Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey

GIS-based landslide susceptibility zoning using a coupled model: a case study in Badong County, China

Landslide susceptibility zoning is necessary for landslide risk management. This study aims to conduct the landslide susceptibility evaluation based on a model coupled with information value (IV) and logistic regression (LR) for Badong County in Hubei Province, China. Through the screening of landslide predisposing factors based on correlation analysis, a spatial database including 11 landslide factors and 588 historical landslides was constructed in ArcGIS. The IV, LR and their coupled model were then developed. To validate the accuracy of the three models, the receiver operating characteristic curves (ROC) and the landslide density curves were correspondingly created. The results showed that the areas under the receiver operating characteristic curve (AUCs) of the three models were 0.758, 0.786 and 0.818, respectively. Moreover, the landslide density increased exponentially with the landslide susceptibility, but the coupled model exhibited a higher growth rate among the three models, indicating good performance of the proposed model in landslide susceptibility evaluation. The landslide susceptibility map generated by the coupled model demonstrated that the high and very high landslide susceptibility area mainly concentrated along rivers and roads. Furthermore, by counting the landslide numbers and analyzing the landslide susceptibility within each town in Badong County, it was discovered that Yanduhe, Xinling, Dongrangkou and Guandukou were the main landslide-prone areas. This research will contribute to landslide prevention and mitigation and serve as a reference for other areas.

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.