Landslide-prone Areas Research Articles

Mitigating Landslide Hazards in Qena Governorate of Egypt: A GIS-based Neutrosophic PAPRIKA Approach


 This paper presents a novel approach to landslide susceptibility assessment in the Qena Governorate, Egypt, integrating the neutrosophic Multi-Criteria Decision-Making (MCDM) method, the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA), and the ArcGIS weighted overlay technique. The research focuses on the quantification and prioritization of eight criteria: slope, aspect, proximity to road, soil type, proximity to river, land cover, elevation, and Lithology. These factors are evaluated under the uncertainty and indeterminacy of the neutrosophic environment by employing the PAPRIKA method. The results of the analysis are visualized and interpreted using ArcGIS weighted overlay, offering spatially explicit insights into the landslide-prone areas. This study's outcomes could significantly contribute to the overall understanding of landslide hazards in Qena, promoting better hazard management and mitigation strategies. The results of the study demonstrated varying levels of landslide susceptibility within the study area: 2% of the area was identified as having Very High Susceptibility, 17% presented High Susceptibility, 28% had Moderate Susceptibility, 44% indicated Low Susceptibility, 8% showed Very Low Susceptibility, and 1% with Practically No Susceptibility. These findings can aid local authorities and policy-makers in prioritizing areas for mitigation efforts based on their susceptibility to landslides. The study also incorporates a sensitivity analysis, exploring ten different scenarios to ensure the robustness and reliability of the results. In the first scenario, we adhere to our initial criteria weights to represent the current situation. In the second scenario, all criteria are accorded equal significance to check the model's steadfastness when no one criterion outweighs another. Scenarios three to ten each elevate the weightage of one criterion, allowing for a comprehensive understanding of each individual criterion’s influence on the decision-making process. This systematic alteration helps pinpoint the salient features driving landslides and aids in fortifying our mitigation strategies.

Determine of Potential Evacuation Locations as Disaster Mitigation Against Landslides

Bandar Lampung City has a lowland topography, which is quite diverse in valleys to ridge hills. The city’s development made the face of the city of Bandar Lampung turn into a dense residential area between the hills. Landslide is one of the disasters that often occur in Indonesia. About 15 sub-districts that are prone to landslides in Bandar Lampung, including West Telukbetung, East Telukbetung, South Telukbetung, Bumiwaras, Panjang, Kedamaian, North Telukbetung, Tanjung Karang Pusat, Enggal, Tanjung Karang Barat, Kemiling, Langkapura, Kedaton, Rajabasa and Sukabumi (BPBD, 2018). Research about landslide-prone areas with a geospatial approach aimed at disaster mitigation efforts. The study results will be necessary information about disaster mitigation to minimize loss of life and material. The geospatial approach integrates landslide threat maps with road networks and public facilities. Also, the landslide threat area’s accessibility and affordability functions can be arranged to secure the city’s priority location. The preparation of a landslide threat map begins by compiling an entire dataset in spatial data information from various sources. Whole datasets are arranged based on parameters that affect landslides. Each parameter has a different weight and is summarized in the Overlay method. It is hoped that this research can be used as a reference in disaster management and spatial planning in Bandar Lampung in the future. The determination of public facility services using the Analytical Hierarchy Process (AHP) method.

산사태 취약지역 정밀점검표 개발

Since 2012, a comprehensive investigation has been conducted to identify areas prone to landslides, with the objective of implementing preventive measures to mitigate the risk. To ensure effective monitoring, field inspections should be conducted at least twice a year in the designated landslide-prone areas. This study aimed to develop an inspection checklist for field assessments in landslide-prone areas by determining the key indicators and their respective weightings in terms of landslide risk. The effectiveness of the detailed inspection checklist was verified by applying it to landslide-prone areas. The analysis showed that the most useful way to categorize landslides was into three main types: soil slopes, rock slopes, and debris flows. The inspection items showed a high weightage towards dynamic vulnerability. The application of inspection checklists to landslide-prone areas suggests that implementing class-based criteria (Classes A, B, C) is a rational approach for the management of these landslide-prone areas.

Suitability Evaluation of Land Use/ Land Cover (LULC) Towards Landslide Prone Areas in Structural and Volcano Landform

Suitability Evaluation of Land Use/ Land Cover (LULC) Towards Landslide Prone Areas in Structural and Volcano Landform

Pemetaan Daerah Potensi Longsor di Kecamatan Leihitu Barat, Kabupaten Maluku Tengah, Menggunakan Metode Slope Morphology (SMORPH)

Landslide prone area mapping is an important step in mitigation and natural disaster management efforts in West Leihitu Sub-district, Central Maluku Regency. This research aims to map landslide prone areas in West Leihitu District, Central Maluku Regency, using Slope Morphology (SMORPH) method. This research uses DEMNAS data. The Slope Morphology (SMORPH) method was used in this research to overlay the slope map and slope shape to produce a map of landslide prone areas in West Leihitu Sub-district. The results showed that very low landslide potential class has an area of 465.40 ha (4.47%), low landslide potential class has an area of 4,232.96 ha or 40.63%, medium landslide potential class has an area of 2,524.30 ha or 24.23% and high landslide potential class has an area of 3196.19 ha or 30.68%. The results of this research are expected to be useful for the government and community in West Leihitu Sub-district, Central Maluku Regency in mitigating landslides in the future.

Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis

Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities. In this context, research on landslide susceptibility modelling has become a topic of relevance and is in constant evolution. Though various machine-learning techniques (MLTs) have been identified for landslide susceptibility modelling, the uncertainty inherent in the models is rarely considered. The present study attempts to quantify the uncertainty associated with landslide prediction models by developing a new methodological framework based on the ensembles of the eight MLTs. This methodology has been tested at the highlands of the southern Western Ghats region (Kerala, India), where landslides have frequently been occurring. Fourteen landslide conditioning factors have been identified as part of this study, and their association was correlated with 671 historic landslides in the study area. The study used four ensemble models such as the mean of probabilities, the median of probabilities, the weighted mean of probabilities, and the committee average. The weighted mean of probability was proved to be the best model based on the average of 800 standalone MLTs, viz., receiver operating characteristics, true skill statistics, and area under curve with corresponding validation scores. Thereafter, an uncertainty analysis was carried out on the coefficient of variation. A confident map was generated to represent the distinct zonation of landslide susceptibility areas with definite uncertainty scales. Nearly 74% of the past landslides fall in the higher susceptibility-low uncertainty category. It is also inferred that such micro-level zonation based on MLTs may improve the efficiency of landslide susceptibility maps and may help in accurately identifying landslide-prone areas in the future. The confident maps thus generated can be used as a ready reference to the planners for the formulation of landslide adaptation strategies at micro-scales.

LANDSLIDE DISASTER RISK REDUCTION STRATEGY BASED ON COMMUNITY EMPOWERMENT (Case Study in Kuta Cot Glie District, Aceh Besar District)

Lamkleng Village, Kuta Cot Glie District, is located in the Selawah valley area of Aceh Besar, which is a landslide-prone area. So far there has been no effort to reduce disaster risk that has been carried out optimally. This study aims to obtain alternative strategies for reducing the risk of landslides based on community empowerment in Lamkleng Village, Kuta Cot Glie District, Aceh Besar. This research uses a qualitative approach (action research). In this study using a qualitative descriptive method, using SWOT analysis (Strengths, Weaknesses, Opportunities, Threats), and using QSPM (Quantitative Strategic Planning Matrix) analysis. The results of this study are the social conditions of the people of Lamkleng Village, Kuta Cot Glie District, Aceh Besar as farmers and traders.

Penentuan Kedalaman dan Sudut Kemiringan Sesar Grindulu di Pacitan Menggunakan Metode Crow Search Algorithm (CSA) pada Data Self-Potential (SP)

Self-Potential (SP) is a geophysical survey method that is relatively easy and inexpensive. Interpretation of SP data can be used for various purposes such as the detection of landslide-prone areas, exploration of various types of minerals, and identification of the parameters of a fault or crack. In this study, SP data acquisition was carried out in Tambakrejo Village, Pacitan District with a total of 102 measurement data which aims to determine the depth and dip of the Grindulu Fault. SP data acquired in the field needs to be corrected for reference, namely corrections caused by a displacement of the starting point of measurement. This data is then filtered to increase the signal-to-noise ratio (SNR) and sharpen the resulting anomalies. This filtering process is carried out using the ICEEMD (Improved Complete Ensemble Empirical Mode Decomposition) method which is a development of the EMD method. Furthermore, the SP data inversion process to obtain model parameters is carried out by utilizing the CSA (Crow Search Algorithm) method. Based on the anomaly model generated from the SP data inversion process, it can be concluded that the Grindulu Fault was identified at a distance of 803,8 meters from the starting point of measurement with depths ranging from 11,06 to 102,74 meters. Furthermore, based on distance, depth, and anomaly shape data, the dip value can be calculated. The calculation results show that the dip of the Grindulu Fault in the study area is 75.58o. Identification of the Grindulu Fault in the form of depth and dip is very important in efforts to model the fault comprehensively.

Integration of GIS and Machine Learning Techniques for Mapping the Landslide-Prone Areas in the State of Goa, India

A landslide susceptibility map (LSM) assists in reducing the danger of landslides by locating the landslide-prone locations within the designated area. One of the locations that are prone to landslides in India's Western Ghats of which Goa is a part. This article presents the LSMs prepared for the state of Goa using four standard machine learning algorithms, namely Logistic Regression (LR ), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest (RF). In order to create LSMs, a 78-point landslide inventory, as well as 14 landslide conditioning factors, has been used, including slope, elevation, aspect, total curvature, plan curvature, profile curvature, yearly rainfall, Stream Power Index, Topographic Wetness Index, distance to road, depth to bedrock/soil depth, soil type, lithology, and land use land cover. The most pertinent features for the models' construction have been chosen using the Pearson correlation coefficient test and the Random Forest method. The presence of landslides is shown to be strongly influenced by the distance to road, slope of the terrain, and the annual rainfall. The LSMs generated were classified into five levels ranging from very low susceptibility level to very high susceptible. The prediction accuracy, precision, recall, F1-score, area under the ROC (AUC-ROC), and True Skill Statistics (TSS) have been used to analyse and compare the LSMs created using various methodologies. All of these algorithms perform pretty well, as evidenced by the overall accuracy scores of 81.90% for LR, 83.33% for SVM, 81.94% for KNN, and 86.11% for RF. SVM and RF are the better approaches for forecasting landslide vulnerability in the research area, according to TSS data. The maximum AUC-ROC of 86% was achieved by the RF algorithm. The results of performance metrics lead to the conclusion that the tree-based RF approach is most appropriate for producing LSM for the state of Goa. The results of this study indicate that more landslide-prone areas can be found in the Sattari, Dharbandora, Sanguem, and Canacona regions of Goa.

Landslide-prone area retrieval and earthquake-inducing hazard probability assessment based on InSAR analysis

Landslide-prone area retrieval and earthquake-inducing hazard probability assessment based on InSAR analysis

The accuracy of landslide susceptibility map in the regional spatial plan with landslide event in Lima Puluh Kota Regency

Spatial plan is a reference in spatial development in Indonesia. One of the information displayed is landslides susceptibility area. Lima puluh kota regency in province of West Sumatra, which is located in the Bukit Barisan mountains, is a landslide-prone area, so that the regional spatial plan (RTRW) document contains a map of landslide susceptibility areas. This study evaluates the accuracy of the landslide susceptibility map contained in the regional spatial plan with landslide events in the regency until 2021, using the overlay method and then examining with the characteristics that affect it. The results of the study show that not all landslide events are in the locations indicated on the map in the spatial plan. It is necessary to improve the information on the landslide susceptibility map on a regional scale which has been based on the InaRisk map published by the BNPB (National Disaster Management Agency) on a small scale. In order to be more accurate with the causal factors, it is needed to improve the method of landslide susceptibility maps at the regional level. The methods that have been heuristic in nature need to be improved with statistical methods.

Diversity of Arbuscular mycorrhizae in landslide areas of Garhwal Region, Uttarakhand, India

Hill slopes in Uttarakhand Himalaya are observed for their largescale slope destabilization. Landslides are among hydro-geological hazard in the fragile geology of the state which cause huge losses in terms of biodiversity, infrastructure, time and human life. This paper highlights presence and diversity of mycorrhizal species in landslides prone areas in locality of selected highways in the Garhwal region of Uttarakhand. Eight (08) AM fungi, viz., G. macrocarpum, G. fasciculatum, F. coronatum, A. laevis, A. foveata, A. tuberculata, A. sporocarpia and A. myriocarpa were found in 20 large landslide areas. Acaulosporaceae was found dominant family followed by Glomeraceae. Spore density of G. macrocarpum was the highest with 13 spores/100 g soil. The percentage of importance value varied from 7.16 to 21.59. Results of present paper may endorse the use of AM fungi in restoration the unstable soil profiles.

Spatial Analysis of Landslide-Prone Areas in Damer District, Maluku Barat Daya Regency, Maluku Province

Based on historical data on landslide events, Damer District is an area prone to landslides in Southwest Maluku Regency. One of the first steps in mitigating landslides in Damer District is to map areas that have the potential for landslides. The purpose of this study was to determine the spatial distribution of landslide-prone areas in Damer District, Southwest Maluku Regency. This study uses the SMORPH method to identify and classify areas that have the potential for landslides based on the matrix between the shape of the slope and the angle of the slope. This study resulted in 4 levels of landslide potential areas, namely very low, low, medium and high potential. Villages with high landslide potential are Wulur Village and villages with very low landslide potential are Ilih Village. The results of this study also illustrate that the higher the slope accompanied by the formation of convex or concave slopes, the higher the potential for landslides to occur. The results of the research are expected to help the Southwest Maluku Regency government, especially the Damer District government, in an effort to spatial planning based on disaster mitigation.Spatial Analysis

Routing UAVs in landslides Monitoring: A neural network heuristic for team orienteering with mandatory visits

Unmanned aerial vehicles (UAVs) are widely used for surveillance in both civilian and military scenarios. The utilization of UAVs provides an opportunity for monitoring landslide-prone areas by automatically collecting geological information, thereby reducing the risks and the time required to be working in harsh environments. Due to the maximum travel time limit of UAVs and the geographical dispersion of landslide-prone areas, multiple UAVs are dispatched for surveillance tasks, and landslide-prone areas with high emergency priorities require mandatory visits. Here, we investigate a team orienteering problem with mandatory visits (TOPMV) for routing multi-UAVs to monitor scattered landslide-prone areas, with mandatory visits on those in poorly stable states. The proposed TOPMV aims to plan the optimal multi-UAV paths for maximizing the total amount of collected geological information. To solve the TOPMV with a realistic scale, we develop a large neighborhood search (LNS) algorithm embedding a neural network heuristic (NNH), in which the embedded NNH learns to perform adaptive destroy operators through a hierarchical recurrent graph convolutional network (HRGCN). We consider a real-world case study for monitoring of landslide-prone areas in three counties in southern Shaanxi Province, China. Finally, we test the proposed NNH on both small- and large-scale benchmark instances of the team orienteering problem. The experimental results demonstrate that our proposed NNH exhibits higher efficiency and provides better solution quality than state-of-the-art methods, especially in large-scale settings.

Mapping of Optimum Freight Route by Using Hybrid VNS Algorithm to Sustain the Economic Viability of a Landslide Prone Area: A Case Study of Tripura

Mapping of Optimum Freight Route by Using Hybrid VNS Algorithm to Sustain the Economic Viability of a Landslide Prone Area: A Case Study of Tripura

Inferring slip-surface geometry and volume of creeping landslides based on InSAR: A case study in Jinsha River basin

The slip-surface geometry and volume of landslides are fundamental for landslide modeling and mechanism interpretation. The movement of a landslide, which is generally controlled by the slip-surface geometry, can be obtained from interferometric synthetic aperture radar (InSAR) measurements. However, there is a lack of a general approach for inferring the slip-surface geometry and volume of landslides through the InSAR-derived deformation field. Here we developed a geometry-based method to determine the landslide slip-surface geometry and volume using InSAR measurements and applied it to the Jinsha River Basin, a landslide-prone area that poses a significant threat to residents and infrastructure. Through the InSAR-derived displacement, topography, and Google Earth images, 50 creeping landslides were identified in the study area. Based on the displacement field, the landslide slip-surface slope was inverted under the assumption that the landslide displacement was parallel to the slip-surface. Then the two-dimensional slip-surface depths and volumes of selected landslides were inferred using the slip-surface slope. Comparisons with the in-situ data suggest that the results obtained in this study are reliable. The mapped landslides in the study area have depths of approximately 16–160 m along the central axis and volumes ranging from 483,412 to 135,789,944 m3. The derived volume–area relationship and 2D slip-surface depth suggest that deep-seated landslide is a major landslide type in the study area. We conclude that our method can infer the slip-surface geometry of creeping landslides based on InSAR observation and our results have improved the understanding of the landslide mechanisms in the Jinsha River Basin, China.

A study of community slope risk and awareness in Penang, Malaysia

Malaysia faces a high probability of experiencing landslide disasters, particularly during rainy weather. Copious rain would result in landslide prone areas with low slope strength; thus increasing the possibility of landslide disasters occurring and threatening the safety of surrounding communities. The locations of this study are in the state of Penang that has been listed as landslide prone areas, namely Mukim 17 at Batu Ferringhi, as well as Tanjung Bungah and Tanjung Tokong, two areas which are in Mukim 18. This research aims to map community slope risk areas and to evaluate the understanding, knowledge, and awareness of the community on slope risk and landslides. There are two methods involved, namely GIS and survey. The findings show that the estimated community slope risk mapping is 24.71% and 20.62% of inhabitants living in high risks landslide zones in Mukim 17 and 18. 62 respondents participated in the survey and the result analysis shows that more than 50% have a strong fundamental knowledge of slope risks, while less than 50% are aware of possible landslide risks, and 42.6% will always obey an evacuation order. Overall findings show that most of the community does have a general awareness of slope risks and landslides.

Measuring the economic impact of landslide hazard: hedonic price approach

One of the main climate-related disasters in Yogyakarta, Indonesia, is landslides, which result in slope degradation through soil loss. The largest number of landslides was recorded in Kulonprogo regency. In the Samigaluh sub-district, where recent landslides resulted in the biggest number of displaced persons, this study seeks to determine the economic impacts of landslides. For this investigation, we used primary data gathered using a questionnaire. The systematic random sampling was utilized to choose households in the villages. The study uses a total of 300 residential households that were interviewed in landslide prone area of Samigaluh sub-district of Kulonprogo, Indonesia. The age of building, number of rooms, and building size as structural characteristic has correlation with property price, respectively. As shown by the hedonic pricing approach’s finding, there is a positive association between property price and the distance to landslide point as characteristic of neighborhood. The distance to the river was also have impact on the property price. More frequent the landslide, higher the property price. The findings of this study can assist property owners in understanding the elements that contribute to property devaluation as a result of landslides. Since landslides are a serious issue for real property owners, this study also suggests that landslide insurance programs be developed.

Analysis of landslide susceptibility in Tirtomoyo Watershed, Central Java, Indonesia

Landslide susceptibility is a measure to express the high or low probability of an area experiencing a landslide disaster which has an impact on the community which is very necessary for disaster mapping of the Tirtomoyo Watershed including Tirtomoyo. From 2014 to 2022, the Tirtomoyo Watershed experienced 147 landslides which caused material losses and casualties. The aspect of landslide disaster assessment in the watershed is very basic to reduce the risk of loss. This study aims to map landslide-prone areas. The research method uses scoring to analyze the level of landslide vulnerability and analysis of land units. The Tirtomoyo Watershed has a low landslide vulnerability potential of 119 hectares, a moderate landslide vulnerability of 2,395 hectares, a high landslide vulnerability of 18,946 hectares, and a very high landslide vulnerability of 373 hectares.

Spatial Analysis of Landslide Potential Using Modification of the Storie In-dex Method in the Wae Batu Gajah Watershed, Ambon City, Indonesia

Based on data on landslide disaster events in 2022, it shows that the Wae Batu Gajah watershed is a land-slide-prone area in Ambon City. The development of increasing population settlements in this area will cer-tainly increase the risk of landslides that are getting higher and higher. Based on this, it is necessary to con-duct spatial analysis of landslide-prone areas in the Wae Batu Gajah watershed. This study aims to analyze the potential for landslides and their exposure to settlements in the Wae Batu Gajah watershed, Ambon City using the Storie Index method. The results showed that the potential for landslides in the Wae Batu Gajah watershed is a hazard class, namely not potential, low, medium, high. The high landslide potential class dom-inates the Wae Batu Gajah watershed, covering an area of 280.19 ha or 43.67% spread across the middle and upper reaches of the watershed. An area of 98.05 ha or 47.82% of the area of residential land located in land-slide-prone areas with low potential located in the lower reaches of rivers with marbles of 0-2% and 2-8% which are relatively sloping and flat. The results of the study are expected to help the Ambon City govern-ment in efforts to mitigate landslide disasters in the future and in spatial planning efforts based on disaster mitigation