Implementing Landslide Susceptibility Map at Watershed Scale of Lompobattang Mountain South Sulawesi, Indonesia

This study attempts to predict future landslide occurrence at watershed scale and calculate the potency of landslide for each sub-watershed at Lompobatang Mountain. In order to produce landslide susceptibility map (LSM) using the statistical model on the watershed scale, we identified the landslide with landslide inventories that occurred in the past, and predict the prospective future landslide occurrence by correlating it with landslide causal factors. In this study, six parameters were used namely, distance from fault, slope, aspect, curvature, distance from river and land use. This research proposed the weight of evidence (WoE) model to produce a landslide susceptibility map. Success and predictive rate were also used to evaluate the accuracy by using Area under curve (AUC) of Receiver operating characteristic (ROC). The result is useful for land use planner and decision makers, in order to devise a strategy for disaster mitigation

Comparative Evaluation of Various Statistical Models and Its Accuracy for Landslide Risk Mapping: A Case Study on Part of Himalayan Region, India

Among other natural hazards, Landslides are the most prominent and frequently occurring natural disaster in the state of Himachal Pradesh with higher socio-economical losses. About 0.42 million sq.kms of area are prone to landslide activities in our country that is excluding the snow covered areas. The current research focuses on estimating the landslide risk zones of the Shimla Tehsil, Himachal Pradesh using various statistical models. Landslide contributing factors as such Landuse Landcover, Elevation, Slope, Lithology, Soil, Geology and Geomorphology has been used to assess the Landslide risk factors. Data obtained from LANDSAT 8 OLI sensors, SRTM DEM, Soil and Land Use Survey of India and SOI Toposheets have been used as sources. Weighted Overlay, Fuzzy logic and Analytical Hierarchical Process models will be used to categorize the Vulnerability and risk Zones of the study area. The causative factors were analyzed and processed in GIS environment. These values will be then being integrated using various studied models to produce individual landslide vulnerability and risk zones. The results reveal that most of the study area falls under Very Low risk category with a total coverage of 67.34%. Low and Moderate area covers about 23% and 9.13% of the study area. Higher risk areas only account for about 0.46%. Higher percent of the study area is mostly covered by settlements. National highways, Metal roads, Slopes and Denser settlements are located along the Moderate and low risk areas. The results retrieved from the WOM model reveals a total of 55% of the area comes under very low category. Low and Moderate category covers about 31.4% and 10.6% of the study area. High and Very High category cover a total of 1.9% together

Debris flow susceptibility mapping using the Rock Engineering System (RES) method: a case study

The main purpose of the present study is to develop a debris flow susceptibility map of a mountain area (Susa Valley, Western Italian Alps) by using an upgraded version of the Bonetto et al. (Journal of Mountain Science 18, 2021) approach based on the Rock Engineering System (RES) method. In particular, the area under investigation was discretized in a 5 × 5-m grid on which GIS based analyses were performed. Starting from available databases, several geological, geo-structural, morphological and hydrographical predisposing parameters were identified and codified into two interaction matrices (one for outcropping lithologies and one for Quaternary deposits), to evaluate their mutual interactions and their weight in the susceptibility estimation. The result for each grid point is the debris flow propensity index (DfPI), an index that estimates the susceptibility of the cell to be a potential debris flow source. The debris flow susceptibility map obtained was compared with those obtained from two expedited and universally recognized susceptibility methods, i.e. the Regional Qualitative Heuristic Susceptibility Mapping (RQHSM) and the Likelihood Ratio (LR). Each map was validated by using the Prediction Rate Curve method. The limitations and strong points of the approaches analysed are discussed, with a focus on the innovativeness and uniqueness of the RES. In fact, in the study site, the RES method was the most efficient for the detection of potential source areas. These results prove its robustness, cost-effectiveness and speed of application in the identification and mapping of sectors capable of triggering debris flow