, and largerscale, openly available but coarser resolution datasets is essential. The objective of this study isto investigate the efficiency of globally available public data for landslide susceptibility mappingby comparing it with the performance of the data provided from local institutions. For this purpose, the Göta river valley in Sweden and the country of Rwanda were selectedas study areas. Göta river valley was used for the comparison of local and open data.While Rwanda was used as a study area to ensure the efficiency of open data analysis andtransferability of the framework. The selected landslide impact factors for this study are;elevation, slope, soil type, land cover, precipitation, lithology, distance to roads, and distanceto drainage network. Landslide susceptibility maps were prepared by using the state-of-the-artFrequency Ratio method. The validation results using the prediction rate curve technique show92.9%, 90.2%, and 83.1% area under curve values for local and open data analyses of Göta rivervalley and open data analysis of Rwanda country, respectively. The results show that globallyavailable open data demonstrate strong potential for landslide susceptibility mapping whenhigh-resolution local data are not available.