Low Susceptibility Zones Research Articles

Landslide susceptibility assessment for Uttarakhand, a Himalayan state of India, using multi-criteria decision making, bivariate, and machine learning models

BackgroundLandslides, among the most catastrophic natural hazards, result from natural and anthropogenic factors, causing substantial financial losses, infrastructural damage, fatalities, and environmental degradation. Uttarakhand, with its unique topographical and hydrological conditions, unplanned human settlements, and changing precipitation patterns, is highly susceptible to landslides.MethodsThis study evaluates landslide susceptibility for Uttarakhand, a Himalayan state in India, by employing bivariate analysis, multi-criteria decision-making, and advanced machine learning models, such as Random Forest and Extreme Gradient Boosting (XGBoost). A total of sixteen landslide influencing factors were used for performing landslide hazard susceptibility zonation, including the innovative use of geomorphons for detailed terrain analysis.ResultsApproximately 18.47% of the study area was classified as high to very high landslide susceptibility zones, and 21% was classified into the moderate susceptibility category. High to very high susceptibility zones were concentrated in the Uttarkashi, Chamoli, and Pithoragarh districts of the Lesser and Higher Himalayas, areas characterized by rangelands and high annual rainfall. Conversely, very low to low susceptibility zones were predominantly located in the Tarai-Bhabar and Sub-Himalayan districts, including Haridwar and Udham Singh Nagar. The Random Forest and XGBoost models demonstrated superior predictive performance.ConclusionsThe spatially explicit landslide susceptibility maps provide critical insights for urban planners, disaster management agencies, and environmentalists, aiding in developing effective strategies for landslide risk reduction and promoting sustainable development in Uttarakhand. This study exemplifies applying advanced analytical techniques to address landslide susceptibility and related soil erosion and water resource management challenges in Uttarakhand.

Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.

A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity

Landslides threaten human life, property, and vital infrastructure in most mountainous regions. As climate change intensifies extreme weather patterns, the landslide risk is likely to increase, resulting in challenges for disaster management, sustainability development, and community resilience. This study presents a comprehensive framework for assessing landslide risk, integrating advanced machine learning models with the Iyengar–Sudarshan method. Our case study is Son La province, the Northwest region of Vietnam, with data collected from 1771 historical landslide occurrences and fifteen influencing factors for developing landslide susceptibility maps using advanced ensemble machine learning models. The Iyengar–Sudarshan method was applied to determine the weights for landslide exposure, vulnerability, and adaptive capacity indicators. The resulting landslide risk map shows that the highest-risk districts in Son La province are located in the central and northeastern regions, including Mai Son, Phu Yen, Thuan Chau, Yen Chau, Song Ma, and Bac Yen. These districts experience high landslide hazards, exposure, and vulnerability, often affecting densely populated urban and village areas with vulnerable populations, such as young children, the elderly, and working-age women. In contrast, due to minimal exposure, Quynh Nhai and Muong La districts have lower landslide risks. Despite having high exposure and vulnerability, Son La City is situated in a low-susceptibility zone with high adaptive capacity, resulting in a low landslide risk for this region. The proposed framework provides a reference tool for mitigating risk and enhancing strategic decision making in areas susceptible to landslides while advancing our understanding of landslide dynamics and fostering community resilience and long-term disaster prevention.

Quantifying uncertainty in landslide susceptibility mapping due to sampling randomness

The quality of landslide and non-landslide samples plays a crucial role in landslide susceptibility maps (LSMs) generated using machine learning algorithms. However, uncertainties arising from the collection of non-landslide samples can significantly compromise the reliability of these maps. Current methods, such as buffer-controlled sampling (BCS), often fail to address this issue adequately. This study aims to fill that gap by employing Monte Carlo simulations combined with BCS to quantify the uncertainties associated with non-landslide sampling and improve the accuracy of LSMs. A novel framework is proposed by incorporating landslide susceptibility confidence maps (LSCMs) to address the inherent uncertainty in BCS-based LSMs. The framework evaluates inconsistencies in LSMs, showing that maps generated by the same model may differ in over 30 % of the area due to variations in selection of non-landslide samples. The proposed approach outperforms traditional methods by correctly classifying landslide-prone areas, particularly in low and very low susceptibility zones, while providing a more reliable quantification of uncertainty. These findings underscore the limitations of traditional LSM methods and demonstrate that LSCMs offer a more robust tool for landslide hazard assessment. The framework enhances the precision of susceptibility mapping and provides critical insights for better risk mitigation and disaster preparedness.

Landslide susceptibility mapping using frequency ratio and analytical hierarchy process method in Awabel Woreda, Ethiopia

A landslide is a serious geo-environmental problem that results in the death of life and the devastation of infrastructure, properties, and agricultural lands. This research aimed to identify landslide susceptibility zones in selected Kebels of Awabel Woreda, central Ethiopia. Frequency ratio (FR) and analytical hierarchy process (AHP) methods were used. 175 landslide inventory data collected from Google Earth and field data were collected for testing and training data sets. Using the analytical hierarchy process, all the thematic layers (stream distance, slope, aspect, rainfall, lineament density, elevation, lithology, soil, land use/land cover, and curvature) were reclassified and weighted based on their relative contribution to landslide occurrence with the help of experts’ knowledge. The results show that 11.85% and 20.52 % of the study fall under the very high and high susceptible zones, respectively, while the low susceptible zones cover 26.3% and 14.74% of the area. The landslide susceptibility zone identified by the frequency ratio model shows that (6.09%) and (16.9%) of the area covered very high and high susceptible zones, respectively, while 30.4% and 23.4% of the area covered low and very susceptible zones, respectively. The predicted landslide-susceptible areas were validated using existing landslide points with the help of the ROC tool in ArcGIS. Area under the curve (AUC) results were 84.5% for the AHP model and 73% for the frequency ratio model. The find of this study will provide an input for decision makers and land use planners in the future.

Freeze-thaw landslide susceptibility assessment and its future development on the seasonally frozen ground of the Qinghai-Tibet Plateau under warming-humidifying climate

Exploring freeze-thaw landslide susceptibility on the Qinghai-Tibet Plateau (QTP) under warming-humidifying climate is greatly important for preventing and mitigating the risks of landslide hazards on engineering facilities. This study proposed a random forest-based freeze-thaw landslide susceptibility assessment model, where annual rainfall, annual average air temperature (AAAT), slope gradient, normalized difference vegetation index (NDVI), elevation, lithology, and plan curvature were fully considered. Selecting a study area of 324 km2 on the seasonally frozen ground (SFG) of QTP with 1059 freeze-thaw landslides, the model accuracy was validated. Low, moderate, high, and very high susceptibility zones were precisely classified, which accounted for 27.0, 27.5, 28.3, and 17.2%, respectively. Furthermore, its future development was explored under warming, humidifying, and warming-humidifying climates. Results indicated that when the AAAT or annual rainfall increased by 1.16 °C or 20 mm, both high and very high susceptibility zones increased by 2.0 or 1.0%, respectively. When AAAT and annual rainfall simultaneously increased by 1.16 °C and 20 mm, a higher increase in the high and very high susceptibility zones of 2.8% occurred. It was noteworthy that climate warming transitioned low and moderate susceptibility zones into high and very high susceptibility zones. These areas where freeze-thaw landslide susceptibility changed featured the AAAT of 4.29–6.15 °C, annual rainfall of 528.9–552.3 mm, slope gradient of 16–25°, and elevation of 3750-3940 m. Compared to climate warming, the humidifying climate and warming-humidifying climate expanded moderate susceptibility zones, and areas where freeze-thaw landslide susceptibility changed featured the gentler slope gradients of 8–16°. This study can provide a better guidance for safe engineering constructions influenced by freeze-thaw landslides on the QTP.

Landslide Susceptibility Mapping Using Weight of Evidence Method in the Purwanegara District and Surrounding Areas, Banjarnegara Regency, Central Java Province, Indonesia

Abstract The research location is in the Purwanegara Subdistrict, Banjarnegara Regency, Province of Central Java. According to the Geological Agency of Indonesia, this location is a landslide-prone area, and many landslides occur yearly. Therefore, it needs a landslide susceptibility map is basic information for landslide mitigation in this area. The parameters used in this research are slope, lithology, distance from structural geology, distance from the river, land use, and rainfall. Using six parameters, the weight of evidence (WoE) method was used to develop a landslide susceptibility map. This research aims to make a zonation of landslide susceptibility in this area. The data were collected by field survey and visiting some related institutions. The landslide data used are 85 landslide events in the last three years. All data was then randomly divided into two groups: 70% as model data sets and 30% as validation data sets. The map of every parameter will be overlaid to create a landslide susceptibility zone. The Area Under Curve (AUC) method validated the final map. The result shows that landslide susceptibility zonation can be divided into four zones: very low susceptibility zone, low susceptibility zone, medium susceptibility zone, and high susceptibility zone. The very low susceptibility zone is mostly located in the north, the low susceptibility zone is mostly in the north and south, the moderate susceptibility zone is in the middle, and the high susceptibility zone is in the middle of the research’s location. The validation results of the success and prediction rates using the AUC method show an accuracy value of 0.86 and 0.82, respectively. According to the validation result, both can be classified as very good.

Landslide susceptibility mapping in East Ungaran, Indonesia: A comparative study using statistical methods

East Ungaran, is one of landslide prone areas in Semarang Regency, Indonesia. In addition to provide a more detail map of landslide susceptibility, the objective of this research was to compare performance of three widely used methods, which are the Weight of Evidence (WoE), Logistic Regression (LR) and combined Weight of Evidence (WoE) – Logistic Regression (LR), for landslide susceptibility mapping. Slope, elevation, lithology, land use, normalized difference vegetation index (NDVI), distance from lineament, distance from river, and distance from road were considered as landslide controlling parameters in the research area and were used as input variables in the landslide susceptibility zonation. The results showed that the slope, elevation, and distance from the road are significant parameters causing the landslides. The research area is divided into very low, low, moderate, and high landslide susceptibility zones. The WoE performs better than the LR, while the combined WoE-LR method performs the best among the three methods in predicting landslide susceptibility in this area. The landslide susceptibility map developed using the combined WoE-LR method is suggested to be used for landslide mitigation planning of this area.

Landslide susceptibility mapping using frequency ratio method along the Bhaderwah-Bani Road, Jammu and Kashmir, India

This study outlines a method for landslide susceptibility mapping utilizing remote sensing and geographic information systems (GIS). Since the terrain from Bhaderwah to Bani in J&K is prone to landslide threats, the study has been conducted there. The purpose of the current study is to determine and identify the significant terrain elements causing landslides. Nine contributing factors on landslides were taken into account throughout the analysis. Thematic data layers are created in the GIS domain based on local events. Topographic maps, satellite images, on-site observations and publicly available published maps are used to gather the landscape data. To determine the standardized scores of criteria expressing their factor of importance for a given decision problem in terms of thematic parameters, categories, and their normalized weights the Frequency ratio (FR) method is used to digitize these maps along with tabular data and to create a GIS database. A particular landslide susceptibility zonation (LSZ) map was created on a GIS platform by statistically combining weightages from several thematic maps. Using FR, the LSZ map was divided into five separate susceptibility zones. According to the study's finding, 8% of the area is in a very high susceptibility zone, with the remaining 12%, 15%, 23%, and 42% falling into high, moderate, low, and very low susceptibility zones, respectively. The outcomes of the current study might aid policy and decision makers in moving forward with regional development initiatives including land use planning.

GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.

Landslide Inventorization and Susceptibility Mapping in the SemiArid Kargil-Ladakh Region of Northwestern Himalaya

ABSTRACT Landslides often result in damage to property and loss of life in the Himalayan Region because of high seismicity and rugged terrain. To address this issue, the current study focused on developing a landslide inventory based susceptibility map for Kargil-Ladakh Region of North-Western Himalaya. The landslide susceptibility map for the region was generated with the help of Frequency Ratio (FR) Method, landslide inventory layer and eleven influencing factors such as; elevation, geomorphology, aspect, slope, plan curvature, lithology, rainfall, profile curvature, distance from road and lineaments, temperature, and land cover. The results reveal that approximately (50%) of the study-area lies under moderate to very high susceptibility zones. The low and very low susceptibility zones cover the remaining (50%) of the study-area. The findings highlight that several factors significantly contribute to landslide occurrences in the region, namely slope, road network, elevation, rainfall, and land cover. The northern part of the study area is particularly susceptible to landslides due to the presence of the national highway, a high concentration of settlements, and increased infrastructure development. Furthermore, areas surrounding local highways and community road networks show moderate landslide susceptibility. The results were validated with the help of the Receiver Operating Characteristic (ROC) curve, yielding a value of (82%) which is well within the acceptable limit. The results underscore the importance of developing site-specific landslide mitigation strategies to safeguard vulnerable communities in this strategically significant border region of India.

Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models.

Landslide susceptibility mapping is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility mapping (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility mapping and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study's findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

Use of Spatial Data in Mapping Landslide Prone Areas in Luhurjaya and Surrounding Areas, Lebak Regency, Banten Province

Landslides are a natural process that occurs in a natural landscape on earth which can cause various environmental damage and can cause loss of life. An increase in the risk of landslides can be caused by uncontrolled land conversion due to the increasing population so that land development is necessary. The research was conducted in Luhurjaya and surrounding areas, Cipanas District, Lebak Regency, Banten with an area of 9 x 9 km. The spatial integration method was used to map landslide-prone zones using a scoring and weighting method, using five parameters, namely slope, rainfall, lineament density, rock type, and NDVI. Those five parameters are then overlaid to form a landslide susceptibility map. As a result of analyzing the research area based on these five parameters, it was found that this area has five slope classes, one rainfall class, five lineament density classes, three rock type classes, and four NDVI classes. After all the datas are scored and weighed, it was found that this research area can be classified to three zones, namely low landslide susceptibility zone, medium landslide susceptibility zone, and high landslide susceptibility zone. From the results of this analysis, disaster mitigation should be needed that can affect the community in the Lebak Regency area, Banten Province, namely in the form of providing provisions in the form of knowledge to the community regarding disaster management which is useful for minimizing the risk of the consequences of landslides

Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers – the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models – the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CART-Bagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The resultsof this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques

Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km2) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.

INFLUENCE OF DIGITAL ELEVATION MODEL RESOLUTION ON MAPPING TERRITORY SUSCEPTIBILITY TO LANDSLIDE DEVELOPMENT

Link for citation: Van B. Duong, Fomenko I.K., Duc T. Ta, Trung K. Nguyen, Zerkal O.V., Gorobtsov D.N., Hong D. Vu. Influence of digital elevation model resolution on mapping territory susceptibility to landslide development. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 8, рр. 164-181. In Rus. The relevance. Landslides are one of the most hazardous natural disasters in the world, causing significant economic damage and human deaths. For 20 years (1995–2014), 3876 landslides killed 163658 people and injured 11689 people, as reported by the U. Haque study (2019), based on the data from 128 countries. Excluding events triggered by earthquakes, the total number of fatal landslides from 2004 to 2016 was 4862, and most landslides (75 %) occurred in Asia. In Vietnam, particularly in the northern mountainous regions, landslides frequently caused considerable losses of life and property. The systematic evaluation of landslide hazards is a crucial component of Vietnam's disaster prevention strategy. The main aim of this study is to assess the landslide susceptibility of the Batxat district, Laocai province (Vietnam), using four digital elevation model resolutions (10, 30, 40, and 60 m) and nine landslide causative factors: slope aspect, elevation, slope, distance to roads, distance to faults, distance to drainage, average monthly precipitation, land use, and weathering crust. Object of the study is the landslide susceptibility in Batxat district, Laocai province, Vietnam. Methods: Certainty Factor method, fractal method. Results. The performed analyses indicated a statistically significant relationship between the distribution of landslides and the landslide causative factors in the study area. As a result, the territory was divided into five zones according to its susceptibility to the landslide process: very low, low, moderate, high, and very high. The very low landslide susceptibility zone is less than 10 % of the study area, while the very high susceptibility zone varies from 14,95 to 18,32 %, depending on the digital elevation model spatial resolution. Analysis of the Receiver Operating Characteristics curve revealed that all models, independent of digital elevation model resolution, have good prediction efficiency, with the area under the receiver operating characteristic curve over 70 %. In addition, the Receiver operating characteristic and fractal analysis results indicated that the model with a digital elevation model spatial resolution of 60 m has the highest accuracy. This significant conclusion may be explained by the fact that the accuracy of the landslide susceptibility assessment result is dependent not only on the digital elevation model resolution but also on the ratio of the digital elevation model resolution and the average area of landslides in the study area. This conclusion proves that it is necessary to depict landslide locations and their detailed morphological characteristics on the landslide inventory maps.

Artificial neural network for flood susceptibility mapping in Bangladesh

The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.