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LSM methods: (a) Matrix; (b) Linear discriminant analysis; (c) Random forest; (d) Artificial neural network.
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Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one...
Citations
... However, informal occupation in hazard-prone areas, vulnerable to landslides exacerbated by previous mining, ensued [48]. Morphodynamically, AE's unstable zone encompassed two distinct landslide sectors: 'El Espino' to the north and 'La Carbonera' to the south, covering an approximate area of 110 ha ( Figure 1) [49]. ...
... However, informal occupation in hazard-prone areas, vulnerable to landslides exacerbated by previous mining, ensued [48]. Morphodynamically, AE's unstable zone encompassed two distinct landslide sectors: 'El Espino' to the north and 'La Carbonera' to the south, covering an approximate area of 110 ha (Figure 1) [49]. ...
This paper analyzes the spatiotemporal evolution of a complex landslide risk scenario in a Latin American megacity, underscoring the key challenges it poses for sustainable urban planning in such cities. This research draws upon multiple studies commissioned by the mayor’s office of the megacity of Bogota, Colombia, and utilizes aerial photographs and satellite imagery from diverse sensor types. The methodology used considered six spatiotemporal analysis scenarios: rural/natural, mining, urban, landslide risk, stabilization and environmental park, and informal reoccupation. The findings reveal a complex interplay between the megacity’s peripheral areas, which face constraints for human settlement, and their potential for construction material exploitation. This complex relationship was further compounded by weaknesses in planning and controlling peripheral occupations, coupled with a burgeoning demand for developable land in a landslide risk context (landslide area: 73 ha). The analysis scenarios highlighted the predominant use of a reactive urban planning approach that addressed events, changes, or problems after they had occurred, rather than proactively anticipating and preventing potential risks at the study site. The detected land-use transformations unveiled different historical moments, culminating in a landslide disaster (804 houses destroyed, 3000 families at risk). This catastrophe necessitated a radical and significant intervention, incurring substantial costs for the megacity administration (USD 26.05 million). This landslide was the largest recorded in the megacity and one of the most extensive in urban areas across Latin America.
... In mountainous regions, these terrain characteristics are especially important since they can trigger landslides. Landslides are more likely to occur in areas that are mountainous, have steep slopes, geologically unstable rocks, insufficient drainage, and sparse vegetation cover (Haeberli et al. 2017;Herrera-Coy et al. 2023;Baruah et al. 2023). Understanding these factors is important for figuring out how likely a landslide is in rocky areas and what can be done to stop it. ...
Landslides in the Nainital district of Uttarakhand, India, pose a significant threat to human communities and local ecosystems. This study aims to improve landslide susceptibility modeling by integrating advanced analytical techniques with deep learning, sensitivity analysis and explainable artificial intelligence (XAI). Our approach captures the complex interaction between natural terrain and human intervention and provides a novel framework for risk assessment and management. In this analysis, we performed a multicollinearity analysis to ensure the independence of predictor variables. We optimized deep learning models, including deep neural network (DNN), convolutional neural network (CNN) and a hybrid of CNN with long short-term memory (LSTM), using Bayesian techniques. This optimization achieved a high degree of precision in parameter tuning. In the study, multicollinearity analysis showed that no parameter exceeded the multicollinearity threshold of over 9. When evaluating accuracy, the CNN-LSTM model was found to be the most effective with an Area Under the Curve (AUC) of 0.96, while DNN and CNN also had high AUCs of 0.94 and 0.95, respectively. Spatially, the CNN model identified 16.28% of the total area as highly susceptible, while the hybrid CNN-LSTM model delineated 13.39%. Sobol’s sensitivity analysis emphasized critical factors such as slope, elevation and geology as well as the anthropogenic influence of distance to built-up (DTB). The SHAP analysis confirmed the importance of these factors. This integrated method offers an innovative way to understand the dynamics of landslides by combining natural and human factors and provides the basis for sustainable infrastructure planning in Nainital.
... While deterministic methods assess slope failures using the factor of safety at large scales [33] and require detailed information and parameters, statistical methods evaluate the relationship between landslides and causative factors to predict the occurrence probability through the use of GIS tools that reduce the subjectivity and biases in the process of weighting landslide causative factors. The widely used statistical methods are bivariate [34]- [36], multivariate [16], [37]- [40], and neural networks [41]- [43]. Logistic regression (LR) is the most widely used multivariate statistical analysis method [39], [44]- [48]. ...
Landslides triggered by rainfall are among the most frequent causes of natural disasters in mountainous terrains. However, landslide susceptibility assessments are often limited due to the scarcity of reliable observations. Due to this lack of data, especially in developing countries, remote sensing is used for landslide susceptibility analysis. This study presents the application of remote sensing data and a logistic regression model to assess landslide susceptibility in a basin on a remote terrain in the northern Colombian Andes, where a rainstorm on May 18th, 2015, triggered more than 40 landslides and an associated debris flow afterwards. The methodology applied is based on free access remote sensing tools, since the study area is considered a scarce-data zone. The results show that free remote sensing tools provide enough information to run a model as logistic regression and achieve a successful first approach to the landslide susceptibility map of complex terrains as the study area. This suggests that the proposed methodology could be implemented in several regions with similar characteristics based only on free access information.
... This study used secondary data processed in 2022. The data consisting of Digital Elevation Model (DEM) image data to obtain land slope maps (Herrera-Coy et al., 2023). The process were done using SRTM global digital elevation data at a nominal resolution of 30 m. 1"SRTM DEM it is currently the most widely used worldwide because of its tolerable vertical and horizontal quality and accuracy (Milevski, 2014). ...
A landslide is the movement of soil mass down a slope. Landslides can be influenced by some factors including rainfall, soil type, land slope, land cover, and human activities. Cilawu Sub district, Garut, West Java is one of the most frequently experienced landslide areas which cause severe losses. This mapping aims to provide information about the landslide susceptibility areas in Cilawu Sub-district, Garut, West Java by using a Geographic Information System (GIS). This study used Digital Elevation Model (DEM) image, Landsat image, rainfall, geology, and soil types data which were then mapped using ArcGIS software. The analysis process used the overlay method, scoring method, and weighting method. The final result was a landslide map with 4 susceptibility levels covering low susceptibility, medium susceptibility, high susceptibility, and very high susceptibility. Based on this analysis, Cilawu District was dominated by the following classes: high susceptibility with an area of 5470.07 Ha, medium susceptibility with an area of 1627.78 Ha, very high susceptibility with an area of 515.96 Ha, and low susceptibility with an area of 366.16 Ha.
