Abstract
This paper assesses the performance of the landslide susceptibility analysis using frequency ratio (FR) with an iterative random sampling. A pair of before-and-after digital aerial photographs with 50 cm spatial resolution was used to detect landslide occurrences in Yongin area, Korea. Iterative random sampling was run ten times in total and each time it was applied to the training and validation datasets. Thirteen landslide causative factors were derived from the topographic, soil, forest, and geological maps. The FR scores were calculated from the causative factors and training occurrences repeatedly ten times. The ten landslide susceptibility maps were obtained from the integration of causative factors that assigned FR scores. The landslide susceptibility maps were validated by using each validation dataset. The FR method achieved susceptibility accuracies from 89.48% to 93.21%. And the landslide susceptibility accuracy of the FR method is higher than 89%. Moreover, the ten times iterative FR modeling may contribute to a better understanding of a regularized relationship between the causative factors and landslide susceptibility. This makes it possible to incorporate knowledge-driven considerations of the causative factors into the landslide susceptibility analysis and also be extensively used to other areas.
Highlights
Damage caused by natural hazards has been increasing due largely to residential expansion and population growth in many countries in the world [1,2,3]
Slope gradients were divided into nine classes, Stream power index (SPI) was divided into six classes, and Topographic wetness index (TWI) was divided into nine classes since the specific values of these three factors cover more than 10% of the study area (Table 2)
The landslide susceptibility mapping is instrumental in making a decision for the land use planning and urban development in Korea
Summary
Damage caused by natural hazards has been increasing due largely to residential expansion and population growth in many countries in the world [1,2,3]. Most studies on landslide susceptibility modeling (1) separated the training and validation data from given landslide location data through the random sampling approach, (2) created the landslide susceptibility map using the selected training data, and (3) calculated the accuracy of the landslide susceptibility map using the validation data. In those studies, the modeling procedure was applied just once and had Landslide mapping by aerial photographs and field survey 82 landslide locations. The outcomes obtained from the one-time modeling were most likely to be biased For this reason, it is logically necessary to derive the landslide susceptibility maps through a well-established statistical approach via an iterative random sampling. Running FR modeling ten times enabled us to properly understand the regularized relationship between the causative factors and landslide susceptibility
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