Abstract
Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.
Highlights
Landslides are major natural hazards that have a wide impact on human life, property, and natural environment
The findings of collinearity results indicate that no linearity exists among the landslide vulnerability conditioning factors (LVCFs) because the Tolerance and Variance Inflation Factor (VIF) values of these factors do not surpass their limits (Table 2) which suggests the aptness for inclusion in the modelling
The information gain ratio (IGR) values of the geo-environmental LVCFs for landslide vulnerability prediction were higher for geology, soil resistivity, rainfall, and elevation (Fig. 8a) and for socio-economic LVCFs, it is higher for distance to road and population density (Fig. 8b)
Summary
Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. The occurrence of landslides is a natural phenomenon but man-made processes are one of the causes of vulnerability to landslides, which makes it difficult to predict the spatial and temporal occurrence of landslides[7] In this connection, landslide vulnerability maps (LVMs) may be used as the primary method to classify the high-risk zones which are prone to landslides and help in identifying the variables responsible for the occurrence of landslides[8]. The inclusion of these man-made processes as causative factors to produce a spatial vulnerability map is of utmost importance
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