Abstract
Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.
Highlights
IntroductionLandslides are the most common geological disasters that damage property and infrastructure and result in loss of life
Two training datasets of landslide and non-landslide points were generated by random generation and manual sampling, respectively, and were taken as the inputs to Geo-detector, while Q-statistics of every conditioning factor (Figure 5) were the output
The results demonstrated that the distribution of relative contribution of all the potential landslide conditioning factors calculated from the Manual
Summary
Landslides are the most common geological disasters that damage property and infrastructure and result in loss of life. Landslide susceptibility mapping is an important tool to optimize land use planning and policy to reduce damage from landslides to public property, infrastructure and people’s lives [1,2]. Landslide susceptibility mapping refers to a division of the land into zones of hazard classes ranked according to different landslide occurrence probabilities based on an estimated significance of conditioning factors to the causes of landslides [3,4,5]. Landslide susceptibility is determined by qualitative and Remote Sens.
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