Landslide Investigation Research Articles

Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis

The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.

Modeling Landslide Susceptibility in Forest-Covered Areas in Lin’an, China, Using Logistical Regression, a Decision Tree, and Random Forests

Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (<1.2 m), a lower NDVI (<0.70), a heavier MDR (>115 mm), or a shorter DTRD (<500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the “very high” category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation.

Probabilistic framework to evaluate scenario-based building vulnerability under landslide run-out impacts

Quantifying building vulnerability under landslide run-out impacts is a pivotal aspect of landslide quantitative risk assessment (QRA). The degree of loss of buildings exposed to landslides (i.e., vulnerability) is dictated by the interplay between landslide run-out impacts and the resistance capacity of the building. However, the current link between landslide run-out and building capacity evaluations in landslide QRA is weak. Furthermore, uncertainties regarding both landslide run-out and building response must be quantified via suitable methods to enhance the trustworthiness of vulnerability evaluations. This study proposes a probabilistic framework to evaluate building vulnerability under landslide run-out impacts. The framework comprises a numerical model, which integrates dynamic run-out computations (for simulating the landslide run-out process) and finite element computations (for evaluating the resistance capacity of the building under lateral loading), aiming to assess the damage inflicted on buildings by landslide run-out impacts. Considering the uncertainties of the landslide run-out process and building structural response, a probability density evolution method (PDEM) is employed to calculate the exceedance probabilities of different limit states of building capacities. Ultimately, building vulnerability under landslide run-out impacts is ascertained by combining the probabilities of different damage states with their respective loss ratios. The proposed methodology is illustrated in a scenario where a flow-type loess landslide impacts a five-story reinforced-concrete (RC) frame building. The results indicate that the PDEM effectively estimates the exceedance probabilities with tolerable computational errors. Moreover, the vulnerability results link the failure probability of landslides and the cost of building damage within the QRA framework of individual landslides whose run-out affects nearby buildings.

A Bayesian Network-Based Inhibition Model of the Rainstorm–Landslide–Debris Flow Disaster Chain in Mountainous Areas: The Case of the Greater Bay Area, China

In this study, a Bayesian network (BN)-based inhibition model is developed for the rainstorm–landslide–debris flow (R-L-D) disaster chain in the mountainous area of the Greater Bay Area (GBA), China, using the historical disaster data. Twelve nodes are selected for the inhibition model, which are classified into four types, including Hazardous Factor, Response Operation, Disaster Evolution, and Disaster Result. By combining the proposed inhibition with the scenario analysis method, the probabilities of the BN nodes under different rainfall scenarios are analyzed, and then the inhibitory effects of the environmental geological conditions and rescue speed on the R-L-D disaster chain under the most unfavorable rainfall scenario are investigated. On this basis, an inhibition framework consisting of the early warning, inhibition, and measures layers is proposed for the R-L-D disaster chain. The results reveal that under the most unfavorable rainfall scenarios, where the rainfall intensity is greater than 100 mm/d and the rainfall duration is greater than 24 h, the probability of landslides and debris flow is 0.930 and 0.665, respectively. Improving the environmental geological conditions such as slope, lithology and geological structure can greatly inhibit the occurrence of the R-L-D disaster chain. Moreover, the improvement of geological structure conditions is the most significant, and reduces the probability of landslides and debris flow by 0.684 and 0.430, respectively, as well as reducing the probability of death and direct economic loss by 0.411 and 0.619, respectively. Similarly, increasing the rescue speed leads to a reduction in the probability of death and direct economic loss by 0.201 and 0.355, respectively. This study can provide theoretical and practical insights into the prevention and inhibition of the R-L-D disaster chain.

Geotechnical and geological investigation of landslide in West Arsi Zone, Ethiopia

Geotechnical and geological investigation of landslide in West Arsi Zone, Ethiopia

Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity

The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km2, mainly expanding forest land and cropland land, with areas of 8.28 km2 and 5.99 km2, respectively. The predicted LULC map for 2060 shows a growth of 45.88 km2 in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures.

Landslide Investigation through Emerging Technology of Electrical Resistivity Tomography

A landslide is an event of a disaster and has adverse effects on every country with varying degrees of impact. The lithology's complex nature, the vast slide area and the landslide's topography make the direct investigation challenging. In depth landslide investigation demands multidisciplinary knowledge, skilled human power and costly testing equipment, making direct investigation tedious and time-consuming. However, an alternative for the direct investigation is needed to cover all the aspects of landslide mechanism. Recently advanced geophysical methods have an excellent potential for collecting landslide mechanism-related information without disturbing them. One such method is effectively used to investigate the soil's subsurface properties as one of the significant threats to stability is rainfall in a landslide-prone area and its resolve is effectively called Electrical Resistivity Tomography (ERT). The present study endeavors to display cutting-edge research on the applications of Electrical Resistivity Tomography (ERT) in the field of landslide investigation. The foremost objective of the study is to present the outcomes of geophysical surveys conducted through ERT, which encompass 1D, 2D, 3D and time-lapse investigations. The study also highlights the advantages and limitations of these ERT investigations and identifies potential challenges that need to be addressed for the optimum utilization of ERT in future research.

Influence of Localized Rainfall Patterns on Landslide Occurrence—A Case Study of Southern Hiroshima with eXtended Radar Information Network Data during the July 2018 Heavy Rain Disasters

In this study, we use GIS and other analytical platforms to analyze the landslide distribution pattern in the July 2018 heavy rain disasters in the southern part of Hiroshima Prefecture in Japan in conjunction with chronological XRAIN (eXtended Radar Information Network) radar-acquired localized rainfall data in order to better understand the relationship between rainfall characteristics and landslide probability. An analysis of event rainfall from the July 2018 disasters determines that landslide-inducing rainfall started from 8:30 AM on 5 July and continued until 7:30 AM on 7 July, accumulating to up to 368 mm in total precipitation, and that there were two intensity peaks, one around 7:30 PM on 6 July, and another one around 4:30 AM on 7 July. These two events are associated with particularly high landslide activity, which indicates that landslide activation is related to peak-intensity rainfall combined with accumulated continuous precipitation. The XRAIN data were also used together with landslide reports to calculate the intensity–duration (i.e., I-D) rainfall threshold for the area. The mean annual precipitation in the whole study area ranged between 2025 mm and 3030 mm, with an average value of about 2300 mm. The spatial distribution of rainfall throughout the sampled years indicates that rainfall is remarkably localized, with higher values concentrated on elevated areas. However, it was also observed that the maximum precipitation volumes are not so closely related to landslide occurrence, and the highest landslide activity was found in intermediate precipitation class zones instead. Correlating the localization patterns of event precipitation and mean annual precipitation using Pearson’s correlation coefficient, we found an r value of 0.55, which is considered a moderate correlation between the two datasets (i.e., event precipitation and mean annual precipitation).

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.

Prediksi Jarak Luncur Longsoran berdasarkan Parameter Geometri Lereng dan Tipe Batuan

landslide disaster. Based on this fact, a method is needed to predict the range of landslides to minimize the impact of disaster losses. The empirical statistical method is one of the methods that can be used to predict landslides by taking input data from the history of previous landslide events. This research aims to find the best modeling form for sliding distance prediction and which parameters influence a landslide's sliding distance prediction. This study used multiple linear regression methods. The data used in this study are geometric slope parameters in the form of slope height (H), original slope (θ), landslide area (A), and rock type (RT). The data was taken from the 2015-2021 PVMBG landslide investigation report and used the Google Earth and Global Mapper program. Based on the analysis of the best empirical model that can predict the sliding distance of a landslide log Lmax = 0,387 – 0,097 RT + 0,230 log H + 0,458 log A – 0,220 tan θ with an R2 value of 0,94 and an average estimated error of 31,56%. The parameter that has the most influence on the prediction of sliding distance is the area affected by the landslide (A).

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model’s accuracy across diverse geographical areas and terrains.

Geographical assessment of landslide susceptibility using statistical approach

Worldwide, steep terrain is extremely susceptible to the landslip phenomenon. In the numerous hill and mountain ranges of India, landslides occur frequently and on an annual basis. Due to its significant rainfall during both the South West and North East monsoons, the Nilgiris district of Tamil Nadu is particularly susceptible to landslides. Despite being in a seismic zone, landslides were the most common natural disasters between 1865 and 2009 in this area. This has made a significant negative impact on the environment (fauna and flora) and human settlements in this region. In order to identify the proximity of their relationship, a simple statistical approach has been applied to derive it with the frequency ratio model. Moreover, frequency ratio method became valuable in validate the preferred causal factors depends on their ability to control a landslide incident since frequency ratio can explain clearly the difference of each score between landslide causative factors in each class and landslide distribution using Geograhical Information System (GIS). The result of frequency shows that the relationship between landslide occurrence and the slope shows that steeper slopes have greater landslide probabilities with the predicting rate of slope is 8.25%. Soli is the second most responsible factor for the cause of landslide with the value of 7.18%. High susceptibility area is widely spread over the western part of study area and some part in the western region.

Remote sensing and GIS-based landslide susceptibility mapping using LNRF method in part of Western Ghats of India

Every year, the Western Ghats region experiences devastating landslides, resulting in significant loss of life and damage to both private and public assets. To mitigate these losses, it is essential to identify the areas most susceptible to landslides. This research aims to create an accurate landslide susceptibility map for a section of the Western Ghats region by employing a comprehensive approach that combines remote sensing and geographical information systems (GIS). The Landslide Numerical Risk Factor (LNRF) method is utilized to determine landslide susceptibility zones. The LNRF model incorporates a landslide inventory based on geographic object-based image analysis and influencing factors. The resulting LNRF model generates a landslide susceptibility map, classifying over 35% of the study region's land area as having a high probability of landslides. The model's accuracy is assessed using the receiver operating characteristic (ROC) method, which yields an area under the curve (AUC) value of 0.77. This indicates that the LNRF model exhibits good predictive performance, resulting in a reliable landslide susceptibility map.

An integrated geotechnical and geophysical investigation of landslide in Chira town, Ethiopia

Landslides pose a significant threat to infrastructure, property, and human lives in many regions worldwide, including Chira town in Ethiopia. This study presents an integrated geotechnical and geophysical investigation aimed at identifying the contributing factors to landslides in Chira town, Ethiopia, with a focus on a recent landslide event. The methodology employed a combination of geotechnical and geophysical techniques to comprehensively analyze the landslide problem. The geotechnical investigation involved a detailed analysis of the soil characteristics in the area, including the composition of fine-grained soil and the determination of cohesion and angle of internal friction through triaxial testing. The geophysical investigation utilized electrical resistivity tomography to assess the subsurface soil profile. The findings revealed the presence of a massive basaltic tertiary volcanic rock layer underlying a very low resistivity layer of sticky clay soil. Through this study, it was established that rainfall, soil type, land use, elevation, and proximity to streams, slopes, and aspects were the main factors contributing to the landslide, accounting for 22.03%, 18.89%, 15.75%, 15.46%, 10.87%, 9.7%, and 7.5% of the overall influence, respectively. Based on these findings, the study proposes a range of interventions to enhance resilience against landslides, including surface drainage, the implementation of appropriate land use management practices, and the introduction of vetiver vegetation. The integration of geotechnical and geophysical methodologies provided a comprehensive understanding of the landslide problem in Chira town. The proposed interventions aim to inform future land use planning, infrastructure development, and disaster risk reduction efforts in the region. By expanding our knowledge of the mechanisms driving landslides, this study offers valuable insights that can be utilized in similar regions facing comparable geotechnical and geophysical conditions.

Detailed investigation and movement process analysis of typical high-locality landslide in dry season: a case of Qingliu landslide

Detailed investigation and movement process analysis of typical high-locality landslide in dry season: a case of Qingliu landslide

Landslide or Land Subsidence: A Critical Review at Joshimoth Municipal Town

Hazards are an extreme event but disasters are outcome of hazards in terms of different perspective from country to country. Landslide is one of the great traumatic natural disaster but its vulnerability and risk factor vary on the basis of different socio economic condition. This study will reveal future probability of landslide in Joshimoth town.

Multi-Hazard Analysis in Gunungkidul Regency Using Spatial Multi-Criteria Evaluation

This study has two aims: to determine the spatial distribution of multi-hazard levels and to discover the interactions, interdependencies, and effects that cascade among hazards in Gunungkidul Regency, Special Region of Yogyakarta. A multi-hazard map was created with a scale of 1:250,000 and modelled using SMCE (Spatial Multi-Criteria Evaluation). The conditioning factors used included slope, rainfall, historical data, river distance, geomorphology, land cover, lithology, and fault distance. The selected conditioning factors for each hazard were then weighted using weighted overlay analysis. The results showed that the area of Gunungkidul can be included in the high-risk category for floods, since the probability of flooding is 56.72%, the probability of landslides is 9.14%, that of drought is 48.86%, and that of seismic disturbance is 20.83%. (Multi-hazard maps are created by overlaying flood, landslide, seismic, and drought hazard maps.) After these risk factors were calculated, the regions on the multi-hazard map were re-categorized into five hazard classes. All areas in Gunungkidul Regency have multiple hazards, with risk factors of different magnitudes. The interaction between hazards can generate new hazards that are called cascading hazards. The cascading hazards in the study area are most likely to occur in the north of Gunungkidul Regency with its very high seismic and landslide hazard risk factors, due to the geological and geomorphological conditions of the Baturagung Structural Hills.

Engineering geological investigation and runout modelling of the disastrous Taliye landslide, Maharashtra, India of 22 July 2021

Engineering geological investigation and runout modelling of the disastrous Taliye landslide, Maharashtra, India of 22 July 2021

Predictive model for seismic displacements of flexible sliding block subjected to near-fault pulse-like ground motions

A post investigation of landslides induced by strong earthquake events in recent years shows that near-fault pulse-like ground motions have a considerable impact on slopes. Accurate evaluation of the stability of slopes under near-fault earthquakes has become a crucial issue in seismic-prone areas. Permanent displacement is widely recognized as an effective index for evaluating the stability of slope. Based on the coupled analysis, an empirical earthquake-induced permanent displacement prediction model was developed. A displacement database of 318 pulse-like ground motions from 50 earthquake events was established. The proposed predictive model is a function of the maximum seismic coefficient-time history (kmax), maximum velocity coefficient of velocity-time history of the k-time history (kv-max), yield acceleration (ky), and period ratio (ratio of the natural period of the slope to the mean period of input motion, Ts/Tm). The dynamic responses (kmax and kv-max) are represented by two different functions. At small period ratios (Ts/Tm≤1.8), it is a function of peak ground acceleration (PGA), peak ground velocity (PGV) and ky. At large period ratios (Ts/Tm > 1.8), it is a function of PGV, Ts and ky. It successfully captured the velocity pulse and long period characteristics of pulse-like ground motions. Compared to the existing model, it is found that the model is more accurate when considering pulse-like ground motions. This prediction model can be used not only for the preliminary evaluation of slope stability but also for probabilistic seismic demand analysis of flexible slopes considering pulse-like ground motion in near-fault regions.

Investigating the Shear Strength of Granitic Gneiss Residual Soil Based on Response Surface Methodology.

The shear strength of granitic gneiss residual soil (GGRS) determines the stability of colluvial landslides in the Huanggang area, China. It depends on several parameters that represent its structure and state as well as their interactions, and therefore requires accurate assessment. For an effective evaluation of shear strength parameters of GGRS based on these factors and their interactions, three parameters, namely, moisture content, bulk density, and fractal dimension of grain size, were selected as influencing factors in this study based on a thorough investigation of the survey data and physical property tests of landslides in the study area. The individual effects and interaction of the factors were then incorporated by implementing a series of direct shear tests employing the response surface methodology (RSM) into the regression model of the shear parameters. The results indicate that the factors affecting shear parameters in the order of greater to lower are bulk density, moisture content, and fractal dimension, and their interactions are insignificant. The proposed model was validated by applying it to soil specimens from other landslide sites with the same parent bedrock, showing the validity of the strength regression model. This study demonstrates that RSM can be applied for parameter estimation of soils and provide reliable performance, and is also significant for conducting landslide investigation, evaluation, and regional risk assessment.