Landslide Vulnerability Research Articles

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.

Spatial-Based Landslide Vulnerability Index Assessment in Bogor Area, Indonesia

Within the concept of disaster risk, vulnerability is one of the key determinants. It acknowledges the degree of unsafe conditions in a susceptible zone so that mitigation measures and disaster resilience can be enforced. Bogor area is the most susceptible region to landslides with more than 485 landslide events since 2012. However, there is still inadequate information on its vulnerability to landslides, as the risk reduction challenge per se, is a long-run task. Correspondingly, this study aims to measure the degree of vulnerability to landslides in Bogor area from four focal points (i.e.: social, physical, economic, and environmental dimensions), through the proxy of Vulnerability Index (VI). This study employs a GIS-based spatial analysis on a sub-district level. The result shows that although having high records on landslide events, Bogor area mainly retrieves low VI. In general, Bogor area has low VI [0.347–0.454] on 26 sub-districts. Moreover, 13 sub-districts attain medium VI (0.454–0.562], and 7 sub-districts achieve high VI (0.562–0.670]. Sub-districts that have high VI are: Bogor Tengah, Bogor Barat, Bogor Selatan, Cibinong, Bojonggede, Ciomas, and Bogor Utara; which are mainly promoted by the high indexes on social and physical vulnerabilities. Nevertheless, further study is still needed to extend the knowledge of relationship between landslide susceptibility and this vulnerability result, by using a more extensive and longer data series. That is especially in accord with taking the appropriate mitigation measures in spatial planning and landslide risk management.

Knowledge of disaster safe education at landslide-prone schools in Tawangmangu, Indonesia

Natural disasters are phenomena that cause losses to humans. Therefore, research on disaster education must be intensified in areas where natural disasters frequently occur. Tawangmangu is an area that is prone to landslides. This research aims to analyze landslide vulnerability in Tawangmangu Sub-District, determine the distribution of schools in landslide-prone areas, and the school community’s knowledge of the Disaster Safe Education Unit (SPAB). This research is qualitative research based on literature studies, school locations, landslide disaster vulnerability data as well as policies and history of SPAB implementation in Tawangmangu. Determining the research location based on the level of landslide vulnerability in Tawangmangu which shows that most of the area is in the high vulnerability category. The research results show that the actual condition of schools prone to landslides in Tawangmangu is 54%, but individual knowledge about SPAB is still very low. The SPAB program needs to be implemented in schools prone to landslides in Tawangmangu Sub-District.

Geomorphology mapping and landslide susceptibility for disaster risk reduction and sustainability environment in the Sub DAS Maspo, Mt. Welirang, East Java, Indonesia

Landslides are still problematic for areas with rough surface shapes and high rainfall. Landslides are still causing human life losses, so mitigation efforts for disaster risk reduction must be prioritized. The purpose of this study is geomorphological mapping and landslide hazard mapping as a basis for disaster risk reduction efforts. The method used integrates remote sensing data using geographic information systems. Geomorphology mapping is processed and analyzed using four geomorphological approaches, while for landslide susceptibility maps are analyzed using the Weight of Evidence (WoE) method using existing landslide variables and 17 landslide control factors. The geomorphological mapping results show that in the Maspo sub-watershed, there are 25 landform units based on geomorphological mapping. There are five classes of landslide vulnerability indices, i.e very low index (0.001-0.341) namely 9.38%; low index (0.341-0.471) that is 26.18%; moderate index (0.471-0.584) that is 28.49%; high index (0.584-0.741) that is 23.94% and very high index (0.714-1) that is 12.02%. The accuracy of the landslide hazard map results is 88%, indicating that the landslide hazard map has good accuracy. Dominant landslides of 26.98% and 23.59% occurred in the HS2V1/ME landform, which is the middle slope of the steep hills of the lava eruption of the 2nd Mt. Welirang with moderate geomorphic processes and HS2V1/VSE which is the middle slope of the steep hills of the 2nd eruption of Welirang volcano with the process strong geomorphism. The results of geomorphology mapping and landslide susceptibility can be used as an information base for disaster risk reduction strategies and sustainable environmental management.

Landslide potential mapping in Pituruh District of Purworejo by using geographic information system

Located in Purworejo Regency, Pituruh is an area that prone to a landslide. Disaster mapping was a strategy that has been done so far by the government institutions and academia. According to its topography, Purworejo has been dominated by slope with more than 30 degree. The creation of landslide vulnerability map was carried out through several stages, namely, calculating rainfall at 6 stations located in Pituruh district, then producing average rainfall data to be used as a reference to create a rainfall map, then overlay process. Referring to the 4 parameters used, slope became an important parameter to determine the level of landslide vulnerability in Pituruh. The development of Geographic Information Systems can provide various types of spatial data related to the earth’s surface, and has the ability to perform accurate spatial analysis. Mitigation aims to reduce risks that have the potential to become disasters or reduce the effects of disasters. This study represents the level of vulnerability in Pituruh district by classifying the existing level. The results of this study have 5 classifications with different areas, namely highly vulnerable that covers 240.27 ha, moderately vulnerable for about 2237.81 ha, moderate with 3322.68 ha, vulnerable by 3040.14 ha, and not vulnerable area of 24.72 ha.

Assessment of Landslide Vulnerability in Urban Areas Using GIS and Remote Sensing: A Study in Ambon City

Slope stability and land movements, commonly referred to as landslides, are natural hazards that involve the shifting of materials like soil, rock, and debris, primarily caused by the force of gravity. This research utilized both qualitative and quantitative approaches, focusing on spatial analysis by examining primary and secondary data derived from satellite imagery, observations, and pertinent institutions. Processing of the collected data using specialized software like Global Mapper 20, ArcGIS 10.8.1, and ER Mapper 8.1. The findings of this investigation disclosed that a significant portion of Ambon City, roughly 51.63% of its area, exhibited high susceptibility to landslides. Conversely, only about 16.26% of the total area demonstrated very low or low vulnerability. Similar trends were observed in urbanized regions, where the majority, around 39.01%, were classified as highly vulnerable (Z-4). In contrast, approximately 35.09% showed very low vulnerability (Z-1), and 11.89% depicted low vulnerability (Z-2). The study's findings clearly highlight a critical situation in Ambon City, where a substantial 89% of its territory, characterized by mountainous landscapes, is experiencing a markedly increased frequency of landslides. Given these concerning insights, it is absolutely essential for government authorities to engage in rigorous spatial planning. This should involve redirecting development efforts towards areas identified as safer, away from high-risk zones. Furthermore, the government must enforce and adhere to policies that not only mitigate landslide risks but also promote sustainable development, ensuring the long-term safety and resilience of Ambon City against such natural disasters.Keywords: Mapping, Landslide, Vulnerability, Build Area

Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology

Enhancing Landslide Vulnerability Mapping Through Automated Fuzzy Logic Algorithm-Based Methodology

Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province

Landslides occur when masses of rock, debris or soil move due to various factors and processes that cause land movement. The Taba Penanjung-Kepahiang route is one of the areas in Bengkulu Province that is highly prone to landslides. This causeway is the only fastest land route connecting the Bengkulu-Kepahiang area. In recent years, the road area has often been cut off due to landslides and fallen trees, which have caused road access to be cut off and obstructed and claimed lives. This study uses a Machine Learning (ML) and GIS approach with Variable Frequency Ratio using 16 independent factors obtained from the spatial database and DEM, which correlate with landslide events. This research aims to gain an in-depth understanding of the factors that cause landslides. In addition, the research focus is the development of a Disaster Mitigation Model to design and implement effective strategies to reduce the risk and impact of landslide disasters through in-depth analysis The dependent factor is the location of the landslide from the historical landslide area for the last five years, with a distribution of 70/30%. Furthermore, frequency ratio is used to analyze the correlation between conditioning factors and historical landslides. Then, the independent and dependent factors were normalized to create a landslide susceptibility map. Frequency Ratio (FR) indicates the likelihood of an event occurring, with drainage density (FR= 0.69), shear wave velocity (Vs30) (FR= 0.66), slope (FR= 0.60), and rainfall (FR= 0.55). The output of the processed data is in the table below.

Pembuatan Peta Resiko Bencana di Desa Kubang Tangah Kota Sawahlunto dengan Metode Arc Toolbox

Landslide movement is the activity of a balance disturbance process that causes the movement of masses of soil and/or rock from higher to lower areas. The cause of landslides is that the driving force on the slope is greater than the resisting force. Several factors cause landslides such as rainfall, steep slopes, soil density, rock type, land use and vibration. Sawahlunto City is one of 19 cities/regencies in West Sumatra Province that has a high level of vulnerability to landslides. Areas prone to landslides can be identified by utilizing remote sensing data and geographic information systems. This service is carried out by making maps of areas prone to landslides using parameters that cause landslides, including rainfall, soil type, height, slope and land use. These parameters will be processed and analyzed with the help of the ArcToolbox method, where ArcToolbox can be used to detect landslide areas in Kubang Tangah Village. The analysis process is carried out by using the spatial analysis tools feature found in ArcGIS software. Community service produces a slope map that can illustrate the vulnerability of landslides. Obtain results in the form of a landslide vulnerability level map which has 4 vulnerability classes, including non-prone vulnerability class, low vulnerability class, medium vulnerability class, and high vulnerability class.

Landslide risk management using geospatial technique: comparative insights of China and Indonesia

Landslides are defined as the movement of soil and rocks that form slopes. Landslides can cause environmental damage, property losses, and deaths for people in disaster-prone areas. This study aims to review and compare landslide risk management patterns in China and Indonesia from research conducted in 2019-2023. The method used in this study is a Systematic Literature Review (SLR). While searching for literature using Scopus, Mendeley has a publication period of 2019-2023. The research findings show that disaster risk management also focuses on more than community knowledge in disaster emergency response. However, other elements need attention, namely road sections most vulnerable to landslides, slope conditions, river density, land use, GIS, resources, community participation, and training. In Fengjie County, China, landslide vulnerability is a significant problem, with about 70% of areas in the vulnerability zone very high. In Pengasih Sentolo district, Indonesia, nine villages are included in the very high-risk site, showing significant landslide vulnerability. The integration and application of GIS technology have greatly assisted in assessing landslide susceptibility and identifying high-risk zones. Conclusion: The case study in Fengjie County, China and the study in Pengasih Sentolo District, Kulon Progo, Indonesia, emphasize the importance of using geospatial techniques, particularly GIS, for landslide risk assessment.

Landslide vulnerability mapping using multi-criteria decision-making approaches: in Gacho Babba District, Gamo Highlands Southern Ethiopia

The landslide has been a life-threatening natural disaster in most districts of Gamo Highlands. This study was conducted to assess the status of landslide vulnerability in the Gacho Baba district of the Gamo zone in southern Ethiopia. The Geographic Information System Analytical Hierarchy Process and Weighted Linear Combination multi-criteria decision-making approaches were applied. Eight causative factors of landslide, namely, slope, elevation, aspect, distance from the stream, drainage density, soil type, distance from the road, and land use/cover were considered. The weight values of each causative factor were determined by previous studies, field observations, and experts’ judgment. The calculated weight of each causative factor is the slope (23%), elevation (21%), aspect (8%), stream distance and drainage density each (12%), soil type and road length each (9%), and land use/cover (6%). Moreover, the Consistency Index (0.13) and Consistency Ratio (0.08%) were calculated with acceptable values for comparison to weighted overlay analysis to produce a landslide vulnerability map of the study area. The result shows that the vast majority of the study district (86.6%) falls within a very high to moderate landslide susceptibility class and only (13.4%) falls within a very low and low susceptibility. This indicates that almost all the 11 villages in the district, are found within the high to moderate landslide vulnerability class which alerts responsible community and zonal disaster risk prevention and related offices to take action on the identified causative factors to reduce the occurrences of landslide hazard in the study district.

Comparison of factors influencing landslide risk near a forest road in Chungju-si, South Korea

BackgroundThe study aimed to identify the influential factors required to prepare landslide vulnerability maps and establish disaster prevention measures for mountainous areas with forest roads. The target area is Sancheok-myeon, Chungju-si, where several landslides have occurred in a narrow area of approximately 3 km × 4 km. As the area has the same rainfall and vegetation conditions, the influences of the physico-mechanical characteristics of the soil in accordance with compaction and topographic characteristics could be analyzed precisely.MethodsGeological surveying, sampling, and laboratory testing assessed landslide risk in the study area, and data including unit weight, specific gravity, porosity, water content, soil depth, friction angle, cohesion, slope angle, profile/plan curvature, TWI were obtained. Preprocessing and screening such as min-max normalization and multicollinearity were conducted for the data in order to eliminate overestimation of each factor’s effectiveness. The influence of each factor was analyzed using logistic regression (LR), structural equation modeling (SEM), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM).ResultsAll methods showed that soil depth has the greatest impact on landslide occurrence. Friction angle, slope angle, and porosity were also selected as influential factors, although each method ranked them slightly differently. Topographic factors, such as plan curvature, profile curvature, and the topographic wetness index, had minimal influence. This appears to be because landslides near forest roads are more affected by how well compaction was performed during banking than by the concave or convex shape of the slope. This study presents analysis results for an area with the same rainfall and vegetation conditions; therefore, the analysis of the influence of the physico-mechanical characteristics of the soil and topography was more precise than when comparing landslides occurring in different regions. Our results may be helpful in preparing landslide vulnerability maps.

Landslide Hazard Area Identification Using Smorph-Slope Morphology Method in Manado City

Landsliding is one of the natural process that shape the earth’s surface to achieve slope stability and it will be hazard if they threat human life. This study was conducted to identify potential landslide hazard in Manado region using SMORPH method with digital elevation model (DEM) data. ASTER GDEM data provide more detail landslide information than SRTM data because of the higher spatial resolution. Result shows that 716 ha approximately of land have high landslide vulnerability and 1,176 ha approximately of land have medium landslide vulnerability.

Strategies for Landslide Disaster Mitigation in Baktiraja’s Tourism Zones, Humbang Hasundutan Regency, Indonesia

Mitigation disaster is very important to develop region in many areas. This study addresses landslide mitigation for tourism development in the Baktiraja District, Humbang Hasundutan Regency. Four parameters, including slope inclination, rainfall, soil type, and land use, reveal four levels of landslide vulnerability: low, moderate, high, and very high. The method in this study used spatial analysis using ArcGIS (Geographic Information Systems) 10.8 identifies Tipang Village, Simangulampe Village, and Marbun Tonga Marbun Dolok Village as areas with the highest vulnerability, covering 77.60% of the Baktiraja District. MSP+DM analysis categorizes the tourism potential in these villages as "Embryonic." To elevate this status to "Growing" and "Self-sustaining," emphasis on improving marketability indicators through enhanced packaging, diverse promotional models, and an extensive marketing network is needed. Sustainable development efforts by the local government and active community participation in preserving local wisdom, natural attractions, and cultural heritage are crucial. Recommendations include regular updates on disaster vulnerability data, establishment of evacuation routes and shelters, early warning systems, and community training. Regulations on open land use for physical development need attention, and the government should allocate budget for tourism infrastructure. Ensuring synergy among the government, local community, and tourism stakeholders is key to sustainable tourism development in this area.

Landslide susceptibility mapping using the analytical hierarchy process and the Geographic Information System along the Agadir Ida Ou-Tanane province, Morocco

This study focuses on evaluating the susceptibility to landslides in the Agadir Ida Ou-Tanane province following the significant seismic event on September 8, 2023, employing Geographic Information System (GIS) and Analytical Hierarchy Process (AHP). The region, characterized by tectonic activity and diverse lithology’s such as clay, limestone, and alluvium, coupled with steep slopes, is inherently prone to landslides. Utilizing the AHP methodology, we integrate ten factors influencing landslide vulnerability: lithology, slope, earthquakes zone, rainfall, land cover (LULC), distance from faults, hypsometry, drainage, distance from road, and aspect to map susceptible areas effectively. Pairwise comparison matrices and expert assessments were employed to establish the relative significance of these factors, with the reliability of judgments evaluated through the calculation of the consistency ratio (CR). The resulting map depicting landslide susceptibility illustrates a spatial continuum from very low to very high susceptibility areas. Given the active tectonic setting of the region, lithology and slope emerge as the predominant factors influencing susceptibility. Model validation was conducted using observed landslide occurrences, enhancing the applicability of this mapping for land-use planning and risk management purposes.

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area’s disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation’s findings mostly agreed with the survey’s actual findings. The study’s findings demonstrated that: (1) the FCNN model’s AUC value was 0.910, while the I-AHP model’s AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened.

An integrated approach of machine learning and remote sensing for evaluating landslide hazards and risk hotspots, NW Himalaya

Landslides have become more frequent in the last decade in the NW Himalayan region, threatening people and damaging infrastructure. The study presented here aims to identify landslide hazards and risk hotspots in the NW Himalaya. The area is strategically important due the many holy pilgrimage sites and major hydropower projects. A Multilayer Perceptron (MLP) was used to generate the landslide susceptibility map, which was combined with the rainfall intensity map to create the hazard map. To determine the spatial landslide vulnerability, land use and land cover components were taken into account with their respective monetary values. The landslide risk map is the combination of landslide hazard and vulnerability maps, showing that ∼5% of the area falls in high and very high-risk zones, ∼6% in moderate, ∼47% in low, and ∼42% in very low landslide risk zones. High to very high landslide risk zones are mainly confined to Uttarkashi and its surroundings in the southwestern part of the study area, as well as the Tehri, Karanprayag, and Pithoragarh regions in the southern area. Societal risk was also analyzed and revealed that ∼53% of the human population resides in high to very high landslide risk-prone areas. The findings of this study will be beneficial for promoting sustainable development and safe urbanization in the Himalayan region, if used for planning.

Regional Rainfall-Induced Landslide Risk Assessment Using Susceptibility Mapping and Unexpected High-Intensity Rainfall

Landslides are one of the most common natural hazards in Malaysia, and besides geological conditions, rainfall intensity and duration are critical factors in assessing landslide risk. This study investigates the impact of daily rainfall variation on landslide susceptibility mapping and risk assessment. For this study, drainage, road, geological, and rainfall data were considered as key. The results show that rainfall significantly contributes to landslide risk increment by keeping geological and other triggering factors constant. Landslide vulnerability due to high-intensity rainfall was high on steep slopes causing high susceptibility compared to low-slope regions. The results also highlighted the relationship between triggering and geological factors in susceptibility assessment. Additionally, a comprehensive analysis through big data sets would help understand the spatio-temporal relationship between rainfall intensity and other landslide factors. Spatio-temporal data resolution will improve landslide susceptibility mapping and prediction accuracy to protect infrastructure and communities from potential landslide disasters in tropical climates.

Mitigasi Daerah Rawan Bencana Longsor Di Kota Batu Dengan Menggunakan Metode Analisis SIG

Landslide is one of the natural damages caused by mass movement in a natural landscape. The occurrence of landslides causes damage that provides direct and indirect risk effects. Efforts made to reduce the effects of risk can be done by mitigation planning. Batu City is one of the cities located at the foot of Mount Panderman, located at 700-1100 meters DPL. Research was conducted to determine landslide prone areas based on GIS which can then be carried out a sustainable mitigation process. In this area, research was conducted by utilizing GIS in the form of mapping landslide prone areas. Batu city has a predominantly very steep slope that causes high landslide prone areas. Therefore, GIS method can be utilized in mapping landslide vulnerability in Batu city as a map editor and data processor of spatial area which includes rainfall data, soil type data, slope data, and land cover type data.

Geological Condition and Landslide Vulnerability Zonation Using Frequency Ratio Method in Salaman District, Magelang Regency, Central Java Province

Geological Condition and Landslide Vulnerability Zonation Using Frequency Ratio Method in Salaman District, Magelang Regency, Central Java Province