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BACKGROUND AND OBJECTIVES: Landslide disasters in Thailand between 1970 and 2011 revealed a notable pattern: they primarily originated on mountain slopes, distinguished by a deeper soil profile. This soil profile comprised clay loam and sandy loam textures and was situated over aged geological formations of granite and shale rocks. The affected areas included the southern and northern provinces of Thailand.This study investigated the consequences of landslide hazards on stream water and sediment quality in two watersheds: the Mae Phul–Mae Prong watershed in Uttaradit province, the northern part of Thailand, and the Klong Kram watershed in Surat Thani province, the southern part of Thailand. These watersheds had experienced recurrent landslides, primarily on mountain slopes characterized by deep clayey and sandy loam soils over old granite and shale rock types as well as old granite limestone.METHODS: During wet and dry periods in April and November 2015, 108 samples were collected from 18 stations (9 stations in the Klong Kram watershed and 9 stations in the Mae Phul–Mae Prong watershed). These samples included upland soil, stream water, and sediments. For upland soils, 1 kilogram samples were collected through auger and V-shaped pit techniques using a stainless-steel spade, with composite sampling conducted at 0–30 centimeters across all 18 stations. Stream water was collected in one part using a 1-L polyethylene bottle at 30 centimeter from the stream layer, while other samples were compositely collected in sterilized glass bottles to determine coliforms. Soil and sediment samples were compositely collected from the bottom using a stainless-steel spade. All samples were stored at 4 degrees Celsius and transported to a laboratory for analysis. The insight gained from these collection efforts elucidated the dynamics of landslide impacts at the spatial scale for the two watersheds.FINDINGS: Most water samples met Thai surface water quality standard for various parameters; however, microbial contamination of the water samples attributed to community activities along stream banks was detected. Notably, arsenic was consistently detected in upland soil, stream water, and sediment samples. For Uttaradit, the average arsenic concentrations were 0.22 ± 0.09 milligram per kilogram, 0.01 ± 0.14 milligram per liter, and 9.74 ± 4.42 milligram per kilogram in upland soil, water, and sediment samples, respectively. For Surat Thani, arsenic concentrations were 87.63 ± 208.83 milligram per kilogram, 0.01 ± 0.01 milligram per liter, and 19.44 ± 36.38 milligram per kilogram in upland soil, water, and sediment samples, respectively, particularly near landslide scars where the arsenic concentrations were significantly higher in sediments and upland soils compared with stream water, highlighting the role of landslides near streams. These data suggest that sediment transport from upland soil in the landslide scar into stream water affects water quality, particularly in terms of arsenic concentration near the landslide scar, often surpassing natural standards.CONCLUSION: The study concluded that stream water was directly affected by landslides as these watersheds were unsuitable for consumption due to arsenic and microbial contaminations. This conclusion emphasizes the critical need to incorporate landslide hazard considerations into watershed management practices to safeguard downstream communities and preserve water resources.

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas. [ABSTRACT FROM AUTHOR]

Copyright of Global Journal of Environmental Science & Management (GJESM) is the property of Global Journal of Environmental Science & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The early identification of potential landslide hazards has always been a hot and difficult issue in the field of international landslide research. In recent years, many scholars have conducted extensive and beneficial explorations in this field, making significant contributions to the effective prevention of landslide disasters. However, until now, there are very few review documents on summarizing such valuable experience in the system, which makes it difficult to meet the ever-increasing demand of researchers in scientific documents. To address the gap, this paper systematically reviews 843 documents collected by the two data platforms of Web of Science (WOS) and Scopus from 1971 to 2023 by using the bibliometric analysis software. This paper first figures out the internal relationship between documents by analysing their spatial and temporal distribution characteristics, and then emphatically analyses the application, advantages and disadvantages of different early identification methods based on the influencing factors of landslide disaster formation and multi-source data acquisition links. And finally, this paper discusses the challenges and development trends in this field from four aspects of cooperative analysis, multi-source data, topic analysis and research trends, and puts forward some suggestions. This research can help researchers to use various early identification methods reasonably and provide summary and integration services of scientific document achievements for efficient research in this field. [ABSTRACT FROM AUTHOR]

In mountainous landscapes, the diverse geotechnical conditions amplify landslide susceptibility. Factors such as precipitation and seismic activity can trigger landslides, while inherent hazards such as voids, fissures, and compaction deficits jeopardize long-term slope stability. Detecting and forecasting these susceptibilities accurately is crucial. In this paper, the time-domain finite- difference approach and the gprMax software are used to conduct forward modeling of landslide susceptibility. An electrical model of subsurface aqueous structures is created, including water-filled and air-filled cavities, fracture zones, and fault lines. The distinctive radar signal responses within these environments are examined, and a dataset of B-scan images associated with their electrical models is constructed. By employing deep learning algorithms and the robust nonlinear mapping ability of convolutional neural networks in the Pix2Pix generative adversarial network, we accelerate the intelligent inversion of the geological radar data on landslide susceptibility. This innovative approach effectively reconstructs hazard models, offering a reliable basis for interpretation of radar signals. [ABSTRACT FROM AUTHOR]

Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in this paper to predict landslide hazards at a local scale. The proposed model not only extracts spatial scene features for computations but also achieves dynamic prediction, which means that only one input is needed to obtain continuous predictions. Experiments were performed in Lushan, Sichuan, China. After using a convolutional neural network (CNN) to obtain the initial static landslide hazard zoning results, the landslide hazard zoning results for 2016–2025 were predicted with the SCBR-CA model. For comparison, a CA combined with a CNN (CNN-CA), was introduced. The area under the curve (AUC) of the receiver operating characteristic curve and Moran's I index were used to assess the performance of the model. The experimental results showed that SCBR-CA yields slightly better AUC and Moran's I index values than CNN-CA, and the dynamically predicted landslide hazard zoning results are equivalent or superior to those of static zoning, which indicates that the SCBR-CA model effectively predict local landslide hazards. [ABSTRACT FROM AUTHOR]

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas.

Landslide is a geological hazard typically associated with extreme events such as earthquakes, heavy rainfall, volcanic eruptions, changes in groundwater level, etc. This study was carried out in Okemesi-Ekiti (also known as Okemesi), Southwest Nigeria, with the purpose of using remote sensing and GIS technologies to analyze the environmental factors (grain size, direct shear strength resistance, rainfall data, wet density, surface, and slope) resulting in the occurrence of the Okemesi landslide. The study also aimed to conduct a vulnerability analysis in the study area to identify regions with a probability of landslide occurrence. The grain size analysis of the soil in the Okemesi landslide area showed that slope materials comprised 17.14% gravel, 59.31% sand, and 19.48% fines, thus the soil type could be classified as poorly graded gravely sand with a high possibility of landslide occurrence. The geomorphic characteristics of the study area was characterized by slopes ranging from 0.00° to 49.00°, while most slopes in the area were less than 8.00°. The slope aspect direction was mainly in south (157.51°–202.50°), southwest (202.51°–247.50°), west (247.51°–292.50°), and north (0.00°–22.50° and 337.51°–360.00°). The highlands were primarily bounded by the slope directions of north (0.00°–22.50° and 337.51°–360.00°), northeast (22.51°–67.50°), east (67.51°–112.51°), and southeast (112.51°–157.50°), which indicated the potential direction of mass movement. The study area can be divided into three vulnerability zones: high, medium, and low, with the area percentages of 9.00%, 61.80%, and 29.20%, respectively. The analysis suggested that the Okemesi landslide was likely triggered by rainfall, which might have weakened the physical structure of slope materials. Understanding the causes and impacts of landslides is crucial for policy-makers to implement measures to mitigate landslide hazards, protect infrastructure, and prevent the loss of life in the landslide-prone regions.

In mountainous landscapes, the diverse geotechnical conditions amplify landslide susceptibility. Factors such as precipitation and seismic activity can trigger landslides, while inherent hazards such as voids, fissures, and compaction deficits jeopardize long-term slope stability. Detecting and forecasting these susceptibilities accurately is crucial. In this paper, the time-domain finite-difference approach and the gprMax software are used to conduct forward modeling of landslide susceptibility. An electrical model of subsurface aqueous structures is created, including water-filled and air-filled cavities, fracture zones, and fault lines. The distinctive radar signal responses within these environments are examined, and a dataset of B-scan images associated with their electrical models is constructed. By employing deep learning algorithms and the robust nonlinear mapping ability of convolutional neural networks in the Pix2Pix generative adversarial network, we accelerate the intelligent inversion of the geological radar data on landslide susceptibility. This innovative approach effectively reconstructs hazard models, offering a reliable basis for interpretation of radar signals.

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Landslide is a geological hazard typically associated with extreme events such as earthquakes, heavy rainfall, volcanic eruptions, changes in groundwater level, etc. This study was carried out in Okemesi-Ekiti (also known as Okemesi), Southwest Nigeria, with the purpose of using remote sensing and GIS technologies to analyze the environmental factors (grain size, direct shear strength resistance, rainfall data, wet density, surface, and slope) resulting in the occurrence of the Okemesi landslide. The study also aimed to conduct a vulnerability analysis in the study area to identify regions with a probability of landslide occurrence. The grain size analysis of the soil in the Okemesi landslide area showed that slope materials comprised 17.14% gravel, 59.31% sand, and 19.48% fines, thus the soil type could be classified as poorly graded gravely sand with a high possibility of landslide occurrence. The geomorphic characteristics of the study area was characterized by slopes ranging from 0.00° to 49.00°, while most slopes in the area were less than 8.00°. The slope aspect direction was mainly in south (157.51°-202.50°), southwest (202.51°-247.50°), west (247.51°-292.50°), and north (0.00°-22.50° and 337.51°-360.00°). The highlands were primarily bounded by the slope directions of north (0.00°-22.50° and 337.51°-360.00°), northeast (22.51°-67.50°), east (67.51°-112.51°), and southeast (112.51°-157.50°), which indicated the potential direction of mass movement. The study area can be divided into three vulnerability zones: high, medium, and low, with the area percentages of 9.00%, 61.80%, and 29.20%, respectively. The analysis suggested that the Okemesi landslide was likely triggered by rainfall, which might have weakened the physical structure of slope materials. Understanding the causes and impacts of landslides is crucial for policy-makers to implement measures to mitigate landslide hazards, protect infrastructure, and prevent the loss of life in the landslide-prone regions. [ABSTRACT FROM AUTHOR]

Rainfall-induced shallow landslides are widespread throughout the world, and vegetation is frequently utilized to control them. However, in recent years, shallow landslides have continued to frequently occur during the rainy season on the vegetated slopes of the Loess Plateau in China. To better probe this phenomenon, we considered vegetation cover in the sensitivity analysis of landslide hazards and used the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model to quantitatively describe the impacts of different types of vegetation cover on slope stability. Based on the rainfall information for landslide events, the spatiotemporal distributions of the pore water pressure and the factor of safety of the vegetated slopes were inverted under the driving changes in the soil properties under different vegetation types, and the average prediction accuracy reached 79.88%. It was found that there was a strong positive correlation between the cumulative precipitation and the proportion of landslide-prone areas in woodland covered by tall trees, grassland covered by shrubs and grasses, and cultivated land. The highest landslide susceptibility, which has the greatest potential to hasten the occurrence of rainfall-induced landslides, is found in woodland with tall trees. Therefore, this paper proposes the promoting relationship between vegetation and landslide erosion, which provides a new scientific perspective on watershed management to prevent shallow landslide disasters and manage and develop watershed vegetation. [ABSTRACT FROM AUTHOR]

Landslides pose a greater potential risk to the Sichuan-Tibet Transportation Project, and extensive landslide inventory and mapping are essential to prevent and control geological hazards along the Sichuan-Tibet Transportation Corridor (STTC). Recently proposed landslide detection methods mainly focused on new landslides with high vegetation. In addition, there are still challenges in automatic detection of old landslides using optical images. In this paper, two methods, namely mask region-based convolutional neural networks (Mask R-CNN) and transfer learning Mask R-CNN (TL-Mask R-CNN), are presented for detecting and segmenting new and old landslides, respectively. An optical remote sensing dataset for landslide recognition along the Sichuan-Tibet Transportation Corridor (LRSTTC) is constructed as an evaluation benchmark. Our experimental results show that the recall rate and F1-score of the proposed method for new landslide detection can reach 78.47% and 79.80%, respectively. Transfer learning is adopted to detect old landslides, and our experimental results show that evaluation indices can be further improved by about 10%. Furthermore, TL-Mask R-CNN has been applied to identify ice avalanches based on the characteristics of landslides. It appears that our proposed methods can detect and segment landslides effectively along the STTC with the constructed LRSTTC dataset, which is essential for studying and preventing landslide hazards in mountainous areas. [ABSTRACT FROM AUTHOR]

The risk assessment of landslide hazards has a tremendous influence on people’s lives and property safety; therefore, its investigation is significant. The stratigraphic lithology, degree of weathering, relationship between the structural plane and slope direction, cohesive force, angle of internal friction, severity, average slope degree, height of slope, and type of landslide are adopted as the evaluation factors first. Second, an assessment model is developed based on the variable fuzzy set theory. In addition, the proposed model is utilized to assess the landslide hazards in the Badong section of Three Georges in China. Finally, the results demonstrate that the results derived from the proposed model are consistent with the current specifications; the accuracy rate reaches 83%. The method can determine the risk level of landslide hazards and provide an alternative scheme. Hence, this study can accurately present a new approach for assessing landslide hazards in the future.

In this paper, new models were investigated and developed for landslide hazards in Muzaffarabad District, located in the Azad Jammu and Kashmir region of Pakistan. The influential factors used in the landslide modelling were land use/landcover (LULC), elevation, slope, slope aspect, rainfall, drainage, road, surface roughness, and topographic index. The GIS-based Analytic Hierarchy Process (AHP) model was applied by utilizing the database of 35 active landslides and their pixels present in classes of all influential factors. The mean landslide hazard values, obtained from the mean landslide hazard analysis, were used as hazard weightages in the AHP model for development of a landslide hazard zone map. The highest mean hazard values for: (i) bare soil in LULC was 14.6%; (ii) 600–800 m in elevation was 6.89%; (iii) 30°–35° in slope was 6%; (iv) S and SW in slope aspect was 9.01%; (v) 1350–1405 mm/yr in rainfall was 9.03%; (vi) 40–80 m in buffered drainage was 12.83%; (vii) 40–80 m in buffered road was 12.48%; (viii) 60–138 in surface roughness index was 10.99%; (ix) −1.74–−1.25 in topographic position index was 13.07%. The percentages of very low, low, moderate, high, and very high landslide hazard zones were 1.48%, 11.80%, 39.36%, 37.36%, and 9.57% respectively. The co-efficient of the determination (r2) value of 0.96 indicated a strong relationship between the model development and validation. Thus, landslide hazard zone map models and methodology indicated a very high accuracy. This landslide hazard zone map could be utilized for the landslide damages' reduction and the planning and development of road and building infrastructures in the study area. Additionally, this research could be replicated in other landslide prone areas of Pakistan for the minimizing the damages of landslides. [ABSTRACT FROM AUTHOR]

Landslides inventories are crucial documents for extrapolating the spatial distribution of landslides that occurred in the past. However, the production of landslide hazards maps also needs deepening the knowledge on the temporal distribution of landslide activity. In this regard, historical maps and archive documents represent important sources of data and information on territory evolution through time and especially on landslides occurrence. Nevertheless, these kinds of data sources are rarely considered as they often must be interpreted integrating different disciplines. This work shows how historical and geomorphological data on landslides occurrence in an area of the Northern Apennines can be integrated to produce a synthesis map. This synthesis map put together analogic and digital information allowing to delineate the landscape evolution, in terms of spatial and especially temporal changes occurred in the study area representing a tool that could be useful for the landslide hazard assessment in territory management.

The landslide hazard is one of the geological hazards in mountainous zone. Its occurrence is controlled by many factors. To assess the risk level of landslide in Shiwangmiao accurately, intuitionistic fuzzy sets-Topsis model is introduced at first; secondly, the decisive matrix about the intuitionistic fuzzy sets is established, and the index weight coefficients considering the uncertainty of assessment indices are determined by using the Entropy weight method, then the weighed decisive matrix is obtained. Finally, degree of membership at different levels about the landslide is determined based on the ranking sequence of degree of membership, the risk level corresponding to the maximum degree of membership is final assessment level. The conclusions are drawn that accurate rate of risk estimation about landslide hazards is very high based on the intuitionistic fuzzy sets model in comparison with the current specifications, and the method is feasible for the risk assessment of landslide hazards, so it provides a new method and thoughts to assess the risk level of landslide in future. [ABSTRACT FROM AUTHOR]

Ukraine is characterised by active natural hazards processes within different structural, tectonic and landscape zones. In Middle Dnieper basin region mass movement processes have great impact on people's livelihoods and infrastructure. These processes occur on the slopes with different geological structure. The determining causes include lithologic and stratigraphic conditions, hydrogeological regime, structural and textural peculiarities of rocks and the geomorphology of the slopes. Landslide inventory database has been developed based on long-term observations of more than 400 landslides and landslide-prone areas. This paper takes efforts forward by combining different geological and geophysical methods to advance the current understanding of landslide phenomena and contributing towards a better informed assessment of landslide hazard and risk. The developed methodology is implemented in a test sites of Kyiv region, covering an area of 18.3 km² situated in the Middle Dnieper basin. Electrical Resistivity Tomography, Self-Potential and Infrared Thermography techniques were employed to investigate the lithostratigraphic sequences, the geometry of landslide body and potential mass movement. The results presented here confirm the potential of using an integrated approach that combines different field data to better plan mitigation activities and measures for the effective land management. This study will be useful in increasing the safety aspects of the infrastructures and lives and also for planning of research and developmental activities. [ABSTRACT FROM AUTHOR]

The occurrence of landslides in mountainous areas of Nepal is recurrent phenomena and this can be disastrous if occurred within human settlements. Proper research is required to manage and reduce the risks of the disaster in places where landslides had occurred frequently. In risk assessment estimation of vulnerability is one major component. This research was aimed to generate information on the vulnerability of people in the Panchase area of central Nepal. The method of analytical hierarchy process was used to define the weightage to be assigned for 4 factors and 26 indicators used in the research. 377 households were surveyed with a questionnaire designed to collect information on those indicators. The households sampled for the survey were made sure that they were near or around the landslide affected area. The response for each indicator was converted into scores which when summed up yielded the vulnerability score. This score for each household was used to categorize households into five levels of vulnerability from very low, low, moderate, high, to very high. The numbers of households in each category were 16, 92, 191, 75, and 3 respectively. Environmental and economic indicators were inflicting higher vulnerability in this research location. Remoteness and lesser number of facilities and/or services, lower numbers of governmental offices, absence of banking and financial institutions, lesser preferences for insurances and savings, degraded natural water sources were major determinants of higher vulnerability in Panchase. These indicators should be prioritized during disaster risk management in Panchase.

Rainfall-induced shallow landslides are widespread throughout the world, and vegetation is frequently utilized to control them. However, in recent years, shallow landslides have continued to frequently occur during the rainy season on the vegetated slopes of the Loess Plateau in China. To better probe this phenomenon, we considered vegetation cover in the sensitivity analysis of landslide hazards and used the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model to quantitatively describe the impacts of different types of vegetation cover on slope stability. Based on the rainfall information for landslide events, the spatiotemporal distributions of the pore water pressure and the factor of safety of the vegetated slopes were inverted under the driving changes in the soil properties under different vegetation types, and the average prediction accuracy reached 79.88%. It was found that there was a strong positive correlation between the cumulative precipitation and the proportion of landslide-prone areas in woodland covered by tall trees, grassland covered by shrubs and grasses, and cultivated land. The highest landslide susceptibility, which has the greatest potential to hasten the occurrence of rainfall-induced landslides, is found in woodland with tall trees. Therefore, this paper proposes the promoting relationship between vegetation and landslide erosion, which provides a new scientific perspective on watershed management to prevent shallow landslide disasters and manage and develop watershed vegetation.

Landslides pose a greater potential risk to the Sichuan-Tibet Transportation Project, and extensive landslide inventory and mapping are essential to prevent and control geological hazards along the Sichuan-Tibet Transportation Corridor (STTC). Recently proposed landslide detection methods mainly focused on new landslides with high vegetation. In addition, there are still challenges in automatic detection of old landslides using optical images. In this paper, two methods, namely mask region-based convolutional neural networks (Mask R-CNN) and transfer learning Mask R-CNN (TL-Mask R-CNN), are presented for detecting and segmenting new and old landslides, respectively. An optical remote sensing dataset for landslide recognition along the Sichuan-Tibet Transportation Corridor (LRSTTC) is constructed as an evaluation benchmark. Our experimental results show that the recall rate and F1-score of the proposed method for new landslide detection can reach 78.47% and 79.80%, respectively. Transfer learning is adopted to detect old landslides, and our experimental results show that evaluation indices can be further improved by about 10%. Furthermore, TL-Mask R-CNN has been applied to identify ice avalanches based on the characteristics of landslides. It appears that our proposed methods can detect and segment landslides effectively along the STTC with the constructed LRSTTC dataset, which is essential for studying and preventing landslide hazards in mountainous areas.

Landslides inventories are crucial documents for extrapolating the spatial distribution of landslides that occurred in the past. However, the production of landslide hazards maps also needs deepening the knowledge on the temporal distribution of landslide activity. In this regard, historical maps and archive documents represent important sources of data and information on territory evolution through time and especially on landslides occurrence. Nevertheless, these kinds of data sources are rarely considered as they often must be interpreted integrating different disciplines. This work shows how historical and geomorphological data on landslides occurrence in an area of the Northern Apennines can be integrated to produce a synthesis map. This synthesis map put together analogic and digital information allowing to delineate the landscape evolution, in terms of spatial and especially temporal changes occurred in the study area representing a tool that could be useful for the landslide hazard assessment in territory management. [ABSTRACT FROM AUTHOR]

Vulnerability mapping is necessary to reduce the effects of a landslide disaster. This study presents an approach to map vulnerability induced by a landslide hazard. The vulnerability was assessed by improved multi-criteria decision making (MCDM), in which the effects of human factors were incorporated. In the proposed approach, the weights of the indicators and factors were calculated using an integration method, including a grey decision-making trial and evaluation laboratory and the analytical hierarchy process (AHP) to calculate each index and factor's weight. The effect of human factors is expressed as the ability of residents to cope with hazards in a community, which is evaluated by the adaptability index layer. The adaptability index layer includes four factors: gender ratio, age, education and medical resources. VIKOR is used to score and rank the adaptability index layer. The vulnerability is then mapped using geographic information system (GIS). A case study of historical landslides in Heyuan, China, was used for validation. The results demonstrate that 17.91 % of Heyuan has the highest vulnerability, which is higher than that from the traditional method (13.76 %). The model, considering adaptability, can predict areas with high vulnerability more accurately. Management recommendations are proposed via the following four specific aspects: i) urban vulnerability management and planning; ii) inter-regional differences in socio-economic vulnerability; iii) individual perceptions of landslides and iv) enhancing urban planning and strategies in similar cities. [ABSTRACT FROM AUTHOR]

A series of shaking table tests were conducted on unreinforced and micropiles-reinforced soil landslides to investigate the seismic response of micropiles-reinforced landslide and the dynamic mechanical behaviour of micropiles in landslide under earthquake. The test results indicate that: (1) The micropiles can raise the anti-seismic performance of landslide, and it can efficiently delay the development of earthquake landslide hazards. (2) After earthquake, the flexural failure of micropile mainly occurs in the area that 1.4–4 times pile diameter above the sliding surface and the area that 1.4–3.4 times pile diameter below the sliding surface. (3) After reinforced by micropiles, the acceleration response on the slope surface of landslide can be decreased, especially the slope toe. (4) Both thrust of sliding mass and resistance of sliding bed are mainly concentrated near the sliding surface. The dynamic bending moment distributions of the three rows of micropiles present as reverse ‘S’. The maximum bending moment of the pile above sliding surface is located at the point that 3.7 times pile diameter above sliding surface, and the maximum bending moment of the pile below sliding surface is located to the point that 1.43 times pile diameter below sliding surface.

Slope‐stability models predict that steeper hillslopes require smaller hydrological triggers for shallow landslides to occur due to the added downslope pull of gravity, which should result in more frequent landslides and faster erosion. However, field observations indicate that landslide frequency does not consistently increase on steeper hillslopes. Here, we use measurements of 1,096 soil landslides in California and Switzerland, and a compilation of landslide geometries, to show that steeper hillslopes typically have thinner soils and that thin soils inhibit landslides due to enhanced roles of cohesion and boundary stresses. We find that the landscape‐averaged landslide erosion depth peaks near the threshold slope for instability, and it drops to half that value on hillslopes that are just 5° to 10° steeper. We propose that faster rates of soil creep on steeper slopes cause thin and more stable soils, which in turn reduces landslide erosion, despite the added pull of gravity. Plain Language Summary: Landslides are generally associated with steep terrain, and physics‐based models predict that tilting the same soil to steeper angles should increase the likelihood that a landslide is triggered by rainfall. Such predictions are used to assess landslide hazards and model mountain erosion. However, some landslide databases, including two presented here, show that landslides can be equally or less common on steeper slopes. We argue that this discrepancy results from differences in soil thickness. Thin soils are more easily held in place by cohesive forces from plant roots and mineral cohesion, and measurements of soil thickness consistently show that soils tend to be thinner on steeper hillslopes. Not only do thin soils reduce the likelihood of landslide occurrence but they also cause landslides to be smaller, reducing shallow‐landslide hazards and erosion on steeper slopes. We present landslide measurements from Switzerland and California that show that shallow‐landslide erosion peaks for ~30° hillslopes and rapidly decreases for steeper slopes. These results suggest that soil creep becomes more effective at transporting soil on steeper slopes—thinning soils and inhibiting landslides. Key Points: The size and density of rainfall‐triggered shallow landslides decrease as slopes steepen beyond the threshold angle for failureSteeper slopes have thinner soils that are more easily held in place by cohesion and produce smaller landslidesSoil creep prevents the accumulation of thick soils on the steepest slopes, inhibiting landslides [ABSTRACT FROM AUTHOR]

A series of shaking table tests were conducted on unreinforced and micropiles-reinforced soil landslides to investigate the seismic response of micropiles-reinforced landslide and the dynamic mechanical behaviour of micropiles in landslide under earthquake. The test results indicate that: (1) The micropiles can raise the anti-seismic performance of landslide, and it can efficiently delay the development of earthquake landslide hazards. (2) After earthquake, the flexural failure of micropile mainly occurs in the area that 1.4–4 times pile diameter above the sliding surface and the area that 1.4–3.4 times pile diameter below the sliding surface. (3) After reinforced by micropiles, the acceleration response on the slope surface of landslide can be decreased, especially the slope toe. (4) Both thrust of sliding mass and resistance of sliding bed are mainly concentrated near the sliding surface. The dynamic bending moment distributions of the three rows of micropiles present as reverse 'S'. The maximum bending moment of the pile above sliding surface is located at the point that 3.7 times pile diameter above sliding surface, and the maximum bending moment of the pile below sliding surface is located to the point that 1.43 times pile diameter below sliding surface. [ABSTRACT FROM AUTHOR]

The consideration was given to the geological and geomorphological factors of natural hazards in the Ukrainian Carpathians. Principal impact of rock lithology, geomorphological structure, underwater regime and seismic processes on the erosion, gravitational and water-gravitational processes formation was demonstrated. The research confirmed a special role of the tectonic factor and the presence of weak zones in the development of natural hazards. Weak zones are characterized by fracturing, brecciation, bedding of flysch deposits and contrast in permeability. The strength properties of rocks decrease drastically within such zones, and large-scale landslide formation processes occur, especially in the case of subparallel location of zones, relative to river valleys and slopes. The factors of hazardous geological processes were defined and classified using GIS analysis and the developed geological model. A conceptual model of landslide hazards in the area of Carpathian Mountains was developed. Weight coefficients of potentially landslide prone factors were determined from the on-site observations and archive data. An integral spatial map of landslide susceptibility was proposed. The results of this research were the basis of geological environment modeling for the assessment of hazardous geological processes impact on the functioning of infrastructure objects. [ABSTRACT FROM AUTHOR]

• Landslide risk result and its causative factors are explored in typical mountainous area. • The modified frequency ratio model improves the accuracy of landslide risk evaluation. • Frequency ratio of factors and GeoDetector reveal the impact of factors on landslide. 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. [ABSTRACT FROM AUTHOR]

In Taiwan, landslides pose serious threats to local residents and infrastructures each year due to high mountain relief and distinct seasonal precipitation distribution. Interferometric synthetic aperture (InSAR) provides a powerful tool to map terrain motion and characterize the failure mechanism of unstable slopes. However, it is challenging for the conventional InSAR technique to obtain reliable landslide information in mountainous regions because of insufficient coherent measurements and signal confusion caused by vegetation coverage and rugged terrain. In this study, we adopt an optimized multi-temporal InSAR (MTInSAR) approach to analyze the surface displacement of an artificial side slope along Freeway No. 3, where a catastrophic landslide failure occurred on 25 April 2010, in northern Taiwan. To increase the spatial extent of the deformation signal, we integrate information from both persistent scatterers (PSs) and distributed scatterers (DSs). Topographic residual and height-dependent atmospheric delays are corrected by a component-based method and joint model estimation, respectively. The results reveal the existence of slope movement with a rate of about −30 mm/year prior to the landslide failure. Further analysis shows that the temporal behaviors of downslope movement are correlated with local precipitation. The study demonstrates the need to continuously monitor and verify the stability of artificial slopes to prevent and minimize the probability of a similar landslide occurrence in the future.

Landslide susceptibility models are important for public safety, but often rely on inaccessible or unaffordable software and geospatial data. Thus, affordable and accessible landslide prediction systems would be especially useful in places that lack the infrastructure for acquiring and analyzing geospatial data. Current landslide susceptibility models and existing methodologies do not consider such issues; therefore, this study aimed to develop an accessible and affordable landslide susceptibility modeling application and methodology based on open-source software and geospatial data. This model used TRIGRS (asc format) and QGIS (Digital Elevation Models (DEMs) extracted from GeoTIFF format) with widely accessible environmental parameters to identify potential landslide risks. In order to verify the suitability of the proposed application and methodology, a case study was conducted on Lantau Island, Hong Kong to assess the validity of the results, a comparison with 1999 landslide locations. The application developed in this study showed a good agreement with the four previous landslide locations marked as highly susceptible, which proves the validity of the study. Therefore, the developing model and the cost-effective approach, in this study simulated the landslide performance well and suggested the new approach of the landslide prediction system. [ABSTRACT FROM AUTHOR]

The 2015 M 7.8 Gorkha earthquake and its aftershocks caused nearly 9000 deaths and more than 23,000 injuries and triggered thousands of landslides and other mass-wasting effects in the steep, rugged topography of Nepal. In this paper, new slope failures and tension cracks induced by the 2015 Nepal earthquake are documented in steep terrain west of the Tatopani Border Station, which lies along the deeply incised Bhote Koshi River valley, a critical trans-Himalayan transport route between south Asia and Tibet/China. The affected area west of the Tatopani station is the site of a large reactivated paleo-landslide that has experienced repeated failures. The Tatopani station is downslope from the amphitheater-like landslide scar and is constructed on distal landslide debris in a position vulnerable to future landslide and fluvial erosion hazards. We carried out detailed field investigations to document different types of geohazards around the station and present an evolutionary model for past and present landslide development. In addition, a simple numerical model was constructed to evaluate the stability of the large paleo-landslide body under different earthquake conditions. Simulated results suggest that the paleo-landslide body is stable without seismic loading. However, the large paleo-landslide will be remobilized again by large slope failures if the PGA of future earthquake exceeds 0.35 g. We suggest that the results presented in this paper should be taken into consideration during earthquake-resistant design of the border station and other infrastructural development along the Bhote Koshi Valley. [ABSTRACT FROM AUTHOR]

In the Indian Himalayan region (IHR), landslide-driven hazards have intensified over the past several decades primarily caused by the occurrence of heavy and extreme rainfall. However, little attention has been given to determining the cause of events triggered during pre- and post-Indian Summer Monsoon (ISM) seasons. In the present research, detailed geological, meteorological, and remote sensing investigations have been carried out on an extreme rainfall landslide event that occurred in Sadal village, Udhampur district, Jammu and Kashmir Himalaya, during September 2014. Toward the receding phase of the ISM (i.e., in the month of September 2014), an unusual rainfall event of ~ 488.2 mm rainfall in 24 h took place in Jammu and Kashmir Himalaya in contrast to the normal rainfall occurrence. Geological investigations suggest that a planar weakness in the affected region is caused by bedding planes that consist of an alternate sequence of hard, compact sandstone and weak claystone. During this extreme rainfall event, the Sadal village was completely buried under the rock slides, as failure occurred along the planar weakness that dips toward the valley slope. Rainfall data analysis from the Tropical Rainfall Measuring Mission (TRMM) for the preceding years homogeneous time series (July-September) indicates that the years 2005, 2009, 2011, 2012, and 2014 (i.e., closely spaced and clustering heavy rainfall events) received heavy rainfalls during the withdrawal of the ISM; whereas the heaviest rainfall was received in the years 2003 and 2013 at the onset of the ISM in the study region. This suggests that no characteristic cyclicity exists for extreme rainfall events. However, we observe that either toward the onset of the ISM or its retreat, the extreme rainfall facilitates landslides, rockfall, and slope failures in northwestern Himalaya. The spatiotemporal distribution of landslides caused by extreme rainfall events suggests its confinement toward the windward side of the Himalayan front. [ABSTRACT FROM AUTHOR]

This paper analyzes the social conditions that produce vulnerability to landslides in two neighborhoods of Teziutlan, Puebla. The goal is to elicit the logic of action that influences vulnerability of these communities, using the concept of risk habitus derived from Bourdieu’s theoretical perspective. This framework provides an analytic framework to understand the social logic and every day decision-making processes that relate to risk perceptions and responses of the residents of these landslide-prone settlements. The methods involved the quantitative interpretation of linkages among variables related to residents’ social, cultural and symbolic capitals, which were collected through two complementary surveys. The selected variables focused on previous disaster experiences and social learning, as well as cooperation networks assessment (solidarity between neighbors, trust in local authorities, experience with disaster situations, perception of risk and attachment to place) in each community. The findings show that individuals’ judgments of their own vulnerability are based on their perceptions of preparedness to face a risk situation; it does not matter the actual hazard level to which they are exposed. This relates to their high level of trust in local authorities and the belief that these authorities will help them in a disaster situation. On the other hand, both neighborhoods are certain about suffering future damages in similar conditions than previous disaster events, even though they have very different objective levels of hazard exposure. In both cases, they strongly believe in their capacity or respond to a landslide, despite that neither of them has invested time or resources in preparedness.

With the explosive development of data mining technology, higher requirements have been put forward to analyze the response of landslide deformation. However, related algorithms, such as the traditional Apriori and FP-Growth algorithms, are still in the starting period of being applied to landslide hazards. Due to monitoring data characteristics, some problems were encountered while applying these algorithms, such as poor applicability and low computational efficiency. Therefore, we propose an optimized Apriori algorithm to solve the above problems. The optimized algorithm strictly controls the front and rear itemsets' construction process and stores the factors and deformation events according to their dimensions and levels. In addition, some key calculation processes of the algorithm are well-parallelized. Based on the monitoring data of the Baishuihe landslide, three experiments were designed to verify the performance of the proposed algorithm. The results show that when the strong association rules with high factor dimension and level characteristics were obtained, the proposed algorithm's computation time was 1/432 and 1/80 of the Apriori and FP-Growth algorithms, respectively. The proposed algorithm has significant advantages in analyzing massive high-dimensional monitoring data of landslide hazards. • An optimized Apriori algorithm is proposed to analyze landslide deformation response. • Three experiments are conducted to verify the proposed algorithm performance. • The proposed algorithm performs well in terms of efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

We used interferometric synthetic aperture radar (InSAR) data from the ALOS-1 satellite to investigate the active movement of landslides along the Columbia River Gorge from 2007–2011. To increase the spatial extent of the deformation signal, we developed a coherence-based small-baseline subset (SBAS) method and derived the time series of the landslide movement. We find no significant movement on the previously mapped Washougal, Sakamania, Oregon Shore, and Wind Mountain landslides. However, we document the slow movement of the Redbluff landslide, which is part of the Cascade Landslide Complex. The InSAR data reveal up to approximately 25 cm of line-of-sight displacement over the 4-year observation period. The Redbluff landslide is seasonally activated, with accelerated movement observed during the early winter of each year. The amplitude of the seasonal signal is variable from year-to-year, with the greatest movement observed in early winter of 2009 and 2011. To assess the triggering mechanisms, we compared the displacement time series data with local precipitation data. The deformation correlated well with precipitation, implying the slow movement is controlled by rainfall that infiltrates to the base of the slide. [ABSTRACT FROM AUTHOR]

The resilience of persons with disabilities in landslide-prone areas is not well understood. Moreover, these disasters are persistent and this information is vital in enhancing socioeconomic transformation of the livelihoods of Persons with disabilities. We anchored the resilience of Persons with disabilities to landslide hazards on four factors, namely; risk exposure, socioeconomic capacity, the individual functioning capacity, and the individual's housing infrastructure. The objectives of the study were (i) to assess the level of resilience of Persons with disabilities to landslides and (ii) to explore the significant sociodemographic factors that underpin the resilience of Persons with disabilities to landslides. We used a cross-sectional design and snowball sampling to access the households respondents for interviews. The analysis included descriptive analysis and inferential statistics computed using in Statistical Package for Social Sciences (SPSS) Version 23 and Microsoft Excel version 2016. The geometric mean method was used to assess the level of resilience. The Chisquare and Kendals' Tau-b were used to ascertain the underpinning sociodemographic factors to the resilience of Persons with disabilities. Results revealed that the overall resilience was low. Sex, education level, and access to disaster training significantly controlled resilience among Persons with disabilities. We conclude that Persons with disabilities in landslideprone areas have low resilience and receive little attention with respect to rescue and recovery amidst these recurrent hazards in the area. There is a need to boost the resilience of Persons with disabilities, especially the housing infrastructure and functioning capacity taking into account their social networks to build the socioeconomic and preparedness wings of disaster resilience among Persons with disabilities. [ABSTRACT FROM AUTHOR]