Landslide Risk Assessment Research Articles

Landslide risk evaluation and its causative factors in typical mountain environment of China: a case study of Yunfu City

The urban area of Yunfu City is a typical mountain environment with the thin surface soil layer, broken terrain, and the fragile geological environment, which is a landslide hazard prone region, seriously threatening the safety of residents' lives and property. It is crucial to accurately reveal the landslide risk and its causative factors to develop regional geological disaster prediction and mitigation strategies. However, the risk assessment and cause analysis of landslides in Yunfu City are rarely studied at present. Therefore, this study selects 11 causative factors (elevation, slope, slope aspect, bedding structure, distance from fault, lithology, precipitation, distance from water, normalized vegetation index (NDVI), distance from road, and land use), and integrates the modified frequency ratio model and geographic detector method to analyze the spatial distribution pattern of landslide risk and its causative factors. The results indicate that the maximum predicted area under the receiver operating characteristic curve (AUC) and the average predicted AUC of the modified frequency ratio model perform best at the precision of 5, with the values of 0.83921 and 0.81872, respectively, which can accurately reveal the landslide risk evaluation in the study area. According to the experimental results, the landslide risk in the study area generally shows a trend of high in the north and low in the south. Besides, the overall risk of landslide hazards is low, with the area of very low grade and low grade accounting for 75.20% of the total area. Among the selected 11 potential factors, precipitation, NDVI, slope aspect, distance from water and slope are the main causative factors to induce landslide hazards. Furthermore, this study also describes in detail the combination of factors that are conducive to landslide hazards, which can provide a decision basis for relevant departments to carry out regional landslide hazards prevention and mitigation strategies, and can also provide a reference for geological hazard risk evaluation study in other mountain environment.

Post-Event Surface Deformation of the 2018 Baige Landslide Revealed by Ground-Based and Spaceborne Radar Observations

On 11 October and 3 November 2018, two large landslides occurred in Baige Village, Tibet, China, forcing the Jinsha River to be cut off and form a dammed lake, resulting in massive economic damages and deaths. This paper uses ground-based radar (GBR) and spaceborne interferometric synthetic aperture radar (InSAR) technologies to perform dynamic monitoring of the Baige landslide. Firstly, the GBR results suggest that the cumulative deformation from 4 to 10 December 2018 was 1.4 m, and the landslide still exhibits a risk of instability. Secondly, with the Sentinel-1A ascending and descending orbit images from December 2018 to February 2022, the InSAR-stacking technology assisted by the generic atmospheric correction online service (GACOS) and the multidimensional small baseline subset (MSBAS) method are utilized to obtain the annual deformation velocity and cumulative deformation in the satellite radar line of sight (LOS) direction of the landslide. Finally, according to the spatial–temporal deformation characteristics of feature points, combined with optical images, field investigation, and geological conditions, the development trend and inducing factors of the Baige landslide are comprehensively analyzed. It is shown that the Baige landslide is in constant motion at present, and the deformation is spreading from the slope to its right side. This research establishes a framework of combining emergency monitoring (i.e., GBR) with long-term monitoring (i.e., spaceborne InSAR). The framework is more conducive to obtaining the deformation and evolution of landslides, providing a greater possibility for studying the development trend and risk assessment of landslides, and assisting in reducing or even avoiding the losses caused by landslides.

Dynamic Risk Assessment of Landslide Hazard for Large-Scale Photovoltaic Power Plants under Extreme Rainfall Conditions

Large-scale photovoltaic power plants located in highland mountainous areas are vulnerable to landslides due to extreme rainfall, posing a significant threat to the normal operation of photovoltaic power plants. However, limited research has been conducted on landslide risk assessment specifically tailored to large photovoltaic power plants, with most studies focusing on static assessments that lack long-term sustainability in risk assessment and prediction. In this paper, a dynamic study on landslide risk at a large photovoltaic power plant project under extreme rainfall conditions is conducted. Firstly, the factors in landslide susceptibility assessment based on typical landslide characteristics in the study area are selected and an assessment index database using mapping units to extract the relevant factors is established. Subsequently, the ANP-FBN model is employed to evaluate the landslide susceptibility of large photovoltaic power plant sites. Furthermore, an assessment index system for landslide hazard vulnerability is developed by considering population, economic, and material vulnerabilities, and the AHP method is adapted to assess landslides vulnerability in the study area. Finally, the landslide rainfall threshold with the susceptibility and vulnerability assessment results are coupled to achieve a dynamic assessment of landslide hazard risk at large photovoltaic power plant sites under extreme rainfall conditions. The findings highlight that the central valley and the eastern steep slope of the study area are the primary “high” and “very high” risk areas. Moreover, with the increase in rainfall duration, the risk level of landslide hazards in the study area also rises.

Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province

Qinghai is rich in mineral resources, but frequent and large-scale mineral mining has caused secondary damage to the fragile primary surface and produced a large number of landslide disasters. In complex geological environments such as glacier ablation and frequent tectonic movements, a complete quantitative evaluation method for landslide risk in high-cold mining areas has not yet been formed. In view of this, this article uses the field survey and remote sensing data of the Datong mining area in Qinghai Province in 2012 as the basic data. We comprehensively considered five first-level factors (13 s-level factors) including topography, lithological structure, mining engineering activities, land use, and dynamic deformation as evaluation indicators for landslide susceptibility in mining areas, and used the Topographic Wetness Index (TWI) and the Human Engineering Activity Intensity (HEAI) to quantitatively estimate the hazard of landslide according to the landslide trigger mechanism. The weight-of-evidence approach was used for landslide hazard and risk mapping under different landslide--inducing conditions. The results indicate that the extremely high-hazard areas induced by human engineering activities account for 14% of the total area, and the extremely high-risk areas account for 13% of the total area in the Datong mining area, and the area of the extremely high-risk area is large; the landslide risk assessment mapping model constructed in this study can effectively evaluate the probability of slope instability caused by rainfall and human engineering activities. The effective value of the receiver operating characteristic (ROC) curve of the sensitivity assessment model reaches 0.863, and the evaluation results are consistent with reality; using the weight-of-evidence model for landslide risk assessment is more in line with the actual situation in alpine mining areas, and is more suitable for guiding landslide risk management and disaster prevention and mitigation in mining areas.

Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework

Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an AutoML-based global LSP framework at two spatial resolutions of 90 m and 1000 m, and achieved an area under the receiver operating characteristic above 0.96. The global prediction results were then validated using additional regional landslide inventories, including three countries, three provinces, and two prefecture-level datasets. Moreover, the global prediction results of 90 m are used to improve the performance of regional LSP. Specifically, the low-and very low-prone areas in the global prediction results were used as non-landslide samples for susceptibility modeling. Results demonstrated that the model achieved a better performance than original global prediction results. We believe that this study will be able to reliably promote the application of intelligent learning methods in global LSP.

GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison.

Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. Modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R2 of this region were calculated to evaluate the model's performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR method can improve the accuracy and performance of methods in studies related to landslide susceptibility. The method is equally applicable to research in other fields.

Advancements in Disaster Management: Insights into Risk Assessment, Mitigation, and Funding Strategies

This edition showcases a diverse range of research articles that offer valuable insights into various aspects of disaster management. The articles included in this issue address crucial topics in the field. For instance, Lendra et al. present a study on occupational safety and health risk management in the construction industry during the COVID-19 pandemic, proposing control solutions to ensure worker safety. Arrisaldi et al. focus on landslide risk assessment using Geographic Information System (GIS) in Kulon Progo, Indonesia, providing a comprehensive landslide risk map for disaster preparedness and community resilience. Indira and Manessa explore the generation of effective tsunami evacuation routes based on hazard models in Sukabumi, West Java, emphasizing the importance of revising hazard maps and identifying optimal routes to enhance the safety and preparedness of coastal communities. Irsyadillah investigates the concept of disaster capitalism and its impact on safety culture within the aviation industry. Other topics covered in this issue include spatio-temporal analysis of ground movement using unmanned aerial vehicles (UAV) photogrammetry by Amalia et al., analysis of hydraulic jumps for dam safety, examination of changes in mobility patterns and CO2 emissions during the pandemic by Fatimah et al. and Darma et al., understanding socio-ecological differences and risks faced by indigenous communities by Sunarti et al., exploring funding strategies through the Land Value Capture (LVC) scheme by Handayani et al., and conducting a bibliometric study on disaster management agencies and public policy by Kurniawati et al. These research articles significantly contribute to advancing knowledge in disaster management and provide practical insights for researchers, policymakers, and practitioners. We encourage readers to delve into these articles and participate in further discussions on the vital field of disaster management.

Quantitative risk assessment of the Shilongmen reservoir landslide in the Three Gorges area of China

Quantitative risk assessment of the Shilongmen reservoir landslide in the Three Gorges area of China

Semi-quantitative landslide risk assessment of district Muzaffarabad, northwestern Himalayas, Pakistan

Semi-quantitative landslide risk assessment of district Muzaffarabad, northwestern Himalayas, Pakistan

Prompt Quantitative Risk Assessment for Rain-Induced Landslides

An extreme rainstorm can cause thousands of landslides and kill hundreds of people. In the changing climate, fatal rainstorms become more frequent and intense. The current landslide emergency management evaluates hazard intensities but lacks key information on likely consequences. This study presents a novel prompt quantitative risk assessment method for rain-induced landslides. The proposed method automatically generates a one-page risk assessment report within minutes to support effective risk communication, resource allocation, and emergency response. The propagation of uncertainties is quantified in a scientific probabilistic framework. The proposed method is tested using 83 major rainstorms during 1995–2016 in Hong Kong. The method accurately predicts the number of affected buildings and the number of potential fatalities and identifies rainstorms that can trigger fatal landslides. The proposed method contributes to the advancement of landslide emergency management from hazard-informed to risk-informed, which will significantly enhance societal resilience and facilitate climate change adaptation.

Processes and factors controlling the groundwater flow in a complex landslide: A case study in the northern Italy

AbstractThe present work provides new insights on some factors controlling the groundwater flow in low‐permeability media affected by deep Apennine landslide, involving several million cubic meters of soil and rock. Rainfall and groundwater circulation are among the major triggers of landslides. However, if the first factor is well studied, only few research deal with the hydrogeological processes in the landslide. Thus, in this study, combined hydrogeological, geochemical and isotopic investigation were performed in order to: (1) characterize infiltration and flow processes and (2) understand the role of sliding surface on groundwater circulation in a complex landslide that mainly involves sandstones and clay materials. Results indicate that at least two circuits coexist: a faster one, where rainfall preferentially infiltrates and flows in a well‐connected network of macropores, cracks and soil pipes, which mainly feeds the springs; a slower and a deeper one that flows in the soil matrix and fractures of the landslide body. Moreover, the investigated slip zones show an aquiclude characteristic in terms of groundwater circulation, where the flow is very slow and precipitation influence is negligible. These results could be helpful in an accurate slope stability considerations and to improve the reliability of landslide risk assessment.

Potential landslides risk assessment based on remote sensing technique-a cause study from Dorset, southern England, United Kingdom

This paper presents a method of potential landslide risk assessment from the Dorset Southern England, United Kingdom. It concentrated on potential geological hazards and vulnerability to build a quantitative landslide risk framework in Dorset. In this study, the landslides risk assessment is centered on three components: identification of risks, calculation of risks, and evaluation of risks. Therefore, different natural environmental situations were collected using a large set of optical high-resolution satellite images and remote sensing to determine the probability of landslide occurrence and provide the quantitative model by combining the remote sensing technique. The result shows Dorset area risk level is high. The possibility of landslides mainly comes from natural conditions and human actions. The calculation of the possibility of potential landslides is based on the Bayes’ theorem. Moreover, the limitation of the probability potential risk calculation is insensitivity. The challenge is related to the temporal resolution of precipitation information.

Landslide risk assessment based on susceptibility and vulnerability

Landslide risk assessment based on susceptibility and vulnerability

A simplified semi-quantitative procedure based on the SLIP model for landslide risk assessment: the case study of Gioiosa Marea (Sicily, Italy)

Landslide risk assessment is fundamental in identifying risk areas, where mitigation measures must be introduced. Most of the existing methods are based on susceptibility assessment strongly site-specific and require information often unavailable for damage quantification. This study proposes a simplified methodology, specific for rainfall-induced shallow landslides, that tries to overcome both these limitations. Susceptibility assessed from a physically-based model SLIP (shallow landslides instability prediction) is combined with distance derived indices representing the interference probability with elements at risk in the anthropized environment. The methodology is applied to Gioiosa Marea municipality (Sicily, south Italy), where shallow landslides are often triggered by rainfall causing relevant social and economic damage because of their interference with roads. SLIP parameters are first calibrated to predict the spatial and temporal occurrence of past surveyed phenomena. Susceptibility is then assessed in the whole municipality and validated by comparison with areas affected by slide movements according to the regional databases of historical landslides. It is shown that all the detected areas are covered by points where the SLIP safety factor ranges between 0 and 2. Risk is finally assessed after computation of distances from elements at risk, selected from the land use map. In this case, results are not well validated because of lack of details in the available regional hydrogeological plan, both in terms of extension and information. Further validation of the proposed interference indices is required, e.g., with studies of landslide propagation, which can also allow considerations on the provoked damage.

Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China

Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and spatial characteristics between landslides and environmental factors may not be captured, and nonlandslide points may be randomly selected in the susceptibility mapping process. To overcome these shortcomings, in this paper, an information-gate recurrent unit residual network (Information-GRUResNet) model is proposed to produce a landslide susceptibility map by combining existing landslide records and environmental factor data. The model uses the information theory method to produce the initial landslide susceptibility map. Then, representative grid units and landslide points are selected as input variables of the GRUResNet model, from which nonlinear temporal and spatial characteristics are extracted to produce a landslide susceptibility map. Changzhou town in Wuzhou, China, is selected as a case study, and it is verified that the Information-GRUResNet model can accurately produce a landslide susceptibility map for the selected area. Finally, the Information-GRUResNet model is compared with GRU, RF, and LR models. The experimental results show that the Information-GRUResNet model is more accurate than the other three models.

Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica

The Caribbean region is highly vulnerable to multiple hazards. Resultant impacts may be derived from single or multiple cascading risks caused by hydrological-meteorological, seismic, geologic, or anthropological triggers, disturbances, or events. Studies suggest that event records and data related to hazards, risk, damage, and loss are limited in this region. National Disaster Risk Reduction (DRR) planning and response require data of sufficient quantity and quality to generate actionable information, statistical inferences, and insights to guide continual policy improvements for effective DRR, national preparedness, and response in both time and space. To address this knowledge gap, we review the current state of knowledge, data, models, and tools, identifying potential opportunities, capacity needs, and long-term benefits for integrating Earth Observation (EO) understanding, data, models, and tools to further enhance and strengthen the national DRR framework using two common disasters in Jamaica: floods and landslides. This review serves as an analysis of the current state of DRR management and assess future opportunities. Equally, to illustrate and guide other United Nations Disaster Risk Reduction (UNDRR) priority countries in the Pacific region, known as Small Island Developing States (SIDS), to grapple with threats of multiple and compounding hazards in the face of increasing frequency, intensity, and duration of extreme weather events, and climate change impact.

Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.

Computationally-feasible uncertainty quantification in model-based landslide risk assessment

Introduction: Increasing complexity and capacity of computational physics-based landslide run-out modelling yielded highly efficient model-based decision support tools, e.g. landslide susceptibility or run-out maps, or geohazard risk assessments. A reliable, robust and reproducible development of such tools requires a thorough quantification of uncertainties, which are present in every step of computational workflow from input data, such as topography or release zone, to modelling framework used, e.g. numerical error.Methodology: Well-established methods from reliability analysis such as Point Estimate Method (PEM) or Monte Carlo Simulations (MCS) can be used to investigate the uncertainty of model outputs. While PEM requires less computational resources, it does not capture all the details of the uncertain output. MCS tackles this problem, but creates a computational bottleneck. A comparative study is presented herein by conducting multiple forward simulations of landslide run-out for a synthetic and a real-world test case, which are used to construct Gaussian process emulators as a surrogate model to facilitate high-throughput tasks.Results: It was demonstrated that PEM and MCS provide similar expectancies, while the variance and skewness differ, in terms of post-processed scalar outputs, such as impact area or a point-wise flow height. Spatial distribution of the flow height was clearly affected by the choice of method used in uncertainty quantification.Discussion: If only expectancies are to be assessed then one can work with computationally-cheap PEM, yet MCS has to be used when higher order moments are needed. In that case physics-based machine learning techniques, such as Gaussian process emulation, provide strategies to tackle the computational bottleneck. It can be further suggested that computational-feasibility of MCS used in landslide risk assessment can be significantly improved by using surrogate modelling. It should also be noted that the gain in compute time by using Gaussian process emulation critically depends on the computational effort needed to produce the training dataset for emulation by conducting simulations.

LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS STUDY CASE AL HOCEIMA REGION, NORTHERN MOROCCO

Abstract. Landslides are one of the most dangerous natural disasters worldwide. Al Hoceima region, Part of the Moroccan mountain chain of the Rif is not an exception, since it’s dominated by relatively young reliefs and marked by its dynamics compared to other regions. The main goal of this study is to assess the performance of Machine learning algorithms and identify the optimal method for the mapping of the area susceptible to landslides, in Al Hoceima city and its periphery, The current study aimed at evaluating the capabilities of six advanced machine learning algorithms including, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), XGBoost (XGB) and Logistic Regression (LR). A total of 114 landslides were mapped from various sources. 70% of this database was used for model building and 30% for validation. Ten landslide factors are selected to detect the most sensitive areas: altitude, slope, aspect, distance to faults, distance from roads, lithology, curvature, plan curvature, profile curvature, and vegetation index (NDVI). The outcome of the landslide susceptibility analysis was verified using receiver operating characteristics (ROC) curves and precision-recall curves (PRC), acknowledging XGBoost and Random Forest as the optimal methods with AUCROC (96% and 95.5% accordingly), a severe imbalanced classification was detected by the PRC, solved by undersampling the majority class, to obtain major improvement in models performance according to AUCPRC (from 40% to 87% for XGBoost) and slight decrease according to AUCROC (from 96% to 94% for XGBoost). The outcome of this study and the landslide susceptibility maps would be useful for environmental, economic, and social protection and to help formulate suggestions for optimizing landslide risk assessment in areas exposed to this phenomenon.

Natural hazards affecting cultural heritage: assessment of flood and landslide risk for the 28 existing Norwegian stave churches

PurposeThe overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.Design/methodology/approachMaterial and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.FindingsThe paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.Originality/valueThe contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.