Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. Hence, a novel framework has been proposed to update landslide susceptibility by simultaneously considering the spatial and temporal effects of landslides at the regional scale. In this framework, the landslide inventory of Chongyi County has been divided into pre- and fresh-landslide inventories. According to the LSP results predicted by the support vector machine (SVM) model using the slope unit-based conditioning factors and pre-landslide inventory, a normalized spatial distance index (NSDI) is calculated to quantitatively represent the spatial correlation between landslides and surrounding slope units to develop the SVM-NSDI model. Furthermore, the SVM-Updating model, which incorporates the LSP results of the SVM-NSDI model and fresh-landslide inventory, could be developed to update the LSP results. Subsequently, the confusion matrix, the area under the receiver operating characteristic curve (AUC) and frequency ratio (FR) accuracy are used to evaluate the prediction performance of the above LSP models. The F1-score values of the SVM, SVM-NSDI and SVM-Updating models are 0.776, 0.816 and 0.831, respectively. The AUC values are 0.869, 0.903 and 0.914 and the FR accuracies are 0.795, 0.853 and 0.873. It can be concluded that landslide susceptibility is a time-variant variable, which can be updated by considering the spatial correlation between landslides and surrounding slope units as well as the temporal effects of multi-temporal landslide inventory. This study provides a new framework to update landslide susceptibility over time and also provides more accurate LSP results for decision-makers.
An updating of landslide susceptibility prediction from the perspective of space and time
Meena S. R.;Catani F.
2023
Abstract
Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. Hence, a novel framework has been proposed to update landslide susceptibility by simultaneously considering the spatial and temporal effects of landslides at the regional scale. In this framework, the landslide inventory of Chongyi County has been divided into pre- and fresh-landslide inventories. According to the LSP results predicted by the support vector machine (SVM) model using the slope unit-based conditioning factors and pre-landslide inventory, a normalized spatial distance index (NSDI) is calculated to quantitatively represent the spatial correlation between landslides and surrounding slope units to develop the SVM-NSDI model. Furthermore, the SVM-Updating model, which incorporates the LSP results of the SVM-NSDI model and fresh-landslide inventory, could be developed to update the LSP results. Subsequently, the confusion matrix, the area under the receiver operating characteristic curve (AUC) and frequency ratio (FR) accuracy are used to evaluate the prediction performance of the above LSP models. The F1-score values of the SVM, SVM-NSDI and SVM-Updating models are 0.776, 0.816 and 0.831, respectively. The AUC values are 0.869, 0.903 and 0.914 and the FR accuracies are 0.795, 0.853 and 0.873. It can be concluded that landslide susceptibility is a time-variant variable, which can be updated by considering the spatial correlation between landslides and surrounding slope units as well as the temporal effects of multi-temporal landslide inventory. This study provides a new framework to update landslide susceptibility over time and also provides more accurate LSP results for decision-makers.
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