Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and Wuxi counties, China

Liao, Mingyong; Wen, Haijia; Yang, Lei

doi:10.1016/j.catena.2022.106428

Cited by 69 publications

(23 citation statements)

References 87 publications

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“…Slope influences the speed of water flow and accumulation. Importantly, it is directly related to landslides, as they contribute significantly toward slope failure (Liao et al, 2022; Nguyen, Dang, et al, 2022). In the study area, the slope value ranged from 0 to 83°.…”

Section: Methods and Techniquesmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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Section: Methods and Techniquesmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

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“…The basic steps for landslide susceptibility modelling are data collection and construction of thematic layers of the possible conditional factors where final part is the validation of the results (Ercanoglu & Gokceoglu, 2004; Guzzetti et al, 1999; Liao et al, 2022). Keeping in view the landslides occurrences and their potential causative factors, 11 conditional factors were chosen for this study.…”

Section: Datasets Preparationmentioning

confidence: 99%

“…The basic steps for landslide susceptibility modelling are data collection and construction of thematic layers of the possible conditional factors where final part is the validation of the results (Ercanoglu & Gokceoglu, 2004;Guzzetti et al, 1999;Liao et al, 2022) SPI, TWI and distance from streams (Table 1). Freely available 12.5 Â 12.5 m ALOS PALSAR DEM was used to prepare these layers.…”

Section: Datasets Preparationmentioning

confidence: 99%

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

et al. 2023

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Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.

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“…Micro‐landforms represent the most minute units of landform morphology and exert an influence on slope stability. Topographic relief, on the other hand, denotes the maximum vertical variation within a specific area and serves as a measure of the broad‐scale indicators of regional terrain characteristics (Liao et al, 2022).…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning

Sun,

Wang,

Wen

et al. 2023

Earth Surf Processes Landf

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The aim of the present study was to assess differences in the conditioning factors and the performance of landslide susceptibility mapping (LSM), employing the SHapley Additive exPlanations (SHAP) model to gain profound insights into the intrinsic decision‐making mechanism of LSM in diverse landforms. Two typical karst erosion landforms were selected as the research areas. Based on 15 conditioning factors, LSMs for the two areas were developed using the Bayesian optimization random forest (RF) and eXtreme Gradient Boosting (XGBoost). The SHAP model was used to explore the landslide formation mechanisms from both global and local perspectives. The results show that the area under the curve (AUC) values of the XGBoost models were 0.791 and 0.761, and the AUC values of the RF models were 0.844 and 0.817, in the two different landform areas, respectively. The RF model's accuracy was higher than that of the XGBoost model in both regions. In the low‐elevation hills area, the primary three conditioning factors were identified as slope, topographic relief and distance from the river. Conversely, in the microrelief and mesorelief low mountain area, the predominant conditioning factors were elevation, distance from the river and distance from the road. Both karst landform areas exhibited a high sensitivity to the distance from the river, indicating its significant interaction with other factors contributing to landslide occurrences. Notably, the RF model demonstrated superior performance compared to the XGBoost model, rendering it a more suitable choice for conducting landslide susceptibility mapping research in karst erosion landform areas. In the present study, a comprehensive explanatory framework based on the RF‐SHAP model was proposed, which enables both global and local interpretation of landslides in various karst landscapes. Such an approach explores the intrinsic decision‐making mechanism of the model, enhancing the transparency and realism of landslide susceptibility prediction results.

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Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and Wuxi counties, China

Cited by 69 publications

References 87 publications

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning

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