Landslide Mitigation Research Articles

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.

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.

Landslide life-loss risk quantification based on historical fatalities

Life-loss risk estimates inform decisions that have dramatic impacts on individuals and communities in landslide hazard zones. Literature provides methods for making these estimates through multiplication of site-specific parameters. However, there is no method in the literature for calibrating those estimates, and regional context is needed to make efficient, fair, and affordable decisions. Therefore, we developed methods to quantify risk to individuals and groups based on regional historical fatality and hazard zone population data. The methods can be used to evaluate the accuracy of large sets of site-specific risk estimates, to assign quantitative risk values to buildings and hazard zones at a regional scale, to develop a long-term regional budget for landslide risk management, or to inform selection of a risk tolerance threshold that can inform where limited resources are invested in landslide mitigation. An example of the methods is provided for a case study of residential landslide risk in British Columbia, Canada. It estimates that there are between a few hundred and a few thousand people living with individual risk exceeding 100 micromorts (1 in 10,000) per year, and between 70 and 370 landslide hazard zones with annual probable life loss greater than 1E-3 (1 death in 1,000 years), based on a historical fatality rate of 0.4 to 1.4 deaths per year across the entire province.

Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China

Abstract. The occurrence of shallow loess landslides induced by prolonged heavy rainfall is prevalent in loess-dominated regions, often leading to property damage, human casualties, and sediment pollution. Developing an accurate prediction model for shallow landslides in loess areas is crucial for effective landslide mitigation. In 2013, prolonged heavy rains from 19–25 July triggered mass sliding-flow loess landslides in Tianshui, China. Landslide data, along with the characteristics of the sliding-flow loess landslides, were obtained through extensive field investigations and remote sensing interpretations. The sliding-flow loess landslide event demonstrated clustering, high density, small areas, and long travel distance. The depth of the sliding surface is correlated with the saturated layer resulting from rainfall infiltration; it is typically less than 2 m deep and negatively correlated with slope steepness. Based on the common characteristics of shallow loess landslides, the mechanisms involved in the sliding-flow landslide are proposed. The Revised Infinite Slope Model (RISM) was introduced using an equal differential unit method to address deficiencies when the safety factor remains constant or increases with increasing slope greater than 40°, as calculated using the Taylor slope infinite model. The relationship between the critical depth and the slope of the shallow loess landslide was determined. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides for different slopes was constructed and combined with the characteristics of rainfall infiltration for use in forecasting regional shallow loess landslides. Additionally, the influence of loess strength on the shallow loess landslide stability was analyzed. The shallow loess landslide stability responds to slope and cohesion but is not sensitive to the internal friction angle.

Implementation of the Snakes and Ladders Educational Game as a Landslide Mitigation Education Media and Its Effect on Student Knowledge

Implementation of the Snakes and Ladders Educational Game as a Landslide Mitigation Education Media and Its Effect on Student Knowledge

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

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

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

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

Integrated Landslide Mitigation Model in Physics Concept of Environmental Pollution and Alternative Energy

This research integrates landslide disaster mitigation into physics education, with a focus on environmental pollution and alternative energy for high school students. The research aims to address the frequent landslides in Indonesia, particularly in Central Bengkulu Regency, which can cause significant disruptions. The objective is to develop a model for integrating disaster mitigation into physics education at MAN Insan Cendekia Central Bengkulu. The research method employed is Research and Development (R&D) using the 4D model, limited to the design stage. The research subjects were students in class XI-1 specializing in Physics at MAN Insan Cendekia. The analysis of disaster potential in Central Bengkulu indicates a need for disaster mitigation education. The Butterfly Webbed Concept Map was developed to integrate physics material with disaster mitigation education. It provides a visual representation of the relationship between environmental pollution, alternative energy, resource utilization, and landslide disaster mitigation. This integration can be applied in physics learning for disaster risk awareness for students.

Comprehensive risk management of reservoir landslide-tsunami hazard chains: a case study of the Liangshuijing landslide in the Three Gorges Reservoir area

AbstractLandslide-induced tsunamis in reservoirs often pose greater risks than landslides themselves. Existing studies and mitigation practices have prioritized increasing landslide stability to meet safety standards, incurring high costs and lacking sustainability. More than 5000 landslides have developed along the Three Gorges Reservoir banks in China, and some may trigger tsunamis. To effectively and sustainably manage risk of landslide-tsunami hazard chains (LTHC), we propose a new framework (based on quantitative risk analysis) involves decision-making based on risk characteristics and local economies, a strategy for selecting optimal risk control plan through cost–benefit analyses that consider long-term effectiveness and adverse effects, and dynamic management through iterative risk reassessment-recontrol cycles. The Liangshuijing landslide accelerated in 2009, posing a tsunami threat to the Yangtze River and coastal residents. Post-mitigation, the risks under four mitigation plans and combined scenarios were quantitatively evaluated. The mass removal plan reduces total risk by approximately 68%, less than the 80% reduction from the anti-slip pile plan, but with a 30-fold greater benefit–cost ratio (BCR). The surface drainage and monitoring network plans show limited long-term cost-effectiveness due to short measure lifetimes, however, their short-term BCRs exceed those of the mass removal plan due to lower direct costs. According to comprehensive long and short-term analyses, combined plans involving mass removal are the most cost-effective. Our results reveal how to select appropriate strategies for managing LTHC risk, how landslide mitigation controls tsunami risks, and how time-related parameters affect solution effectiveness. Our findings show that partially mitigating risks may provide more cost-effective and sustainable management of LTHCs than eliminating risks to negligible levels.

High-resolution landslide mapping and susceptibility assessment: Landslide temporal variations and vegetation recovery

In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide’s temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area.

Landslide Mitigation at The Bagong Dam Abutment, Trenggalek District

Landslides occurred continuously from July 2022 until July 2023, disrupting the construction of the Bagong Dam abutment. Geologically, the foundation of the Bagong Dam consists of a fairly thick colluvial layer, which is prone to landslides. So, the analysis of landslide mitigation at the Bagong Dam abutment is needed. The slope stability analysis carried out by Fellenius and Bishop method, then the slope modeling was carried out using Geostudio software. The analysis results on the existing slopes produced a safety factor of 0.987 (1.07) for the Fellenius method and 1.042 (1.07) for the Bishop method. These safety factors indicate that the existing slope is unstable and slope failure is likely to occur. In the first alternative countermeasure analysis, the slope safety factors for the cross-section of the dam at STA 0+625 were 1.715 for upstream and 1.338 for downstream; at STA 0+641, 1.321 upstream and 1.306 downstream; and for the longitudinal section of the dam, 1.525. All these safety factors greater than 1.25, indicating that the slope is stable. In the second alternative countermeasure, the slope safety factors obtained for the cross-section of STA 0+641 were 1.362 for upstream and 1.386 for downstream, and 1.657 for the longitudinal section. These safety factors are also greater than 1.25, which indicates the slope is in stable condition. The additional cost for implementing the first alternative countermeasure is 73.9 million, while for the second alternative is 35.7 million. So that, the second alternative countermeasure is the best choice by the multi-criteria decision-making analysis results.

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.

Evaluasi Faktor-Faktor Penyebab Longsor dan Kesesuaian Mitigasi

Management and maintenance of slopes require proper knowledge of slope conditions so that appropriate mitigation measures can be implemented. The activities required for the management of slopes include slope inventory, slope inspection, slope risk level assessment, and risk mitigation measures. The objective of this study was to observe the slope conditions and causes of past slope failures, determine the appropriate mitigation measures, and analyze the suitability of landslide mitigation measures and causes of slope failures. The research locations are road segment No. 36 (Kota Lahat – Simpang Air Dingin), No. 37 (Simpang Air Dingin – Pagar Alam), and No. 38 (Pagar Alam – Tanjung Sakti – batas Bengkulu). The research was initiated by analyzing data collected by the South Sumatra National Road Management Agency (BPJNSS) from 2018 to 2021. The field observation was conducted on ten locations where the slope had been repaired after slope failure events. The suitability of mitigation with the factors that cause landslides was analyzed using the slope management system method suggested by the Ministry of PUPR. The results showed that most slope failures were triggered by high-intensity rain falling on slope surfaces containing high humidity due to previous rainfall. The common types of slope mitigation are reducing the forces that cause movement, increasing the resisting forces by controlling seepage, and the use of anchors.

A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network

Landslide susceptibility assessment (LSA) is vital for landslide mitigation and management. Existing LSA methods only consider local environmental characteristics associated with landslides, while neglecting the characteristics of remote yet interconnected geographic environments. In addition, the datasets for landslide samples do not consider the equilibrium among various geographic environmental characteristics. Ultimately, the landslide susceptibility mapping (LSM) generated is unreliable. We propose an LSA method based on the similarity of geographic environments and the equilibrium of landslide samples. We selected the Bailong River Basin (BRB) as the study area. Our proposed LSA method has four steps: (1) We constructed slope units using the curvature watershed method (CWM) based on the digital elevation model (DEM) to generate geographic nodes and selected 12 landslide influencing factors (LIFs) through multicollinearity analysis to extract geographic node features. (2) We constructed an association graph of geographic environment similarity constraints based on geographic node features using the cosine similarity method to correlate landslide and geographical environments. (3) We constructed sample datasets for an equal number of landslide and non-landslides based on various geographic environments. (4) We constructed a graph neural network (GNN) model based on the association graph to predict the susceptibility of unknown geographic nodes, obtaining a reliable LSM. Compared with random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) models, our proposed model framework outperforms in the LSA. The area under the receiver operating characteristic curve (AUC) values improved by 6.2%, 5.7%, and 1.8%, respectively. Specifically, our model can predict landslides across space in areas with few samples. Our proposed model offers an effective approach and essential technical support for landslide disaster investigation and prevention.

Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall.

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

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

Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area

Changes in land use significantly impact landslide occurrence, particularly in mountainous areas in northern Thailand, where human activities such as urbanization, deforestation, and slope modifications alter natural slope angles, increasing susceptibility to landslides. To address this issue, an appropriate method using soilbags has been widely used for slope stabilisation in northern Thailand, but their effectiveness and sustainability require assessment. This research highlights the need to evaluate the stability of the soilbag-based method. In this study, a case study was conducted in northern Thailand, focusing on an area characterised by high-risk landslide potential. This research focuses on numerical evaluation the slope stability of soilbag-reinforced structures and discusses environmental sustainability. The study includes site investigations using an unmanned aerial photogrammetric survey for slope geometry evaluation and employing the microtremor survey technique for subsurface investigation. Soil and soilbag material parameters are obtained from existing literatures. Modelling incorporates hydrological data, slope geometry, subsurface conditions, and material parameters. Afterwards, the pore-water pressure results and safety factors are analysed. Finally, the sustainability of soilbags is discussed based on the Sustainable Development Goals (SDGs). The results demonstrate that soilbags effectively mitigate pore-water pressures, improve stability, and align with several SDGs objectives. This study enhances understanding of soilbags in slope stabilisation and introduces a sustainable landslide mitigation approach for landslide-prone regions.

Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively.

Limit Equilibrium Method Based Slope Stability Analysis of One of the Most Highly Landslide Susceptible Region of Guwahati

Abstract: Guwahati, situated in a hilly region, faces the constant threat of landslides due to its susceptibility to high rainfall. The city has a history of several instances of landslides, causing substantial loss of life and property. The Assam State Disaster Management Authority (ASDMA) has identified at least 366 locations within the city as highly seismic-prone areas. In response to this, a comprehensive analysis of slope vulnerability has been initiated. One of the targeted sites, recently affected by landslides, is undergoing thorough examination using the Limit Equilibrium Method (LEM). To achieve this, a detailed survey of the slope profile has been conducted, including the collection of geological data and soil samples. The geotechnical parameters are being meticulously analysed based on the obtained soil test reports, considering factors such as drainage conditions and historical rainfall intensity data. This crucial information serves as input for a LEM-based software, which is commonly available for slope stability analysis. The software utilizes sophisticated algorithms to assess the vulnerability of the slope under varying rainfall intensities, offering valuable insights into potential landslide risks. By combining field surveys, geotechnical testing, and advanced software analysis, this comprehensive approach aims to provide a holistic understanding of slope stability, aiding in the formulation of effective strategies for landslide mitigation in the identified vulnerable regions of Guwahati. This initiative aligns with the broader goal of enhancing disaster preparedness and resilience in the face of natural hazards in the region.