Landslide: Susceptibility, Risk Assessment and Sustainability

Landslides are among the most dangerous geological hazards of the world as they involve the movement of earth materials under the influence of gravity. Landslides can occur as a result of many different disturbances, but they are also strongly influenced by certain environmental factors such geology, precipitation, land cover changes and population. This Chapter sought to explore the use geostatistical and geospatial models to assess the contributions of the environmental factors to landslide risks and vulnerabilities in Ghana. Through the use of the weighted overlay method, it was found that even though precipitation was assigned the highest weight of 35%, areas which were identified as high landslide risk areas rather experienced the lowest levels of precipitation in the country. Thus, the high landslide risk was increased due to the geological setting which made the location much prone after any little amount of precipitation. The level of vulnerabilities is also on the high side due to increasing population and the resulting improper land use planning. Based on these, it was concluded that even though landslides could exert a significant multidimensional impact on a region’s life, livelihoods and geomorphology whiles their occurrence could not be predicted, the use of geostatistical and geospatial modelling technologies could help integrate available datasets to be able to ascertain the individual contribution of such factors to the level of risks and vulnerabilities.

Hofu City in the southern part of Honshu Island, Japan, was hit by a severe landslide on July 21, 2009. This chapter aims to determine the sites where the landslides are most likely to happen by creating a susceptibility map within the case study sites. We used remote sensing and geographic information systems (GIS) datasets that include ALOS AVNIR-2 satellite imagery, the digital elevation models (DEM), geology records, the local rain gauge data, and geographical representation of the history of landslides. Seven parameters, including land cover, elevation, slope, aspect, geology, and boundary extraction, were integrated using logistic regression with an isohyet map (i.e., from the rainfall dataset) to model a landslide susceptibility map (LSM). Following the research's findings, landslides were more likely to occur at elevations between 50 and 350 m, slope angles between 5 and 50 degrees, slope directions northeast and north, all of the land cover types and lithological types of granodiorite, fan deposits, and middle terrace. Among those statics' parameters, elevation, land cover, and slope were the most significant in determining the landslide susceptibility model. Further, almost half of Hofu City was categorized as high and very high susceptible areas. Moreover, out of the 928 inventory landslide dataset, 916 were placed at the very high susceptibility areas of the LSM.

Recent technological strides have ushered in a transformative era for disaster assessment, monitoring, mitigation, and management. Amid this progress, Geographic Information System (GIS) has emerged as a pivotal tool, assuming a pivotal role in monitoring landslide hazards, risks, and vulnerabilities. Through the fusion of diverse spatial data sources—encompassing topography, land cover, rainfall, geology, and infrastructure details—GIS enables comprehensive data integration and interpretation. This integration bears multifaceted applications, spanning Landslide Hazard and Risk Assessment, Vulnerability Monitoring and mapping, Real-time Information Collection, Early Warning Systems, Decision Support, Management, and Community Awareness. With GIS as a cornerstone, a wealth of benefits unfurl—from bolstering landslide hazard, risk, and vulnerability monitoring to seamlessly incorporating this intelligence into land-use planning and infrastructure development. Particularly significant in Himalayan regions, GIS empowers stakeholders with actionable insights, guiding judicious choices that foster resilience and safety. This chapter provides an introduction to landslide risk and vulnerability and also navigates through the need and methodologies for the preparation of an inventory of historical landslide records, provides an understanding of various factors, their importance, and methodologies of evaluation of these factors as driving factors for the occurrence of landslide. This chapter also provides insight into statistical and machine learning technologies that have been used for landslide susceptibility mapping, provides knowledge about implications and prospective avenues of GIS technologies primed for landslide monitoring, and elucidates the prospective applicability of landslide hazard risk and vulnerability monitoring.

Landslides are a prevalent geological hazard in many regions globally and have a profound effect on the resources and social fabric of the affected community. Identification of landslide-prone zones is mandatory for developing land use plans in regions affected by landslides. Landslide susceptibility maps are crucial for mapping landslide hazards and evaluating the risk resulting from landslides. Zones susceptible to the geologic phenomenon of landslides are identified and mapped using various methods and models. Off-late the increase in frequency of the landslide hazards in many hill and mountain regions around the globe mandates landslide susceptibility mapping. The choice of the method depends on the factors and data available. Common methods include deterministic modelling for specific slopes and statistical & heuristic models for mapping landslide susceptibility at regional scale. Statistical and probabilistic methods like frequency ratio, conditional probability, certainty factor and logistic regression, etc. are popularly used for mapping susceptibility. Off-late machine learning and deep mining techniques are also widely adopted for assessing susceptibility. Heuristic models like analytical hierarchical and analytical network process are also common. The choice of both the model to map susceptibility and factors used to assess the susceptibility of the region are dictated by factors like the local geo-environmental set-up, quality of data used and its reliability. This chapter explores the suitability of various susceptibility models, their merits and limitations in different geo-environmental scenarios. Also, a detailed review on the various topographic, hydrologic, geological, geotechnical, environmental and anthropogenic factors is presented arguing their effectiveness in specific geologic and environmental regional set-up.

The chapter aims to map the susceptibility of landslides in the Rangamati district of the Chattogram Hill Tracts region in Bangladesh using state-of-the-art methods and geospatial techniques. The district has experienced a significant number of landslides that caused numerous fatalities. However, the lack of available data and proper synchronization of existing information impedes our understanding of the extent of the issue. An integrated approach combining community-based mapping, Participatory Rural Appraisal (PRA) techniques, and spatial analysis was employed to address this challenge. Generation of landslide inventory revealed 306 landslide locations spread across the entire district, with 54.58% located in Rangamati Sadar upazila. Additionally, spatial statistical analysis was conducted to find the association of landslides with 12 causative factors. Analysis revealed that areas with Boka Bill geological formation had 37.91% landslide locations, making this region highly susceptible. Similarly, areas with elevations within 50–200 m and slopes of 10°–20° have recorded the highest number of landslides in the study area, with 40.85% and 50.33% of landslide locations, respectively. The study's findings also indicated that areas close to roads (0–250) m and settlements (0–25) m had the highest number of landslide locations, 61.76%, and 34.64%, respectively. Overall, findings indicated that around 43.58 km² area of Rangamati district are susceptible to landslides. This chapter offers a comprehensive understanding of landslide susceptibility using an integrated geospatial approach. The findings will lay the foundation for developing a landslide vulnerability model to enhance landslide risk assessment and facilitate effective mitigation measures in the future.

Since the 1970s, the scientific community has dedicated significant efforts to the development of landslide susceptibility models through various approaches, with the current spotlight firmly on artificial intelligence techniques. Despite their unique advantages, these cutting-edge tools have introduced significant challenges, the solution of which hinges on critical user decisions. These decisions chiefly revolve around selecting landslide conditioning factors and designing the optimal configuration of internal mechanisms of susceptibility modeling approaches—both critical determinants influencing model predictive accuracy. To address origin of these issues, a systematic review of literature spanning seven years, from 2015 to 2021, was conducted. The results revealed the utilization of 151 various landslide conditioning factors, highlighting a clear dearth of consensus on the selection of geospatial covariates in the literature. Nonetheless, only about one-third of the reviewed articles considered the feature selection techniques to seek the optimal factor subset. The review also showed that 54 distinct machine learning algorithms were used, with logistic regression being the most commonly applied susceptibility modeling approach, featured in 70 articles. Notably, deep learning algorithms were marginally employed, appearing in a mere 7.08% of the reviewed articles since 2018. However, a significant proportion (64.32%) of the articles used non-optimized predictive models with default settings, while a trial-and-error approach was adopted in 10.81% of the reviewed literature. Beyond the comprehensive literature review, this chapter delves into a series of ill-explored open questions and reveals opportunities that can serve as potential research roadmaps, potentially guiding the trajectory of future studies in landslide susceptibility mapping.

Landslide is a common phenomenon in the hilly region. The Himalayan region the one of the best examples of this type of landslide-prone region. Kurseong subdivision is the study area of this chapter. Landslide is a common hazard that creates an interruption in the normal human life. With the help of Susceptibility mapping, the landslide zones can easily identified. Analytical hierarchical process (AHP) and frequency ratio model (FR) are used to prepare the susceptibility map. About 77 past landslide inventories are traced for the study. Eighteen (18) inventories from the NASA Global landslide points, six (6) inventories from the field survey (2023) and fifty-three (53) inventories from Google Earth Pro (2010–2023) in Mirik and Kurseong. Twenty (20) landslide conditioning factors are taken into account for landslide susceptibility mapping, viz., slope, elevation, aspect, curvature, relative relief, Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), Topographic Ruggedness Index (TRI), drainage density and land-use and land-cover etc. Among the two models, the Analytical Hierarchical Process (AHP) has the highest accuracy (62.27%) as per ROC-AUC. From the predicted maps, it has been traced that Kurseong is experiencing high and very high landslide-prone areas. The landscape susceptibility map helps to figure out the prime governing factors behind a landslide in the study area. In addition, the said chapter addresses the sustainable mitigation process of the landslide in Kurseong that will contribute to further sustainable development in the area.

Natural disasters are can take place anytime and anywhere. When a natural disaster strikes, it lays a severe effect on the natural and social environment. Sometimes the intensity of damage goes to such an extent that it becomes almost difficult for man to cope with the situation and come out of the losses. Hence, it is very much necessary to adopt proper mitigation strategies so that the severity of the disaster can be reduced and colossal loss to life and property can be averted. The present study makes an attempt to perform a comparative study of landslide susceptibility of Uttarkashi district of Uttarakhand prepared using weighted overlay technique and multi-criteria decision analysis technique by applying GIS and Remote Sensing tools and also tries to point out the suitable model out of the two. The study results revealed five landslide susceptibility zones and also found that the both the models were ‘Good’. However, AUC value of success rate curve of model prepared using weighted overlay (79.7%) is greater than that of multi-criteria decision analysis (78.9%) and thus is considered to more applicable for the future scenario. The study has also delved into the assessment of outrageous values of landslide events through the computation of stand error. The standard error values are widely scattered giving a result of non-homogenous distribution of the landslide points. The multi-spectral behavior also indicate the identical result as majority of the landslide events are detected near the higher elevated areas.

India has witnessed humongous growth in the frequency of landslides in the past 40–50 years. The process of urbanization, concretization, and climate change has imposed various challenges for Indian cities; out of which landslide is one. Landslides inflict substantial damage to human lives, infrastructure, and the environment. The book chapter tries to identify the relevance of land use and land cover as major determinants for triggering natural disasters; however, this book chapter identifies the role of geotechnical characteristics in landslide susceptibility. The next section of the book chapter addresses various cases from Indian cities: Shimla, Himachal Pradesh deciphers the phenomenon of landslides majorly caused by soil erosion; the classical case of Western Ghats in India that is prone to repeated landslides. Moreover, the case of Jharkhand has been triggered in the last few years due to mining activities. Lastly, the chapter embarks on mitigation by the adoption of resilient strategies for landslide susceptibility and envisions the approach for reducing landslides in the near future.

Moisture-driven landslides (MDL), mainly caused by rain, seriously threaten lives and property, resulting in catastrophic damages and considerable economic losses. In areas with steep topography, the temporal and spatial clustering of long-duration, moderate-to-short-duration high-intensity rainfall contributes to landslides. Shifting precipitation patterns due to climate change, alterations to sub-surface conditions, such as pore-water pressures, retreating glaciers, and permafrost, further increase the risk of landslides. This synthesis apprises the physical drivers associated with MDLs and their complex interplay in triggering MDLs. The added value of this synthesis is multifaceted: (1) to uncover moisture-driven landslide trends and associated cascading hazards across the high-mountain areas of the globe (2) To explore the probability of rain-driven landslide-related mortality rate across different continents using the archived landslide information. (3) To present a systematic review of available physical and statistical tools to identify MDL triggers and highlight the need for updating rain thresholds using observed and projected climate information to address nonstationarities related to climate change and climate variability. Our analysis of archived rainfall-triggered landslides of the global landslide catalogue between 2007 and 2022 shows that global precipitation contributes to more than 60% of landslides. Further, our observational assessments of the fatality versus landslide frequency curves show that high-mountain Asia experiences the most frequent landslides, and its populations are especially vulnerable to such catastrophic events. The contribution of this synthesis paper is to inform scientists and practitioners of the latest developments in MDLs, aiding the translation of scientific understanding into developing resilience policies and adaptation efforts.

Landslides are common in Uttarakhand state due to many geo-environmental variables. The main objective of this study is to produce a landslide susceptibility map of Chamoli district, Uttarakhand. To assess the influence of geo-environmental parameters on the occurrence and distribution of landslides, the Frequency Ratio (FR) model using remote sensing and GIS-based techniques is applied in the present study. The FR model is developed from the landslide inventory map and geo-environmental parameters including slope aspect, altitude, slope, TRI, TWI, SPI, rainfall, earthquake, soil, NDVI, geomorphology, geology, LULC, distance to rivers, distance to roads, and distance to faults. The model is validated using the AUC curve method. The landslide susceptibility map is divided into five zones, viz. Very high (7.92% area), high (15.78% area), moderate (21.13% area), low (25.02% area), and very low (30.15% area). The result shows that about 7.92% of the study area is a high landslide potential area where almost 55% of total landslide events are occurred. we can trace about 90% of total landslide occurrences found within 23.7% of the study area.

Steep geographical terrain is very vulnerable to the trend of landslip all around the world. Landslides take place regularly and on a yearly basis throughout India’s diverse hill and mountain ranges. Tamil Nadu’s Nilgiris district is especially susceptible to landslides because of the region’s abundant rainfall for the duration of the South and North East monsoons. The focus of this study is to define high-risk areas and pinpoint features that make landslides vulnerable. It is essential to use the landslide hazard zonation maps appropriately and conduct a thorough analysis of each slope that is vulnerable to landslides. Development, the risk of landslides today, and projected future slopes where we can place the early warning system for slope failure based on past landslide locations should all be represented on planning-level maps. Using the weighted regression model, a straightforward statistical method has been used to calculate the proximity of their relationship. Additionally, weighted regression models were useful in validating the chosen causal factors based on their capacity to prevent a landslide episode because they could explain in detail the variance in scores between the causative factors for each class of landslides as well as the distribution of landslides using geographic information systems (GIS). The outcome indicates a relationship between the likelihood of a landslip occurring and the slope, with steeper slopes having higher landslip probability and a slope prediction rate of 23.82. The research area’s western and eastern halves are heavily populated with high-susceptibility areas.

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.

Landslides are very common phenomena along the full stretch of Himalayan Mountain belt of northern India. Every year the foothill states like Himachal Pradesh, Uttarakhand, West Bengal, Sikkim, Meghalaya and Arunachal Pradesh experience severe landslides especially during the monsoon season. Higher frequency and intensity of landslide imparts a negative impact on the tourism industry which is one of the major economic sectors of this region. Rudraprayag district of Uttarakhand is endowed with a number of religious tourist spots like Gauri Kund, Madmaheswar temple, Kalimath, Omkareswar temple, Trigunayan temple of which Kedarnath derives special mention. Every year huge influx of tourists visit these places of Rudraprayag which generates good revenue. The study area lies within the landslide vulnerability zone and experiences the aftermath of landslide every year which affects the tourism industry in turn. The present research work attempts to perform landslide susceptibility using Shannon Entropy Model, a widely used decision making tool coupled with Geospatial techniques and applied on some selected tourist spots of Rudraprayag district in Uttarakhand. The outcome of the research work would reveal the validation of the Shannon entropy model in landslide susceptibility assessment of a few tourist spots’ of Rudraprayag. This paper would help in prognosticating the future of the region’s vulnerability towards landslide and henceforth in judicious management planning for attaining sustainable tourism in the study area.

The Himalayan region of India, in particular, faces an escalating slope instability due to landslides, posing significant pressure on rapid developmental activities. In this chapter, the study focuses on generating a comprehensive landslide susceptibility map (LSM) to analyze and detect the most crucial zones susceptible to landslides for the biggest hydroelectric project undertaken in India- Subansiri lower hydroelectric project situated on the intensive slopes of the Himalaya which is under a constant threat of recurring landslides utilizing a Geographical Information System (GIS) based Analytical Hierarchy Process (AHP) model. The LSM was created by considering a range of 9 geo-environmental parameters like slope, aspect, lithology, lineament density, geomorphology, drainage density, NDVI, LULC, and Road (Euclidean distance) to identify areas susceptible to future landslides and to delineate potential hazard zones. Using the Receiver Operating Characteristic (ROC) curve model, the AHP model was validated and yielded accuracy rates of 79.2%, 79.8%, and 79.2% for the geometrical-interval, quantile, and natural breaks (Jenks) classification models, respectively. A predictive numerical rockfall model Rapid Mass Movement Simulation (RAMMS) was employed to detect the runout trajectories and spatial extent of rockfall around the vicinity of the dam. The velocity of the estimated future rockfall was detected to be 9.84 ms⁻¹, Jump height of 1 m, and kinetic energy of 132.80 kJ. The study’s outcomes hold practical implications for infrastructure development and management in landslide-prone regions, as the susceptibility map can aid in the implementation of effective risk mitigation strategies.

Landslides have been catastrophic events in mountainous regions for ages. However, as human populations and economies expand, anthropogenic activities aimed at development have led to significant changes in land use and land cover in these regions. These changes have become crucial factors amplifying the susceptibility to landslides and escalating the threat of such disaster’s multiple times over. There are two different aspects that needs to be understand to measure the susceptibility of landslide in a region. These are landslide hazard and risk to the community. Both these aspects are intricately intertwined with the spatial arrangement of land use and land cover. The occurrence of landslides is contingent upon a variety of geospatial factors, including soil texture and integrity, terrain slope, vegetation cover, drainage patterns, rainfall distribution, and soil erosion dynamics. Simultaneously, the impact of landslides on society, namely the risk posed to communities, is influenced by the availability of resources such as flora, fauna, humans, and infrastructure. Therefore, the assessment of land use and land cover holds immense significance not only in understanding susceptibility to landslide hazards but also in conducting impact assessments and devising effective mitigation strategies. This chapter provides insights into the trends of land use and land cover as conditioning factors in landslide hazards. It explores the contributions of 6,650 authors through the analysis of 3,240 articles published on landslides since 1976. Approximately 33% of these articles explicitly mention the terms “land use” and “land cover” in their abstracts, titles, and/or keywords, constituting contributions from 2,723 authors. In recent years, there has been a significant increase in studies focusing on landslides and the utilization of land use and cover as crucial terms, with the number of articles doubling in the last five years. Publications from the period 2001–2021 account for the top 20% of most cited works, contributing to over 75% of the total citations.

As the world warms, many glaciers throughout the world are melting. Glacier melting has been linked to climate change as a major cause of landslides in mountainous regions. The whole effect of glacier growth and sublimation on landslide-induced denudation cannot be understood from an evaluation of the triggering mechanism alone. The proposed chapter constructed landslide and glacial statistics to represent the HMA employing a Landsat time-series spanning the last years to evaluate recent tendencies. Large earthquake-related landslides were isolated and deleted from the database, leaving a resource for investigating potential climate-change-related shifts. Mountainous locations are particularly susceptible to the impacts of climate change. The intricate web of connections among climatic, ecological, and sociocultural systems in these areas is being modified by the shifting climate. This chapter aims to investigate the forthcoming obstacles that these distinctive locations will face. Analysis using satellite images and landslide inventories indicates a substantial increase in the frequency of significant landslides associated with glaciers throughout recent decades. Our investigation is based on the research conducted in 2021 on the High Mountain Asia (HMA) region revealed that the extent of land impacted by landslides almost quadrupled during the time periods of 1999–2008 and 2009–2018. Additionally, the frequency of big landslides (>1 km²) also saw an increase throughout this timeframe. Present chapter examine the significance of these factors and identify the challenges that arise in the current environment and political landscape. The expansion of landslides is linked to the shrinking of glaciers. Proposed work based on the secondary sources of data, given dataset is very useful for risk assessment, is an inventory of landslides connected to glaciers.

This work aimed to utilize the capabilities of the Interferometric Synthetic Aperture Radar (InSAR) technique to assess the slope instability condition in part of Himachal Pradesh, India. India is one of the countries to report the largest number of landslide events, especially in the monsoon season. The Himalayan regions in India are prone to heavy precipitation (rainfall) which loosens the soil and causes slope displacement. This study involves the rain-induced (pre and post-event) landslide study to detect the magnitude of ground deformation in Bariara village in district Kangra, Himachal Pradesh, India. In this study, the microwave repeat pass Interferometric Synthetic Aperture Radar (InSAR) technique was used for deducing the slope deformations. Two SAR imageries were used to detect the phase difference for developing the deformation map showing a downslope movement of almost 1.5 m. The results from the interferometric method have been cross-validated by data collected from the field survey. This study aimed to find the absolute rate of movement of a Himalayan landslide using the DInSAR method and has effectively added to the landslide literature for extensive future research in this study area.

A large number of local people and property losses have been reported in the Rangamati Hill Tracts (RHT) of Bangladesh due to the 17 June 2017 Landslide event. This chapter investigated the actual causes of this landslide event which focuses on the coupling effects of earthquake-rainfall-induced landslide activities that occurred in this area. To know this scenario better, the study considered freely available earth observation multi-temporal Landsat satellite scenes for landslide-induced change assessment. In addition, thirteen associated conditioning factors were integrated within the GIS environment by adopting a multicriteria analytical hierarchy process (AHP) approach to prepare a landslide hazard risk (LHR) map of the study area. The study results quantified 3.81, 3.00, and 8.90% of areas as very high-risk, high, and moderate LHR categories. Moreover, the results also revealed that multiple issues (i.e., earthquakes, heavy rainfall, hill cutting, deforestation, and haphazard human settlement construction on the hill slope) are the main catalysts behind this landslide event. Finally, the study recommends integrated Remote sensing, GIS, and AHP techniques for future landslide risk assessment across the globe.

Landslide is one of the major environmental disasters which affects both our natural ecosystems and human well-being. Therefore, a comprehensive understanding of landslide risk management is crucial. A total of 444 mass movement-related events occurred from 2000 to 2022. Among all these events, landslides were 346 and China (56), Indonesia (52), India (28), Colombia were the most landslide-affected countries. In this chapter, landslide risk assessment, awareness, and risk mitigation were discussed. After a thorough analysis of more than 50 case studies globally, this chapter showcased the major insights. Results found that both quantitative and qualitative methods were used for landslide risk assessment and mapping. More widely used methods include multi-criteria analysis, cluster analysis, multi-variate analysis, Fuzzy modelling, analytical hierarchy approach, machine learning and artificial intelligence. Landslide risk mitigation encompasses both structural and non-structural measures. Particularly, nature-based solutions such as vegetation coverage are cost-effective and environment-friendly measures. It is also found that community-based landslide risk reduction and early warning based landslide risk mitigation are more successful. In addition to risk mitigation, awareness programs play a vital role in educating the public and children about landslide risk management. Existing scientific knowledge from case studies explains the different forms of such awareness campaign. However, there is a gap between the affected communities and relevant stakeholders. To bridge this gap, a strong collaboration and cooperation between all the actors and stakeholders needs to be ensured towards an integrated management of landslide risks.

This chapter will present community-based landslide disaster mitigation on the northern slopes of the “Telaga Lele” hill, Banjarnegara Regency, Indonesia. The landslide that occurred in December 2014 caused dozens of houses to be damaged, and hundreds of people were buried by landslide material. A total of 2,038 refugees and a total of 95 victims were found dead, and 13 people were declared missing. The method used to obtain data is qualitative, namely by interviewing people who know about the disaster. The limitation of this research is that observations were not carried out when the disaster occurred. Data is strengthened with GIS before and after the disaster. Disaster management reports are also used in this paper. The research results show that the Telaga Lele Hill Landslide event was caused by geological conditions with a slope of around 45°, heavy rainfall for three days, and a height of more than 1000 m. The community's lack of strategy in preventing disasters makes them vulnerable to landslides. Community-based disaster mitigation can be carried out in two ways: strengthening understanding of anticipating landslide hazards and biosystems by paying attention to the balance of slopes formed by plants.

Rainfall stations provide reliable rainfall data, but their availability is limited in mountainous areas, complex terrain, and remote areas. Satellite rainfall datasets (SRDs) provide high-resolution worldwide rainfall estimation, which has the potential to be used in identifying rainfall conditions that trigger landslides. Landslides can be predicted through rainfall threshold modeling, serving as an early warning system. It is essential to validate the chosen threshold model to assess the accuracy of forecasting landslide occurrences triggered by rainfall events. This study aims to evaluate the effectiveness of a dual fusion approach, utilizing two SRDs, in establishing rainfall thresholds for landslide prediction in the Badung regency over the period from 2015 to 2022. Rainfall threshold analysis in this investigation focuses on cumulative rainfall events occurring 5, 7, 10, and 15 days prior to the onset of landslides. The first fusion was established through the application of the cumulative distribution functions method, involving a comparison between the SRDs and the datasets from rain gauges. Subsequently, the analysis transitioned to the second fusion, where a weighted correlation coefficient function was employed to assess the connections between rain gauges and individual SRDs. The results illustrate that the second fusion of SRDs yields an area under the curve value of 0.82 for a 15-day cumulative rainfall, surpassing the performance of the first fusion. The first quartile approach demonstrates the highest accuracy compared to alternative methods, providing a reliable estimation of landslide occurrences with minimal error.

Landslides significantly threaten human life, infrastructure, and environmental balance. In the hilly regions of Bangladesh, including Sylhet and Rangamati, landslides are frequent, causing 727 deaths and 1017 injuries between 2000 and 2018. The northeastern section of Bangladesh is projected to receive over 500–600 mm of precipitation in 2023, breaking records over the past 122 years, according to the European Centre for Medium-Range Weather Forecasts (ECMWF). With an elevation range of 0 to 195 m above sea level and 18% of its total land area covered by water bodies, Rangamati is particularly vulnerable to landslides. Despite the devastating impact of landslides, susceptibility assessment and risk management strategies are lacking. This research aims to address this gap by developing a comprehensive framework for sustainable landslide risk mitigation using geostatistical and geospatial modeling techniques. Factors such as land use and land cover (LULC), elevation, slope, topographic wetness index (TWI), precipitation, lithology, soil type, normalized difference vegetation index (NDVI), and distance from roads are used to create a frequency ratio (FR) model and identify landslide susceptibility and risk zones. The resulting high-resolution landslide susceptibility map (LSM) and risk assessment models provide valuable insights for policymakers, land-use planners, and stakeholders involved in disaster risk reduction and sustainable development. By applying geostatistical and geospatial modeling techniques to assess landslide susceptibility, manage risk, and promote sustainability, this research enhances resilience to landslides and highlights the importance of proactive planning and informed decision-making in mitigating the impact of landslides for promoting sustainable development in hilly regions.

Landslides in the Indian Himalayan Region (IHR) primarily result from extreme precipitation events, distinctive topography, and inadequate soil and water conservation practices. Managing such calamity is a daunting task. This study advocates an innovative rule-based approach for Landslide Risk Management (LRM). A distinct feature of this approach is a likelihood-consequence matrix (LCM) that formulates 25 combinations to segregate the basin into four risk zones: low, moderate, high, and extreme. Each combination aligns with a specific landslide likelihood and corresponding damage. Furthermore, integrating Geographic Information System (GIS) technology ensures effective spatial data management and visualization. The proposed rule-based model offers scalability, adaptability, and user-friendliness. The proposed methodology allows the stakeholders to take informed decisions for effective disaster response, evacuation plans, road closures, and long-term mitigation strategies. To illustrate the proposed methodology, the Alaknanda basin in the Indian Himalayan Region is considered a case study. The methodology adopted in this study seamlessly integrates the Likelihood of landslides depicted by Landslide Hazard Maps, with the anticipated Consequences, represented through comprehensive Soil Erosion Maps. It is observed that, 32.0% of the area falls under “Unlikely” likelihood, with 57.3% facing “Minor” consequences. “Low” risk covers nearly half the area (49.7%), while “Moderate,” “High,” and “Extreme” risks constitute 25.2%, 17.6%, and 7.5% respectively. Such an approach offers an expansive analysis, enriching the understanding of inherent vulnerabilities and potential impacts within the Alaknanda Basin. It is hoped that the proposed system will help planners and decision-makers in effective risk management on a scientific basis.

As the demand for vehicle usage continues to surge, the strategic placement of fuel stations within urban areas has become a topic of significant consideration. The location of a fuel station is a critical factor in ensuring accessibility and efficiency. Landslides stand out as one of the most destructive hazards in natural terrain areas, emphasizing the need for reliable susceptibility analysis methods to aid policymakers in facility placement. This chapter investigates the applicable criteria for the location of Compressed Natural Gas (CNG) fuel stations, using standards and expert opinions. The study employs Geographic Information System (GIS) as a quantitative technique for landslide susceptibility, integrating a comprehensive dataset. The research focuses on extracting potential alternatives through GIS and ranking them using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Analytical Hierarchy Process (AHP), and a hybrid TOPSIS-AHP method. To assess the practicality of the framework, the model is implemented in Bonab City, East-Azerbaijan, Iran. Eight potential locations for CNG stations in Bonab are identified using GIS, and they are subsequently ranked through three multi-criteria decision-making methods. The results indicate that locations C and G, situated on Bonab-Tabriz road and Bonab-Maragheh road, respectively, are optimal for constructing a fuel station.

This study presents a geospatial assessment of potential landslide hazards in the Bandarban District of the Chittagong Hill Tracts (CHT) in Bangladesh. Landslides are a severe threat to mountainous regions, causing loss of life and significant economic damage worldwide. The study employs a Geospatial weighted overlaying technique, assigning values on a scale of 1–5 and 100 for factors influencing landslides. These factors encompass historical landslide occurrences, land use patterns, rainfall, elevation, slope characteristics, soil types, geological features, distances to rivers, roads, stream orders, and socio-economic variables like household density, population density, income levels, and education. These values are determined in consultation with local communities and domain experts. The geospatial model categorizes the Bandarban District into five distinct levels of landslide hazards, ranging from “Very High Hazard” to “Very Low Hazard.” “Very Low Hazard” areas, constituting 13.39% of the total hazard area, pose the least risk but still require basic preparedness measures and educational initiatives. “Low Hazard” areas, covering 36.04% of the hazard area, necessitate lower mitigation priority but ongoing awareness and preparedness efforts. The “Moderate Hazard” areas, making up 39.39% of the hazard area, require a multi-faceted approach for risk reduction, including land-use regulations, reforestation, and community-based disaster risk reduction programs. “High Hazard” areas, though smaller at 11.00% of the hazard area, demand immediate attention with actions such as engineering solutions, land use planning, and proactive disaster preparedness. The “Very High Hazard” area, representing only 0.18% of the hazard area, requires the most urgent focus for mitigation measures.

Landslides in the form of circular failures often occur in homogeneous geomaterials like soil, debris and highly weathered or fragile rocks. In slope engineering practices, Factor of Safety (FoS) is commonly determined using Circular Failure Charts (CFCs), Limit Equilibrium Methods (LEM) and Finite Element Methods (FEM). The present study is an attempt to assess the disparities and inter-relationships in FoS using CFCs, LEM and FEM. FoS calculations were performed manually and using an automated Windows-based open-source tool FS Calculator 1.0. Pertinent geomechanical (cohesion, friction, unit weight) and geometrical properties (slope angle and height) of fifty-one slopes from nine case records were considered in the present analysis. A linear correlation occurs in FoS using CFC and LEM i.e., \({\text{FoS}}\left({{\text{CFC}}}_{{\text{FS}}}\right)= 2.7592\times {\text{FoS}}({\text{LEM}})- 2.2082\), with the coefficient of determination of 0.99. However, the linear relationship between SRF and FoS (LEM) is \({\text{FoS}}\left({{\text{CFC}}}_{{\text{FS}}}\right)=0.889\times {\text{SRF}}\left({\text{FEM}}\right)+0.0438\) having coefficient of determination of 0.63 only. It connotes that FoS using CFCs and LEM is relatively well in comparison to FoS from CFCs and FEM. It is also evident that CFCs provide relatively more conservative FoS than FEM and LEM. Among all three proxies, FEM relies on a wider set of geomechanical parameters and evaluates stability under static and dynamic conditions with capability of applying wider range of remedial measures. Since, most of the FEM tools are commercial, open-source CFCs based FS Calculator may be used and proposed relationships may be utilized as correction factor.