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Flow diagram showing different steps involved in the preparation of landslide susceptibility mapping.
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Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topo...
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Citations
... They are sporadic and simultaneous natural hazards in mountainous regions, leading to altered landscapes and significant losses (De, 2017;Dikshit et al., 2020). One of the primary factors behind landslides is the gravitational force, causing slopes to shift from stable to unstable condition (Chawla et al., 2018). Landslides pose significant hydrogeological and man-made elements, posing risks to human settlements, transportation networks, civil infrastructures, and natural resources such as agricultural land and forests (Nad, 2015). ...
Landslides are recurrent natural events that pose significant risks to human settlements and infrastructure, particularly in craggy Himalayan terrains like the Darjeeling Hills. This study aims to examine landslide events in the region, analyze their occurrence, underlying factors, and implications for the local community. Various datasets, including landslide point data, geology maps, road networks, rainfall data, slope maps, and physiography maps, were utilized for spatial analysis of landslides. The findings reveal that landslide occurrences are concentrated in the high and middle altitudinal hills, particularly in areas with extremely steep slopes. The high precipitation and weak geology of the region contribute to the vulnerability of these areas to landslides. Rainfall, road density, slope, and geology were utilized for landslide susceptibility mapping using weighted overlay method in GIS environment. The landslide susceptibility zones were divided into four classes i.e., highly unstable, moderately unstable, moderately stable and stable that cover 1%, 45%, 46% and 7% of the total study area (1265.1 km 2). The findings of the study can be used for mitigation of landslides and land use planning.
... Many studies have investigated various approaches and areas, providing a foundation for improving LS research in the relatively underrepresented Garo Hills region. Chawla et al. (2018) led a study in the Darjeeling Himalayas, identifying high-risk zones. The research highlights the unfeasibility of developing these zones or implementing immediate remedial measures to mitigate landslide risks. ...
Creating accurate and effective Landslide Susceptibility (LS) maps can aid disaster prevention and mitigation efforts and provide sufficient public safety. The primary aim of this study is to develop an LS map for the Garo Hills region in Meghalaya, India, using the weight of evidence (WoE), frequency ratio (FR), and Shannon entropy (SE) methods. A comprehensive landslide inventory catalogued 98 events from 2000 to 2023 for the analysis, and nine key geographical and environmental parameters were prepared. Conducted multicollinearity and correlation analysis to identify and mitigate collinearity issues between factors. The model's performance was analysed through the area under the curve (AUC) value of receiver operating characteristic (ROC) curves and three recent landslides. The results showed that FR method achieved the highest accuracy, with successive rate curve (SRC) AUC and predictive rate curve (PRC) AUC values of 0.860 and 0.940, respectively, and classified susceptibility at three sites as high, moderate, and low. The WoE method effectively identified three landslides site in high and very high susceptibility zones, achieving SRC AUC and PRC AUC values of 0.844 and 0.915, respectively. The SE method showed robust performance in predicting landslide-prone areas, with PRC AUC comparable to other methods (0.913), though its SRC AUC (0.771) was lower. Developed maps revealed that high and very high susceptibility zones account for approximately 10% and 3% of the study area, predominantly near roads, steep slopes, and higher elevations. The information in this study is valuable for civilians and the government authorities involved in hazard monitoring and management.
... But the area suffers from a number of landslides every year that seriously affect locals as well as tourists, upsetting social and economic life in the area (Saha et al. 2023a). The landslides are largely due to the region's favorable terrain conditions, exacerbated by heavy rainfall, human activities, and occasional earthquakes (Sarkar et al. 2013;Pal et al. 2016;Chawla et al. 2018). This susceptibility to slope failure seriously threatens the region's stability, particularly affecting railway services (Samanta and Majumdar 2020). ...
Landslide susceptibility mapping is crucial for reducing risks in culturally and historically significant areas like the Darjeeling Toy Train route, a UNESCO World Heritage site. In this study, the risk of landslides along this road is evaluated using Geographic Information System (GIS) tools and advanced machine learning models, such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Logistic Regression, and Classification and Regression Trees (CART). It uses a set of 512 landslide and non-landslide sites, with a 70:30 split between training and testing. Within the research area, thirteen topographical, hydrological, and geological factors linked to landslides are shown as GIS layers to make maps of landslide susceptibility (LSM). The study area particularly vulnerable to various types of landslides, including debris slides, rock falls, and soil slips. ROC–AUC results show that the SVM model did the best (0.813), followed by GBM (0.807), Logistic Regression (0.797), and CART (0.781). SVM had the highest accuracy rate at 83.2%, followed by GBM at 81.5% and LR at 80.3%. CART had the lowest overall accuracy rate at 78.6%. Furthermore, confusion matrix analysis showed that SVM and Logistic Regression were better at finding actual landslide-prone areas, with 84.6% and 82.1% recall rates, respectively. This made them more accurate in predicting high-risk areas. Susceptibility levels were categorized, revealing high-risk areas like Darjeeling and Rishihat and safer areas like Kurseong and Mohanbari. For lowering the risk of landslides and protecting this historic route, these results are very useful for land management and disaster preparation.
... Despite the frequent occurrence of landslides in the Kalimpong district, a comprehensive assessment of landslide susceptibility using multi-sensor datasets and advanced modelling techniques is lacking. Most of the existing research in this region has employed conventional qualitative methods (Sarkar and Kanungo 2004;Chawla et al. 2018Chawla et al. , 2019Saha et al. 2023aSaha et al. , 2023b to delineate landslide susceptibility zones by assigning weights to each conditioning factor. However, the process of assigning these weights is inherently subjective. ...
This study delineates landslide susceptibility zones in the Kalimpong district by integrating multi-sensor datasets and assessing the effectiveness of statistical and machine learning models for precision mapping. The analysis utilises a comprehensive
geospatial dataset, including remote sensing imagery, topographical, geological, and climatic factors. Four models were employed to generate landslide susceptibility maps (LSMs) using 16 influencing factors: two bivariate statistical models, frequency
ratio (FR) and evidence belief function (EBF) and two machine learning models, random forest (RF) and support vector machine (SVM). Out of 1244 recorded landslide events, 871 events (70%) were used for training the models, and 373 events (30%)
for validation. The distribution of susceptibility classes predicted by The RF and SVM models produced similar susceptibility
distributions, predicting 13.30% and 14.30% of the area as highly susceptible, and 2.42% and 2.82% as very highly susceptible,
respectively. In contrast, the FR model estimated 20.98% of the area as highly susceptible and 4.30% as very highly susceptible,
whereas the EBF model predicted 17.42% and 5.89% for these categories, respectively. Model validation using receiver operating
characteristic (ROC) curves revealed that the machine learning models (RF and SVM) had superior prediction accuracy with
AUC values of 95.90% and 86.60%, respectively, compared to the statistical models (FR and EBF), which achieved AUC values
of 74.30% and 76.80%. The findings indicate that Kalimpong-I is most vulnerable, with 6.76% of its area categorised as very high
susceptibility and 24.80% as high susceptibility. Conversely, the Gorubathan block exhibited the least susceptible, with 0.95%
and 6.48% of its area classified as very high and high susceptibility, respectively. This research provides essential insights for
decision-makers and policy planners in landslide-prone regions and can be instrumental in developing early warning systems,
which are vital for enhancing community safety through timely evacuations and preparedness measures.
... Roy et al. 2023;Poddar and Roy 2024,), Logistic Regression (LR) (Basu and Pal 2017), Support Vector Machine (SVM) (J. , Generalized Linear Model (GLM) (Saha et al. 2022), Random Forest (RF) (Saha et al. 2022), Convolutional Neural Network (CNN) (Saha et al. 2022;Moghimi et al. 2024) and Genetic Programming (GP) (Chawla et al. 2018). Soft-computing based ensembles with bi-variate statistical methods were also proposed by S. Das et al. (2024). ...
In the field of landslide susceptibility, the utilization of data driven methodologies has seen a significant breakthrough. However, the performance of the models depends on the geo-environmental factors, and the selection of factors vary from one location to another, and this leads to a persistent lacuna for the present exploration. This study was aimed to assess landslide susceptibility for Darjeeling hills in Eastern Himalayan region with sixteen causative geo-environmental factors. The selection of causal factors was performed through a two-stage procedure, namely Pearson’s correlation coefficient (PCC) and Boruta algorithm (PCC-BA). The dataset associated with the research was split randomly into 70:30 ratio for train and test data. In addition, 30% of the training data was taken as validation dataset. Four advanced data-driven models namely K-nearest neighbour (KNN), Boosted Tree (BT), Gradient Boosting Machines (GBM) and ensembled Neural Network with Principal Component Analysis (PCA-NN) were taken up and four advanced novel ensembles namely KNN-BT, PCA-NN-BT, GBM-KNN and GBM-PCA-NN were constructed. The susceptibility maps were grouped into five divisions, viz., very low (VL), low (L), medium (M), high (H), and very high (VH) susceptibility. Through area under receiver operation characteristics curve, the accomplishment of constructed susceptibility models was substantiated with training, testing and validation dataset, where KNN-BT attained 0.943, 0.889 and 0.944 respectively, PCA-NN-BT attained 0.934, 0.876 and 0.943 respectively; GBM-KNN attained 0.959, 0.897 and 0.957 respectively; and GBM-PCA-NN attained 0.956, 0.889 and 0.962 respectively. The researchers have utilized an extensive explainable artificial intelligence (ex-AI) method, partial dependence profile (PDP) to quantify the effect of causal factors on all the four ensembled models. The study was aimed to demonstrate a significant capacity to substantially optimize disaster mitigation policies with a constituent endeavour to bridge the chasm between contemporary machine learning approaches and geo-spatial applications, and thereby paving the way to enhance the resilience of inhabitants in landslide prone areas of hilly portion of Darjeeling district.
... Landslides are the result of a combination of intrinsic conditions and external env ronmental factors. However, no standardized guidelines or criteria exist for determinin the influencing factors in landslide susceptibility studies [22][23][24][25]. Based on the findings [21,26], the influencing factors were categorized into four groups: morphological, ge logical, hydrological, and triggering factors. ...
... Landslides are the result of a combination of intrinsic conditions and external environmental factors. However, no standardized guidelines or criteria exist for determining the influencing factors in landslide susceptibility studies [22][23][24][25]. Based on the findings of [21,26], the influencing factors were categorized into four groups: morphological, geological, hydrological, and triggering factors. ...
Landslides are common geological hazards worldwide, posing significant threats to both the environment and human lives. The preparation of a landslides susceptibility map is a major method to address the challenge related to sustainability. The study area, Nyingchi, is located in the southeastern region of the Qinghai-Tibet plateau, characterized by diverse terrain and complex geological formations. In this study, CNN was used to extract high-order features from the influencing factors, while BiLSTM was utilized to mine the historical data. Additionally, the attention mechanism was added to adjust the model weights dynamically. We constructed a hybrid CNN–BiLSTM-AM model to assess landslide susceptibility. A spatial database of 949 landslides was established using remote sensing images and field surveys. The effects of various feature selection methods were analyzed, and model performance was compared to that of six advanced models. The results show that the proposed model achieved a high prediction accuracy of 90.12% and exhibits strong generalization capabilities over large areas. It should be noted, however, that the influence of feature selection methods on model performance remains uncertain under complex conditions and is affected by multiple mechanisms.
... In particular, Darjeeling, a high altitudinal temperate, subtropical climatic region nestled in the Eastern Himalayan region, has been grappling with worsening air quality over the last few decades [25]. Darjeeling has become a popular tourist hub, mainly during summer [26], as the hills are prone to landslides due to heavy rainfall in the monsoon [27], and experience cold temperatures during winter [28]. The surge in tourism has also brought a plethora of vehicles, contributing significantly to pollution levels [25]. ...
The present study focuses on the elemental characterization and contribution of prominent sources of particulate matter (PM) in Darjeeling, the high-altitudinal eastern Himalayan station. The concentration of PM10 and PM2.5 was exceeded the National Ambient Air Quality Standards (NAAQS) for 72% and 83% of the sampling days, respectively. Since the World Health Organization or other government organizations has not set any standards for PM1, the standards of PM10 and PM2.5 were considered as benchmarks. The concentration of PM1 exceeded the NAAQS for PM10 and PM2.5 on 57% and 85% of the days, respectively. The elemental characterization using wavelength dispersive X-ray fluorescence (WD-XRF) technique identified 21 elements with the dominance of Si, Na, B, Ba, Al, and K in PM10; while, Al, N, and B in PM2.5 and PM1. Principal component analysis depicted that biomass burning, fossil fuel combustion, crustal/soil dust, and industrial emissions were identified as primary contributors to PM10; PM2.5 was substantially attributed to industrial emissions, agricultural activities, biomass burning, vehicular activities and natural sources. Additionally, natural sources and anthropogenic activities like vehicular, agricultural, and industrial emissions, and combustion were identified as the major sources of PM1 in Darjeeling. The findings of this study could potentially raise awareness among researchers and policymakers, prompting them to develop sustainable strategies in hill regions across the globe.
... Darjeeling and Sikkim Himalaya cover above forty percent (40%) of landslide-prone areas in India (Geological Survey of India). Many researchers like Kanungo et al. (2008), Ghosh et al. (2011), Chawla et al. (2018), Mandal and Mondal (2019a, 2019b, 2019c, 2019d, Das and Lepcha (2019), Roy et al. (2019), Teja et al. (2019), Abraham et al. (2020), Dikshit et al. (2020) studied landslides in Darjeeling Himalaya and attempt to determine the LCFs and suggest some mitigation procedure for risk reduction. In Darjeeling, the dimension of landslides is mostly structurally controlled and increases over periods that results in substantial damage to infrastructural development. ...
Landslides are the utmost destructive natural along with man-induced hazards/disasters causing extensive damage to properties and losses of lives in the mountainous regions on the earth. Therefore, mapping and identification of landslide susceptible zones (LSZs) and risk-prone areas are essential for planning and management. The core focus is to explore LSZs and elements at risk in the Lish River basin of Darjeeling Himalaya, India applying seven bivariate statistical models based on geospatial techniques. 188 landslide polygons were recognized to locate landslides using the Global Navigation Satellite System. Twenty-one geomorphological, hydrological, lithological, and environmental (triggering, protective, and anthropogenic) landslide causative factors and two elements at risk (settlements and roads) were selected to prepare LSZ maps. The receiver operating characteristics (ROC) curve and frequency ratio (FR) values were used to validate the models. The ROC curve indicated that the prediction truth of the LSZ maps is 80.20% for frequency ratio (FR), 81.40% for modified information value (MIV), 71.30% for landslide nominal risk factor, 77.30% for statistical index (SI), 77.40% for relative effect (RE), 79.80% for fuzzy membership (FM) using FR, and 76.60% for FM using cosine amplitude approaches respectively. Among all models, the MIV model showed the highest accuracy and this model was considered for risk assessment and prediction. The FR values of six LSZs rise from very low to very high LSZs which indicates a positive correlation between LSZs and FR values. The findings also showed that 7.662% and 0.124% of settlement areas were identified under high and very high LSZ and 12.085% and 0.267% of roads falling into high and very high LSZ as per the MIV model. The present study will help policymakers, and environmental engineers for the implication of effective strategies to ensure better ecological and environmental management in the hill region.
... One of the map types commonly used to identify potential landslide areas is the landslide susceptibility map (LSM) [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. A LSM is a type of map that generally defines the relative susceptibility of areas within a region to landslide hazards [23][24][25][26]. ...
Keywords Abstract Frequency ratio Modified information value Landslide susceptibility map Forest roads Costpath analysis Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems. Research Article
... Landslides are a significant natural hazard that can have devastating impacts on human communities, infrastructure, and the environment (Chawla et al. 2018;Abraham et al. 2020;Dikshit et al. 2020;Ali et al. 2021;Alsabhan et al. 2022). Understanding landslide vulnerability is crucial for effective risk assessment, mitigation, and disaster management. ...
The climate system of the earth is definitely warming. That environment changes influence the steadiness of regular and designed slants and have results on avalanches, is likewise undisputable. Less clear is the sort, degree, greatness and heading of the progressions in the security conditions, and on the area, overflow, movement and recurrence of avalanches because of the projected climate change. The present study delves into the assessment of climo-geomorphic role on the occurrences of landslides through the investigation of ecosystem service evaluation by employing remote sensing technology. Environment and avalanches act at just somewhat covering spatial and transient scales, confusing the assessment of the environment influences on avalanches. We found a skewed distribution in the geographical spreading of the landslide-climate studies that have been published, with significant portions of the globe not covered. The present study has identified the change in temperature and precipitation through the climatic based analysis and indices computation. The present study utilizes a probabilistic avalanche danger model to evaluate provincial avalanche alterations. The study advocates for developing outfits of projections in light of a scope of discharges situations, and to utilize cautiously results from most pessimistic scenario situations that may over/under-gauge avalanche dangers and hazard. Additionally, we have computed frequency ratio model (FRM) for landslide susceptibility zonation. The study concludes with the finding of regulating services in the steep terrain to nullify the impact of landslides.
