Landslide Vulnerability Research Articles

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India

Utilization of Sentinel Image Multitemporal Data for Landslide Potential Identification in Pujon Sub-District

Landslides are one of the most common disasters in Indonesia. Landslides are often triggered by high rainfall and occur for several days. Unstable soil structure is very easy to experience landslides. The case study of this research is in Pujon Sub-district, Malang Regency. Pujon sub-district has a hilly and highland topography. Its hilly-mountainous relief and steep slopes cause Pujon Sub-district to experience frequent natural disasters, especially landslides. The research was conducted by processing Sentinel 1 image using DInSAR method to obtain data on land surface change and weighting scoring method to obtain information on landslide prone areas. The processing of DInSAR method produces the value of vertical land surface change. The largest increase in land surface with a displacement value of 0.2972 m. And the largest land subsidence is -0.2234 m. Such movement can be an indicator of landslide occurrence. The GIS method produces 3 classes of landslide vulnerability level, namely low, medium and high in each year in Pujon sub-district. The average landslide prone area in the last 5 years is 15081.1331 ha. The processing results of DInSAR method and weighting scoring method can describe the landslide prone areas in Pujon Sub-district which is expected to be useful for landslide mitigation efforts.

Comparing the effectiveness of landslide susceptibility mapping by using the frequency ratio and hybrid MCDM models

The present study involved the landslide susceptibility mapping of Indian state of Meghalaya by using the frequency ratio (FR) and Analytical Hierarchy Process (AHP)- VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) (AHP-VIKOR), and the outcomes were validated and compared. Therefore, fourteen landslide conditioning factors were selected after a multi-collinearity test by tolerance and variance Inflation Factor. Based on 70 % of the training data, the relative frequency and predictive rate value was used to generate final output. In opposite, the overall ranking was used to generate landslide susceptibility map (LSM) for the AHP-VIKOR model. Subsequently, the generated LSM was classified into five hierarchical zones from very low to very high based on the natural breaking classification. The area under the curve of receiver operating characteristics from 30 % of validation landslides shown more predictive accuracy of AHP-VIKOR model than FR model under similar geo-environmental condition. The spatial distribution of landslide susceptibility zones shows that 37 % of the region under the threat of having a high landslip risk. Landslides are primarily found in barren Cretaceous sedimentary rocks, with increased risk near roadsides. This is due to slope cutting for roads, making hilly terrain unstable. Areas with more annual rainfall face greater landslide threats. Therefore, the outcome of the study may contain the useful information that helps the local administration to recognize vulnerability of landslides as well as to take up the hazard mitigative measures.

AI-enabled Landslide Recognition for Effective Public Health Risk Management

The frequency of hazardous landslides has increased worldwide as a result of increased heavy rainfall events and increased human construction activities. The assessment of landslide vulnerability is an essential and effective method for preventing landslides. To resolve these issues, this paper develops a novel Rat Swarm integrated Random Forest (RSRF) method to manage the risk evolved through the landslide. The LISS-III satellite dataset of remote sensing (RS) images was gathered for the study. The preprocessing is performed by employing the z-score normalization to standardize the data images. The rat swarm (RS) optimization enhances the feature selection in the landslide by efficiently forecasting relevant assessment and the random forest (RF) employed to improve the classification accuracy in the landslide recognition process. Various existing methods are utilized for the comparison performance with the proposed RSRF techniques. The outcome shows that the proposed RSRF method improved more significantly than all other existing techniques in terms of area under the curve (AUC (93.3%)), F1-score (85.5%), log-loss (40.4%), and recall (97%).

Mitigation and Prevention of Landslide Hazard of Himachal Pradesh in the Lesser Himalaya

Landslides are destructive geological processes that have globally caused deaths and destruction to property worth billion dollars. Landslide occurrences are widespread and prolific in India covering more than 15 per cent of the total area. These are mostly concentrated in the Himalayan belt, parts of Meghalaya Plateau, Nilgiri Hills, Western and Eastern Ghats. The slope failure in the hilly terrain is due to geological processes and events. The frequency and magnitude of slope failure also increased due to anthropogenic activities such as road construction, deforestation and urban expansion. Keeping all these problems in mind research focuses on the Lesser Himalaya of Himachal Himalaya as it falls under very high risk zone in case of landslides and comprise of three objectives. They are: a) to analyse the spatial pattern of landslides in the Lesser Himalaya, b) to assess the causes of landslides vulnerability in the study region and c) to suggests some preventive measures to mitigate landslides. In this work an attempt has been made to collect data on landslides incidences and damage from the secondary sources like Geological Survey of India, Building Material and Technology Promotion council from Ministry of Urban Affairs. The methodologies adopted for data analysis are simple tabulations, bar diagrams, statistical and mapping techniques to represent the Landslide vulnerability of the Lesser Himalaya. The analysis of the study reveals that there is increase in the number of landslides. The spatial pattern of landslide shows linear patterns, viz. along roads, rivers or lineaments/ faults. Besides, heavy rainfall, floods and earthquakes enhance the vulnerability condition. The landslides may be part and parcel of the Himalayan landscape, but they can be mitigated by some suitable measures. Few methods of landslide prevention in the study region have been suggested.

Education and character reformation in Nigeria contemporary society: Challenges and prospects

Background: Education is a transformative tool that is critical to the progress and development of a nation. The goal of true education is more than the cognitive development and creative thought processes. It is also about character molding of a person, making a holistic individual. It is true that the foundation of a quality education is character. An individual's education cannot be complete without character, and the knowledge they have acquired will not benefit Nigerian society or themselves unless their character is transformed. Method: This study identifies character deficit as a major challenge in Nigeria contemporary society. The research examines the issues and prospects, and how education can be applied through character reformation to address the problems. The study relies on secondary data collected from text books, journal articles, and was analyzed in relation to the scope of the study (Nigeria). Findings: The theoretical framework is drawn from the social learning theory of Albert Bandura’s and moral development theory of Jean Piaget which set out the progressive model on how the Nigerian society can be morally re-engineered to produce citizens that will contribute to the progress and growth of Nigeria. This study shows that mapping the level of landslide vulnerability in the Kebomas District can help plan and manage disaster risks more effectively, especially in areas with high vulnerability. The results of this study provide a basis for creating more targeted landslide mitigation and handling strategies, as well as the need for special attention in areas with moderate to high vulnerability. Conclusion: It concludes and recommends that an integrative and consensus approach be adopted to achieve this objective. Novelty/Originality in this article: This study offers an innovative approach to integrating character building into the Nigerian education system by combining social learning theory and moral development. This study opens up new avenues for holistic education reform, focusing on cognitive development and character building as the foundation for building a more ethical and productive society.

Landslide disaster risk analysis in Kebomas District, Gresik District

Background: Landslides occurred during the rainy season in Kebomas District, Gresik Regency, which has topography in the form of hills left over from limestone mining in 1960-1990. Field surveys conducted from December 2021 to March 2022 stated that landslides occurred during the rainy season in Kebomas District and caused damage to surrounding settlements. Slope, rainfall, rock type, soil type, and land use are factors that influence the occurrence of landslides. The purpose of this study was to map the level of landslide vulnerability in Kebomas District using a Geographic Information System. Method: The methodologies used in this study include the application of GIS for geospatial data analysis, assessment of landslide hazard potential through secondary data and field surveys, and vulnerability analysis of physical data. Additionally, a qualitative analysis of landslide disaster risk was conducted, combining hazard and vulnerability factors. These methods aim to identify and prioritize landslide-prone areas in Gresik Regency for better disaster risk management. Findings: The results showed that there were three levels of landslide vulnerability in Kebomas District, namely low, medium and high. The low vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages or sub-districts in the south, west and north, namely; Dahanrejo, Kedanyang, Prambang, Kembangan, Randuagung, and Segoromadu. The moderate vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages in the southeast, northwest and north, namely; Randuagung, Prambang, Dahanrejo, Kembangan, Sidomoro, and Ngargosari. Areas with a high vulnerability class are located in the central part of the sub-district which includes 15 villages or sub-districts, most of which are in four villages, namely; Klangonan, Kawisanyar, Randuagung, and Sidomoro. Conclusion: This study shows that mapping the level of landslide vulnerability in the Kebomas District can help plan and manage disaster risks more effectively, especially in areas with high vulnerability. Novelty/Originality of this article: The results of this study provide a basis for creating more targeted landslide mitigation and handling strategies, as well as the need for special attention in areas with moderate to high vulnerability.

Assessment of fluvial response to landslide susceptibility and transient response of tectonically active upper Alaknanda River basin of Uttarakhand Himalaya, India

This paper focuses on the transient response of the upper Alaknanda River basin and landslide vulnerability analysis of tectonically active segments located between the Trans Himadri Fault (THF) and Main Central Thrust (MCT) in the higher central Himalayan domain. We applied the power law functions of the conventional bedrock incision proxies to decode erosionally balanced tectonic processes. The channel concavity and slope of the upper Alaknanda basin have been logarithmically evaluated to understand the balance between erosion/incision and tectonic events. Further, tectonically balanced erosional events along the trunk and tributary stream dynamics have been estimated using the Chi (χ) function law. The results of χ suggest a disequilibrium state of the trunk and tributary stream concerning steady state condition. Furthermore, the landform and longitudinal river profile have been analyzed to understand differential uplift/incision and impact of erosion in river profile between THF and MCT. Furthermore, we applied a geospatial technique for landslide susceptibility analysis. Our results show that approximately 94.45% of the basin area is highly vulnerable and has the potential for future landslides and glacial avalanches. Furthermore, we claim that this study is extremely helpful to identify the locations of future geohazards (landslide, avalanche, cloudburst etc.) and their impact on the downstream areas where population density is very high.

Integrated risk assessment of landslide in karst terrains: Advancing landslides management in Beiliu City, China

Beiliu City, Guangxi Zhuang Autonomous Region, China, like other karst landscape areas, is also suffering from the threat of landslides. This research applied the circular variance method to delineate 31,465 slope units and then combined it with a geographic information system and a well-established risk assessment system to synthesize three perspectives-susceptibility, hazard, and vulnerability to realize the risk assessment of karst landslides in Beiliu City. Using perennial field surveys, aerial images-derived landslide inventories, slope unit investigations, and a literature review combined with a Fully Convolutional Neural Networks approach, we present a culminating in the derivation of landslide susceptibility assessment outcomes within the study area. Because Beiliu City is located in a karst area, this research employs the quantification of soil erosion intensity as a metric to assess landslide hazard. In this study, based on a comprehensive analysis of the basic characteristics of landslides and their associated disaster-bearing bodies, a robust landslide vulnerability assessment index system was constructed for risk assessment of karst landslides in Beiliu City. The landslide risk assessment model in karst area is constructed by combining the landslide susceptibility and the vulnerability in the danger range. Detailed field surveys verified the scientific validity and accuracy of the assessment results. This research extends beyond merely furnishing scientific data support for local governmental efforts in disaster prevention and mitigation. It impressively enhances the methodological framework for conducting landslide risk assessments within karst landscapes, thereby offering a robust foundation for both immediate and strategic responses to the inherent geological vulnerabilities of these areas.

Landslide Vulnerability Mapping Using Spatial Analysis Methods, Case Study in Watulimo Sub-District, Trenggalek District, East Java

This study aims to determine landslde vulnerability areas in Watulimo District base on landside vulnerability factor using GIS. The method used is a weighted tiered quantitative by providing an appreciation value and using weight on various parameters in the analysis. These parameters are ranfall, slope, geology, soil type and landuse. The resul shows that there are classes of landslide vulnerability, named not vulnerable, slightly vulnerable, vulnerable, and very vulnerable. Base on the map, I is known that amost al areas in Watulimo are vulnerable to landslides. The highly vulnerable areas are distributed in Slawe, Dukuh an Watulimo villages. The northern and Southern Watulimo sub-district are slightly and non vulnerable areas. Mapping the level of vulnerability of landslide is one of the solutions to assist in determining policies to reduce the risk of disaster.

Climate change-induced landslide vulnerability: Empirical evidence from Shimla district, Himachal Pradesh, India

Landslides as dynamic geological hazard have caused socio-economic and environmental vulnerabilities at various scales. Thus, landslide vulnerability assessment is essential for understanding the implications for society and the environment. The present study attempts to examine landslide vulnerability in Shimla District, Himachal Pradesh, India. It constructed a composite landslide vulnerability index (CLVI) by integrating 57 indicators of 7 different domains (social vulnerability index, physical vulnerability index, economic vulnerability index, climate change and environmental vulnerability index, early warning system index, emergency response system, and adaptation strategies index) at the household level. CLVI serves as a most effective tool for the quantification of complex domains of disaster vulnerability and identification of thematic areas for disaster risk reduction. The data on various indicators were derived through a field survey of 450 households from 30 villages. The relationship between CLVI and its domains was ascertained using multiple linear regression. The CLVI analysis revealed that nine villages were highly vulnerable to landslides, while six villages experienced moderate vulnerability. Climate change and environmental vulnerability, physical fragility, and low adaptation strategies induced high to moderate vulnerability. The regression analysis showed that the degree of vulnerability was mostly influenced by the physical domain, followed by the economic and environmental domains. Effective policy, institutional setups, provision of landslide-resistant buildings, and livelihood diversification may enhance adaptive capacity among the communities. Thus, CLVI has not only helped in identifying thematic areas for reducing vulnerability but has also been instrumental in recognizing priority areas.

Application of Integrated Multi-source Data in Landslide Vulnerability Assessment

This paper employs GIS technology and multi-source data integration methods to assess landslide vulnerability in a city in the southwestern region. The study combines the Analytic Hierarchy Process (AHP) and machine learning techniques, utilizing field survey data and remote sensing information to perform a systematic quantitative analysis of the hazard and vulnerability of landslides. A comprehensive evaluation model was established, assessing the risk levels for populations, buildings, and infrastructure, thereby providing effective decision support for mitigating landslide risks.

Assessment of Landslide Susceptibility in the Moxi Tableland of China by Using a Combination of Deep-Learning and Factor-Refinement Methods

Precisely assessing the vulnerability of landslides is essential for effective risk assessment. The findings from such assessments will undoubtedly be in high demand, providing a solid scientific foundation for a range of critical initiatives aimed at disaster prevention and control. In the research, authors set the ancient core district of Sichuan Moxi Ancient Town as the research object; they conduct and give the final result of the geological survey. Fault influences are commonly utilized as key markers for delineating strata in the field of stratigraphy, and the slope distance, slope angle, slope aspect, elevation, terrain undulation, plane curvature, profile curvature, mean curvature, relative elevation, land use type, surface roughness, water influence, distance of the catchment, cumulative water volume, and the Normalized Vegetation Index (NDVI) are used along roads to calculate annual rainfall. With the purpose of the establishment of the evaluation system, there are 17 factors selected in total. Through the landslide-susceptibility assessment by the coupled models of DNN-I-SVM and DNN-I-LR nine factors had been selected; it was found that the Area Under the Curve (AUC) value of the Receiver Operating Characteristic Curve (ROC) was high, and the accuracy of the model is relatively high. The coupler, DNN-I-LR, gives 0.875 of an evaluation accuracy of AUC, higher than DNN-I-SVM, which yielded 0.860. It is necessary to note that, in this region, compared to the DNN-I-SVM model, the DNN-I-LR coupling model has better fitting and prediction abilities.

Vulnerability assessment of landslides along the Yunnan section of the Northern Tropic of Cancer based on fuzzy evidence weight model

AbstractFrequent geological disasters pose significant threats to both human lives and property. High-risk landslides, with features such as rapid occurrence, wide distribution, and strong destructiveness, are of particular concern. To investigate the distribution characteristics and threat range of landslides, the Yunnan section of the Northern Tropic of Cancer was selected as the study area. Based on the fuzzy evidence weight model and using a grid size of 1000 m × 1000 m as the study unit, a vulnerability assessment of landslides along the Northern Tropic of Cancer in Yunnan was conducted. This assessment involved constructing a set of 13 evaluation indicator factors from three aspects: geological environment, geographical environment, and human engineering activities. The assessment results were subsequently subjected to precision verification. The results are as follows: (1) The overall vulnerability of landslides in the Yunnan section of the Northern Tropic of Cancer is moderate, covering an area of 25,968.79 km2. This area constitutes the largest proportion, accounting for 46.30% of the entire study area and is primarily distributed in counties such as Yanshan, Funing, and Shuangjiang. The highly vulnerable area covers 15,942.27 km2, representing 28.43% of the total study area. This region is mainly distributed in counties such as Gejiu, Yuanjiang, and Mojiang. (2) The main triggering factors for landslides are twofold: first, large-scale slope instability caused by excavation in human engineering activities, leading to extensive unstable slopes affected by long-term weathering and gravitational effects. Second, soil erosion resulting from land cultivation leads to the collapse and sliding of weathered and fragmented rocks under the influence of heavy rainfall and water erosion. During routine geological disaster patrols, special attention should be given to these factors, with a focus on implementing engineering measures in areas with more fragile geological environments. (3) Precision verification of the assessment results was conducted using both receiver operating characteristic (ROC) curves and Success rate curves. The area under the curve (AUC) values for the success rate curve and ROC curve were 0.85 and 0.74, respectively. The verification results showed high accuracy and feasibility of the evaluation method.

Analysis of landslide-prone areas in Tripe Jaya district, Gayo Lues Regency

Abstract Tripe Jaya District is one of the sub-districts of Gayo Lues regency, which has hilly and mountainous morphological areas with steep slopes and landslides in several regions of the road area. This study aims to identify and map the location of landslides and analyze landslide areas. A quantitative method for measuring landslide-prone levels from the Center for Volcanology and Geological Hazard Mitigation 2005, the method used is the score and weight of the parameters that cause landslide-prone. The causes of landslide-prone sites are rainfall data, soil type, elevation, land use, and slope. The results of this study have five classes of landslide vulnerability, including very low landslide vulnerability level covering an area of 279.21 Ha (1%), low vulnerability level covering an area of 3639.80 Ha (9%), medium vulnerability level covering an area of 17893.07 Ha (43%), high vulnerability level covering an area of 19822.91 Ha (47%), and very high vulnerability level covering an area of 150.23 Ha (0.36%). Landslide events in Tripe Jaya District based on field observations, there are landslide events in high and very high classes. This research provides an overview of landslide-prone areas so the results of the study can be used as a basis for planning mitigation strategies and as a consideration for the government in building the required mitigation developing mitigation measures related to landslide-prone risks in the Tripe Jaya.

Landslide Susceptibility Levels Analysis Based on Geographic Information System (GIS) in Simo District, Boyolali Regency

Abstract Landslides frequently occur in several regions due to high rainfall. This is because Indonesia is a country located in a tropical region with diverse topography and high precipitation. Based on these issues, the objective of this study is to map areas of potentially vulnerable areas to provide accurate information and early warning to the people of Boyolali, especially related to locations that are estimated to have a high level of risk. Mapping areas that have the potential for landslides in Simo District, using Geographic Information System (GIS) software with a weighted overlay method approach. Types of related data such as soil slope, rock type composition, soil type, land use, and annual rainfall amount through a weighted overlay process using weights and scores related to each parameter. Interviews were also conducted to one of the village leaders in each village to find out the history of landslide events for the 2020-2023 period. This study shows the level of landslide vulnerability in areas in Simo District, Boyolali Regency reached 44.25047 km2, medium at 6.333904 km2, and low with an area of 1.094838 km2. The history of landslides in Simo District can be seen in Table 14, where the most landslides occurred in Pentur Village with a total of 4 incidents, followed by Gunung Village and Walen Village with 3 incidents, Wates Village, Kedunglengkong Village, and Sumber Village with a total of 2 incidents and Pelem Village with 1 incident. Kedunglengkong, Pelem, Pentur, Walen, Wates, and Sumber villages need attention from the local government for the implementation of adaptation measures to landslide risk because the 7 villages have a high level of landslide vulnerability.

Identification of landslide vulnerability in the Sendangsari Area, Pajangan District, Bantul, Yogyakarta based on the Weighting Method of Regulation of the Minister of Public Works No. 22/PRT/M/2007

Dusun Krebet, in the Sendangsari Village, sub-district of Pajangan, Bantul regency, is one of the villages situated in a limestone hill area. Due to its location in the hilly terrain, both morphologically and in terms of its geological composition, the Krebet village has the potential for and is at risk of landslides. This research identifies areas that fall into the category of landslide-prone disaster zones using the Weighting Method specified in the Indonesian Minister of PUPR Regulation No. 22/PRT/M/2007. The determination of landslide-prone disaster zones is based on seven parameters, which are slope, geological conditions, land use, water flow density, soil thickness, rainfall conditions, and seismic activity map overlay is conducted with the assistance of weighting based on the Indonesian Ministry of PUPR regulations, which represents a subjective factor according to the researcher’s perspective in the study area. This process results in two risk zones: low landslide risk and medium landslide risk. The research based on the Ministry of PUPR Regulation No. 22/PRT/M/2007 indicates that the Pajangan District is predominantly characterized by a low level of vulnerability to landslide disasters, with only a small area falling into the moderate vulnerability category. This is because the regulation places greater emphasis on the potential damage to infrastructure and does not give as much attention to disaster mitigation aspects that need to be implemented.

Use of Soil Moisture as an Indicator of Climate Change in the SUPer System

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961–2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state were chosen. The results show that in the coastal region there is a tendency towards more saturated soils, and in the semi-arid region there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable. Leveraging soil moisture and precipitation data makes it possible to differentiate between flood risks and landslide vulnerabilities, particularly in regions characterized by higher levels of rainfall. Monitoring soil water content in humid and semi-arid areas can significantly enhance early warning systems, thereby preventing loss of life and minimizing the socio-economic impacts of such natural events. As such, this study provides a holistic understanding of the relationship between climatic patterns, soil moisture dynamics, and the occurrence of droughts and floods, ultimately contributing to more effective disaster preparedness and response measures in Pernambuco and similar regions.

Pemetaan Kerawanan Bencana Longsor Menggunakan Teknik UAV (Studi Kasus: Jalur Sitinjau Lauik Padang – Solok)

One of the most common natural disasters in Indonesia is landslides Heavy rains have caused several landslides in various regions, causing a large impact every year. Landslides caused by rainfall have also become a global problem in many regions, leading to large-scale landslides. Landslides that occur in Sitinjau Lauik, Padang City are influenced by high rainfall with very steep slopes (70˚-80˚), making this area very prone to landslides. One of the techniques used in mapping landslide vulnerability is the Unmanned Aerial Vehicle (UAV) technique, which is a flying machine controlled by a remote controller. The data obtained will be processed using ArcGIS with Weighted Overlay technique, and factors affecting landslides have been conditioned based on Multi Influencing Factor (MIF). This research aims to achieve: 1) the landslide vulnerability level of Sitinjau Lauik Padang-Solok, 2) determine the landslide prone zones in Sitinjau Lauik. The type of research used in this research is quantitative research with spatial approach. The data required are primary data and secondary data. Primary data consists of aerial photographs taken using drones and processed to produce slope, elevation, aspect, and TWI. The secondary data used is the Landsat 8 image in 2023 which is used to determine the vegetation index. This research resulted in 1) The landslide vulnerability level of Sitinjau Lauik is divided into four classes, namely, low landslide vulnerability level with an area of 4342m or 10.81%. Medium landslide vulnerability level with an area of 24575m or 61.16%. High landslide vulnerability level with an area of 11,294m or 28%. And very high landslide vulnerability level with an area of 14m or 0.03%. 2) Landslide vulnerability zonation based on slope condition is divided into 3 zones: Zone A with an area of 20,901m, Zone B with an area of 4642m. And Zone C with an area of 14,614m.

Disaster risk assessment of educational infrastructure in mountain geographies using PROMETHEE-II

This study delves into the crucial realm of disaster risk assessment pertaining to educational infrastructure in mountain geographies, employing the PROMETHEE-II (Preference Ranking Organization Method for Enrichment Evaluations) technique. The study was carried out in Central Kashmir which is susceptible to various hazards, making the assessment of risks to educational facilities imperative for ensuring the safety and resilience of these crucial establishments. The AHP technique was used to assess the seismic, flood and landslide vulnerabilities of the sample institutions in the study area. The vulnerabilities were then treated against the preparedness levels using the PROMETHEE-II model. The findings reveal that out of 620 sample institutions, about 215 institutions are having high disaster risk and only about 60 sample institutions fall in low-risk category. Through the application of this method, this research aims to provide a comprehensive understanding of the vulnerabilities and potential risks faced by educational infrastructure in mountainous areas. The outcomes of the study will contribute significantly to enhancing disaster preparedness, mitigation strategies, and resource allocation for safeguarding educational institutions in these challenging terrains.