Landslide Susceptibility Research Articles

Parsimonious High‐Resolution Landslide Susceptibility Modeling at Continental Scales

AbstractLandslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental‐scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under‐represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (N = 613,724), a high‐resolution digital elevation model (10‐m), and high‐performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split‐sample calibration. We down‐sample our results to a 90‐m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide‐prone terrain within each down‐sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

Assessing the Reliability of Landslides Susceptibility Models with Limited Data: Impact of Geomorphological Diversity and Technique Selection on Model Performance in Taounate Province, Northern Morocco

Assessing the Reliability of Landslides Susceptibility Models with Limited Data: Impact of Geomorphological Diversity and Technique Selection on Model Performance in Taounate Province, Northern Morocco

Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam

Abstract In recent decades, global rapid urbanization has exacerbated the impacts of natural hazards due to changes in Southeast Asia’s environmental, hydrological, and socio-economic conditions. Confounding non-stationary processes of climate change and global warming and their negative impacts can make hazards more complex and severe, particularly in Vietnam. Such complexity necessitates a study that can synthesize multi-dimensional natural-human factors in disaster risk assessments. This synthesis study aims to assess and monitor climate change and land-cover/land-use change impacts on flood and landslide hazards in Vietnam’s Gianh River basin. Three Deep Neural Network (DNN) and optimization algorithms, including the Adam, Tunicate Swarm Algorithm (TSA), and Dwarf Mongoose Optimization (DMOA) were used to determine the regions with the probability of the occurrence of flood and landslide and their combination. All efficiently evaluated hazard susceptibility based on a synthesis analysis encompassing 14 natural and anthropogenic conditioning factors. Of the three, the Deep Neural Network (DNN)-DMOA model performed the best for both flood and landslide susceptibility, with area-under-curve values of 0.99 and 0.97, respectively, followed by DNN-TSA (0.97 for flood, 0.92 for landslide), and DNN-Adam (0.96 for flood, 0.89 for landslide). Although the area affected by flooding is predicted to decrease, the overall trend for total hazard-prone areas increases over 2005–2050 due to the more extensive area affected by landslides. This study develop and demonstrate a robust framework to monitor multi-hazard susceptibility, taking into account the changes in climate and land-use influence the occurrence of multiple hazards. Based on the quantitative assessment, these findings can help policymakers understand and identify confounding hazard issues to develop proactive land-management approaches in effective mitigation or adaptation strategies that are spatially and temporally appropriate.

GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)

Abstract Landslides and slope processes constitute one of the most frequent natural hazards in valleys near major rivers and mountainous regions. The surface layer, characterized by its relatively loose composition, is prone to sliding due to a combination of distinct natural and human-related factors. Specific sections along the right bank of the Danube River in Smederevo city exhibit significant susceptibility to landslide activation, often leading to substantial material losses and posing a risk to the local population. The initial step in the provided research involves analyzing existing literature and mapping landslides within the study area. The initial analysis covers both natural conditions and anthropogenic activities. The second step includes establishing a geospatial database in the Geographic Information System and generating eight thematic maps. In the third step, different weight coefficients were assigned to the criteria, which facilitated the creation of the Landslide Susceptibility Index using the Best–Worst Method. Subsequently, in the fourth step, a composite map illustrating landslide susceptibility was produced. According to this research, about 4% of the territory of Smederevo, or 19.3 km2, is highly or very highly susceptible to landslides. These localities are located on the right bank of the Danube River and around the Ralja River. Receiver operating characteristic-area under the curve value indicates very high predictive power (approximately 1), thus suggesting the reliability of the used methodology. This visualization of areas highly prone to such occurrences empowers policymakers to implement more effective environmental protection measures and institute sustainable management practices for agricultural parcels in this region. Also, the provided research represents the inaugural integration of advanced remote sensing techniques and interdisciplinary investigations, offering deeper insights into landslide activity in the study area and yielding more comprehensive results.

Impacts of bridge constructions on channel dynamics and sediment aggradation: An integrated discussion, West Bengal, India

Channel dynamics, including channel migration, erosion, and deposition, is a significant geomorphological phenomenon. It has been noted that the Gish River exists with its widest extension between 1913 and 2021, causing a bottleneck situation. The section G4 (Gish-4) experienced the most significant squeezing character over time. The catchment landslide-induced sediments, mostly transported in the monsoon by 1st and 2nd order streams, deposited at the beds near the rail and road bridges of the rivers Lish, Gish, and Chel. Landslide susceptibility mapping (LSM) and sediment transport index (STI) maps specify the zones of sediment generation and aggradation. Results of low ‘b” value 0.77 + /−0.04 specify active tectonics and instability of the area. A petrographic analysis of landslide samples and river bed sediments justifies that the landslide connected streams are continuously transported and deposited in the river bed. In the Himalayan foothill rivers like Lish, Gish, and Chel, channel planform dynamics may be the result of landslide occurrences, that induce huge sediments in the rivers and sediment transport index indicates the deposition of the sediments near rail and road bridges in spatio-temporal scale.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Landslide Susceptibility Mapping in North Tehran, Iran: Linear Regression, Neural Networks, and Fuzzy Logic Approaches

Landslide Susceptibility Mapping in North Tehran, Iran: Linear Regression, Neural Networks, and Fuzzy Logic Approaches

Landslide susceptibility analysis and hazard zonation of Koh-e-Suleman range using weighted overlay technique

A healthy environment improves the quality of life on Earth, while natural disasters affect the quality of the environment. An area’s Landslide Hazard Zonation (LHZ) provides key information about the susceptibility to natural or manmade disasters. Using the Weighted Overlay Technique, this research aims to develop the landslide susceptibility and zonation mapping of Koh-e-Suleman Range (a hill station around the heritage site). Six causative factors are evaluated: geology, elevation, slope inclination, distance from streams, type of soil, and annual rainfall intensity, considering equal risk influence for all the factors. All causative factor maps were generated to yield equal classes and provide uniformity to the results except for soil types found in the study area. The findings indicate that the study area may comprise four landslide hazard zones with high, medium, low, and very low-intensity susceptibility of landslides. The accuracy of the final LHZ map is verified by comparing the hazard zonation area percentages with the global landslide inventory map from a macro-study of NASA. It was found that the study areas largely fall within low- and very low-hazard zones.

Simulation Model for Rainfall Intensity to Landslide Susceptibility

Landslides are one of the most hazardous natural disasters because their occurrences are often destructive to natural and artificial structures on earth and reduce, the quality of the surrounding environment. Predicting the susceptibility of an area to landslides is essential for reducing losses in terms of property, human lives, and environmental damages. Kota Wisata Batu (KWB) is one of areas which having a highest landslide potential in East Java, Indonesia, recording data mentioned that there are 109 landslides in 2022 caused by highest rainfall intensity of 502 mm with a number of intensities of 2977mm per year. Study purposed to identify the factors contributing to landslide occurrences using a geodetic measurement method. The study uses geographic information system (GIS) and PRISMA software analyses to examine and/ or evaluate the relationship between rainfall intensity and landslide susceptibility. The study results the generation of rainfall patterns and maps highlighting sensitivity areas to landslides. These visual representations the classification area as a high-risk and susceptibility zone to landslides at Kota Wisata Batu, East Java. January to April, and August to November should have pay attention since these months is reflection of monsoonal climate with the hard rain in a few days without the raining pause. Some places which are required to pay attention, such as: Gunungsari, Tulungrejo, and Songgokerto.

Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China

Landslide risk mapping can be an effective reference for disaster mitigation and land use planning, but the modelling process involves multidisciplinary knowledge which leads to its complexity. In this study, Jiaxian County in Shaanxi Province on the Loess Plateau of China, served as the study area, primarily characterized by Quaternary loess-covered geomorphology, with an average rainfall of about 400 mm annually. Soil erosion and human engineering activities have contributed to significant slope failures, posing threats to local residents and infrastructure. A reasonable inventory of landslides in the region was established by field survey combined with aerial imagery, allowing for characterization of their development and spatial distribution. Nine thematic maps related to landslide occurring and three vulnerability maps were prepared as influencing factors for landslide risk assessment. Subsequently, landslide susceptibility and hazard were evaluated using a kernel extreme learning machine (KELM) and information value (IV) model, followed by map validation. A decision table was then employed to generate the landslide risk map. The results of landslide hazard mapping showed that the historical landslide events were mainly developed in the central part of the study area, particularly concentrated near the developed river network. Integration of overall risk elements suggested that landslide risks in the study area were generally at a low level. Besides, a total of 0.25 % and 2.05 % of the areas were classified as having very high and high landslide risk levels, respectively, where 65.11 % of inventory landslides occurred. Therefore, the proposed procedure is a valuable tool for assessing landslide risk in Jiaxian Country.

Landslide susceptibility mapping using frequency ratio and analytical hierarchy process method in Awabel Woreda, Ethiopia

A landslide is a serious geo-environmental problem that results in the death of life and the devastation of infrastructure, properties, and agricultural lands. This research aimed to identify landslide susceptibility zones in selected Kebels of Awabel Woreda, central Ethiopia. Frequency ratio (FR) and analytical hierarchy process (AHP) methods were used. 175 landslide inventory data collected from Google Earth and field data were collected for testing and training data sets. Using the analytical hierarchy process, all the thematic layers (stream distance, slope, aspect, rainfall, lineament density, elevation, lithology, soil, land use/land cover, and curvature) were reclassified and weighted based on their relative contribution to landslide occurrence with the help of experts’ knowledge. The results show that 11.85% and 20.52 % of the study fall under the very high and high susceptible zones, respectively, while the low susceptible zones cover 26.3% and 14.74% of the area. The landslide susceptibility zone identified by the frequency ratio model shows that (6.09%) and (16.9%) of the area covered very high and high susceptible zones, respectively, while 30.4% and 23.4% of the area covered low and very susceptible zones, respectively. The predicted landslide-susceptible areas were validated using existing landslide points with the help of the ROC tool in ArcGIS. Area under the curve (AUC) results were 84.5% for the AHP model and 73% for the frequency ratio model. The find of this study will provide an input for decision makers and land use planners in the future.

An update on the landslide inventory and susceptibility modelling in the Illawarra with an analysis of the 2022 rainfall events

An update on the landslide inventory and susceptibility modelling in the Illawarra with an analysis of the 2022 rainfall events

Review of Quantifying Slope Stability and Assessing Landslide Susceptibility in Sabah, Malaysia

Landslides affect 3.7 million square kilometers of land and affect around 5% of the global population. In Malaysia, 21,000 locations are prone to landslides, with 16,000 in Peninsular Malaysia, 3,000 in Sabah, and 2,000 in Sarawak. Factors contributing to landslides include geological factors, weather conditions, slope steepness, vegetation, and human activities. Rainfall and geological conditions are significant external and internal causes. Landslides can be mitigated using various construction techniques, such as contagious bored pile, guniting, and rock anchoring. Rainfall and landslides are closely related, with rainfall affecting soil stability and causing slope failure. Prolonged rainfall can decrease slope stability due to increased water infiltration, leading to landslides. Analysis of slope stability is crucial in geotechnical engineering, assessing the stability of natural and man-made slopes. It involves assessing soil properties and identifying potential failure surfaces. Limit Equilibrium (LEM) and finite element (FE) methods are commonly used for slope stability assessments. LEM divides failure mass into slices, while FEM maintains global equilibrium until failure occurs. Both methods offer advantages, such as revealing working stress deformations with realistic soil compressibility data and tracking progressive failure to shear failure.

GIS Based Spatial Analysis of Landslide Hazard in Tehsil Balakot, KP, Pakistan

The Himalayan region is highly susceptible to landslides due to intense rainfall and seismic activity, posing significant risks to slope stability. Due to landslides, Tehsil Balakot valley has suffered substantial damage to infrastructure, roads, and the tourism sector. This research aims to develop a landslide susceptibility map and identify hazard-prone areas to support sustainable development and mitigate risks in Balakot valley. The study employs a weighted overlay analysis using primary and secondary data raster layers, including slope, aspect, precipitation, and seismic raster maps. The slope and aspect maps are derived from a 30-meter ASTER digital elevation model, while precipitation maps are generated using Inverse Distance Weighted (IDW) interpolating data from the Pakistan Meteorological Department. Seismic data is acquired from the Geological Survey of Pakistan (GSP). The resulting landslide susceptibility map classifies the region into five hazard classes: very high, high, medium, low, and very low. The landslide susceptibility maps provide valuable insights for identifying areas at varying levels of landslide risk.

Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm

Landslide susceptibility prediction and mapping in Taihang mountainous area based on optimized machine learning model with genetic algorithm

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

Landslide susceptibility mapping by remote sensing and GIS using frequency ratio, value of information and logistic regression methods in the N'fis watershed, High Atlas of Marrakech (Morocco)

Landslide susceptibility mapping by remote sensing and GIS using frequency ratio, value of information and logistic regression methods in the N'fis watershed, High Atlas of Marrakech (Morocco)

Assessing Landslide susceptibility and formulating adaptation strategies in the Konto Watershed, East Java, Indonesia

A landslide is defined as the displacement of masses of soil or rock constituting a slope, or a combination thereof, resulting from the destabilization of the slope composition. This study aims to develop an integrated approach to managing landslide hazards in the Konto Watershed by assessing susceptibility through spatial mapping and providing a detailed risk assessment to inform disaster mitigation, landslide management, and land use planning. The susceptibility formula incorporates eight observational parameters: max 3-day rain, slope, rock geology, fault presence, regolith depth, land use, road infrastructure presence, and population density. Each parameter is assigned a weight value, representing its respective weighting factor. Primary data on landslide potential is gathered through direct field observations in critical areas with slopes exceeding 40 %, while secondary data is sourced from Indonesian government agencies. The secondary data is then identified, mapped, and analyzed using spatial scoring analysis. The research findings indicate that landslide hazards in areas with high slope gradients and specific soil types, land use combined with elevated rainfall, are categorized as very low (1.16 %), low (6.64 %), medium (89.24 %), and high (3.06 %) of susceptibility from 235.22 km2 of watershed area. The regional function area converts 75.8 % of existing land use into 61.2 % protected zones, 32.2 % buffer zones, and 6.6 % cultivation zones, without altering 24.2 % of existing settlement and rice field areas. Landslide control efforts encompass four treatments: protected and buffer area preservation, mechanical treatment, and cultivation area management based on susceptibility levels. Long-term vegetative treatments are designed according to regional function and susceptibility levels.

Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China

Exploring Bayesian network model with noise filtering for rainfall-induced landslide susceptibility assessment in Fujian, China