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Strong earthquake geological hazards cause significant social and economic losses. The assessment of post-earthquake socioeconomic development levels is one of the important bases from which to measure the recovery capacity of hazard areas. However, the long-term impact of geological hazards is rarely considered in the assessment of the socioeconomic development level of a mountainous earthquake-stricken area. The purpose of this paper is to study the complex relationship between earthquake geological hazard effects and socioeconomic development in the long-term post-earthquake development and reconstruction of mountainous extremely earthquake-stricken areas, to provide a reference for the study area to achieve regional sustainable development goals and high-quality development. On this basis, using the economic, social, ecological environment and other relevant data from 2008 to 2016, and using unsupervised machine learning algorithms, a socioeconomic development level evaluation model based on spectral clustering was established, and the effects of different kernel function scale parameters on the model were analyzed. The optimal parameters were determined, and the spatiotemporal analysis of the socioeconomic development level of the study area was carried out. The research results show that the performance of the evaluation model is optimal when the output category is 4 and the scale parameter is 0.26, and the scale parameter of the kernel function is an important indicator that affects the accuracy of the model. From 2008 to 2016, the socioeconomic development level of Qingchuan has always been at a very low level, Wenchuan and Beichuan have always been at a low level, Shifang and Mianzhu have always been at a high level, and Pengzhou and Dujiangyan have always been at a very high level. The socioeconomic development level of Anxian, Maoxian and Pingwu fluctuates greatly over time. Another interesting finding is that the socioeconomic development level of the mountainous earthquake-stricken area has a strong correlation with its own industrial structure and landslide hazard effects. The work done in this paper is of great significance for understanding the temporal and spatial effects of hazards and understanding the complexities of regional disaster systems. [ABSTRACT FROM AUTHOR]

During the last decades, natural hazards, such as earthquakes, volcanic eruptions, landslides, floods, storms, droughts, and cyclones, have increased in frequency and severity, influenced by climate change and population growth. These natural hazards can become sudden-onset disasters, causing human losses and property damages that affect economic stability and growth. Although there is a pressing need for risk decrease and adaptation strategies to these unexpected events, targeted natural hazard insurance would be a valuable tool to counteract governmental and individual consequences. This paper summarizes an overview of risk assessment and mitigation strategies based on geological, geomorphological, and meteorological factors. The study includes an examination of monitoring systems for movement and gas emissions, risk and emergency maps, and highlighting the vulnerability of different areas at national and regional levels. Furthermore, the paper addresses the importance of promoting comprehensive geological and geotechnical knowledge among citizens of every socio-economical group and proposing the tools to effectively deliver the message, aiming at increasing the willingness for natural hazard insurance at both individual and governmental scales for human and property protection. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide phenomenon in accumulated erodible bed sediments in a reservoir is one of the issues in hydraulic and sedimentation sciences that has received little attention. We intend to model two-dimensional changes of the water surface in a reservoir and of an erodible bed caused by a non-rigid landslide using a particle-based meshless approach. In this study, a fully explicit three-step algorithm is used. In this method, approximate numerical solution to the equations of the fluid dynamics is obtained by replacing the fluid with a set of particles. The governing equations for water flow and sand mass movement are solved for each particle. The movement of each particle, which is in interaction with other particles, is tracked. Experiments of a dam break on a dry bed, and submarine rigid and non-rigid landslides have been used to validate the method. Results indicate that the model was successfully calibrated against the measured data. Moreover, good agreement with the measured data demonstrates high capabilities of this method in simulating free-surface flows and wave-related phenomena. After the model validation, changes of erodible bed in a reservoir due to a non-rigid landslide were modelled. In this study, non-rigid landslide masses and sediment materials were modelled by non-Newtonian Carreau-Yasuda fluid, which is the novelty in the analysis of this type of natural hazard. Two possible scenarios were analyzed—one with the sliding material lighter, and the other with the sliding material heavier than the deposited sediments. The model was run until the landslide completely collapsed and its full impact was applied to the reservoir bed sediments. Additionally, we waited until the water level reached a steady state. These examples demonstrate that the model presented in this paper can be used as a reliable tool for modelling these phenomena. [ABSTRACT FROM AUTHOR]

The Loess Plateau can be considered as a landslide-prone area in northwest China. The genera consensus about the interaction between landslide deposit and terrace sediments is not well studied; this paper summarised 40 loess landslides in the South Jingyang Platform, Shaanxi Province, China to help understand of this issue. Four of the loess landslides with high mobility have been analysed in detail. Three trenches T1, T2 and T3 dug after the loess landslides LD37, LD11 and LD38 highlighted the landslide-induced changes in geomorphology and internal geometry of geology, respectively. Furthermore, observation of upwards seepage flow on the profile of trench T3 is believed to be the trigger of the high speed, and long runout flowslides in the study area. A newly developed sandbox apparatus is used to reproduce the landslide kinematics due to a mass travelling over an inclined plane. The sandbox experiments show that the sediments are sheared and pushed upwards after the collision with the deposits. The deposits are then wrapped in a space between sediments, which tends to form the 'sandwich' structure. The distal sediments are thrust when the loess deposits' kinetic energy consistently dissipates, developing the accumulated folded strata. These results reveal the deposits' interactions with the sediments in the study area and provide key guideposts regarding prevention and mitigation of loess landslide hazards. [ABSTRACT FROM AUTHOR]

The study of landslide initiation mechanisms and dynamic process analysis is important for monitoring, predicting, and controlling landslide occurrence. A giant landslide that occurred on July 19, 2018, in Yanyuan County, Southwest China, was investigated, providing a research example. In this paper, the failure mechanism and dynamic process of the Boli landslide were analyzed using field investigations, laboratory experiments, unmanned aerial vehicle (UAV) photogrammetry, remote sensing images, hydrological calculations, and numerical simulations. The results indicated that the antecedent rainfall was not the direct factor to saturate the sliding mass. The runoff supply provided by the comb-like channel group upstream of the landslide reached 709.5 mm, which was an important hydrodynamic triggering point for accelerating the sliding mass to saturation and instability. Numerical simulation results showed that the evolution of the landslide included three stages: low-speed initiation, accelerating sliding, and flow deposition. The maximum sliding speed of the landslide occurred during the second stage and reached 39 m/s. From the source zone to the Taozi Gully, the silt content decreased from 67.9 to 40%, which was an internal particle–triggering factor of the alteration of the sliding mass from the liquefaction-resistant state to the liquefaction-prone state. The study has revealed the failure mechanism of the landslide and presented its dynamic process, which could be used to provide a reference for the study of similar landslides mainly induced by runoff supplied by such channel groups. [ABSTRACT FROM AUTHOR]

This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model's performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney U test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

Landslide dams are formed mostly in V-shaped valleys, with poor stability and a high risk of failure. Once landslide dams fail, they may cause floods or debris flows that endanger lives, property, and infrastructure downstream. Therefore, it is important to evaluate the stability immediately once a landslide dam is formed. The geometry of a landslide dam is the most critical to its stability assessment. However, the complex terrain and geomorphological conditions in the disaster area often make field surveys difficult. Aerial photography and satellite imagery technology are usually limited by poor weather conditions, which bring severe restrictions to the timely acquisition of the geometry of a landslide dam. In this paper, a discrete element simulation model was proposed and verified by two experiments, and then a series of numerical simulations were performed to investigate the geometry and formation process of landslide dams under different topographic factors including the motion path dip-angle, the river bed inclination, and the angle between valley slopes (valley angle). The results show that the geometry and formation process of landslide dams are significantly affected by the topographic factors. According to the results of comprehensive numerical analysis, a simple predictive model for landslide dam geometry in V-shaped valleys was established, and two landslide dams with data collected from early events were employed to validate the predictive model. Based on the easily accessible topographical factors, the proposed model accomplished the fast prediction of landslide dam geometry. This research provides a vital basis for the stability evaluation of landslide dams and can be used for the disaster emergency response before the dams break. [ABSTRACT FROM AUTHOR]

Landslide susceptibility is the likelihood of landslide occurrence, in a specific place and time. The identification of the potential relationships between landslide susceptibility and conditioning factors is very important towards landslide hazard mitigation. In this paper, we implement a local statistical analysis model geographically weighted regression, in two catchment areas located in northern Peloponnese, Greece. For this purpose, we examined the following eight conditioning factors: elevation, slope, aspect, lithology, land cover, proximity to the drainage network, proximity to the road network, and proximity to faults. Moreover, the relationship between these factors and landsliding in the study area is examined. The local statistical analysis model was also evaluated by finding its differences with the performance of a standard global statistical model logistic regression. The results indicated that the global statistical model can be enhanced by the application of a local model. The outputs of the proposed approach favored a better understanding of the factors influencing landslide occurrence and may be beneficial to local authorities and decision-makers dealing with the mitigation of landslide hazard. [ABSTRACT FROM AUTHOR]

Understanding farmer's agricultural production decisions regarding natural hazards plays crucial role for extension of organizations and policymakers to facilitate farmers to make optimal adaptive strategies. The aspect of determinants affecting the farmer's preference of measure in adaptation procedure investigated in limited studies of literature. This study focused this research gap in scenario of Pakistan and investigated rural households' decision-making adaptation strategies process regarding landslides and flash floods hazards. The research work used household survey data of 398 purposively selected farmers of district Shangla from Khyber Pakhtunkhwa Province of Pakistan. This study used manifold-portfolio structure, assumes as farmers have several preferences of adaptation strategies all together explained adaptation options are symbiotic. Multivariate probit models were employed in this paper for the empirical estimation of the study. Variegation of crops, crop-type diversifications, cropping pattern changing, crop protection and implementation management are primary adaptation measures used by respondent households. Inadequate information regarding landslides and flash floods, lack of technical equipments and machinery, insufficient money and limited support from local government were considered foremost limits in this study region. The study conclusion indicated as farmers' perceptions, farming descriptions, farmers' socioeconomic characteristics, and institutional conditions strongly affects farmers' adaptation assessment about landslides and flash floods. In future policies, it is more appropriate as major involvement of these causative factors to make possible appropriate adopting adaptation strategies for inhabitants of local areas. [ABSTRACT FROM AUTHOR]

The purpose of this work is to carry out seismically induced landslide probabilistic hazard mapping for future seismic scenarios of Aba Prefecture and Chengdu Plain region, Sichuan Province, China. Nine earthquake events that occurred in the regions and neighboring areas are selected, which include a total of 251,260 landslide records. This work used 12 influencing factors including elevation, slope, aspect, relief, topographic wetness index (TWI), topographic position index (TPI), peak ground motion, distance to active faults, vegetation coverage, distance to roads, lithology, and annual rainfall to establish the LR model. Based on the probabilistic seismic hazard analysis (PSHA) method, the distribution of predicted seismic motion under four earthquake scenarios is calculated including frequent, occasional, rare, and very rare earthquake occurrence. Using the PGA distribution of the four scenarios as input peak ground motion parameters, we calculated the occurrence probability of coseismic landslides in the entire Aba Prefecture and Chengdu Plain region under the action of different ground motions. The result shows that the high-hazard areas are mainly concentrated in the Longmenshan fault zone, and the southern area of Kangding is also a potential high-hazard area for landsliding. Meanwhile, as the probability of exceedance decreases, the probability of corresponding earthquake-induced landslides hazard probability and the area of high-hazard regions also significantly increase. Especially, the Pengguan complex rock mass in the southwest of the Longmenshan fault zone is the potential high-hazard area for coseismic landslides. Highlights: Establishing a coseismic landslides evaluation model suitable for Aba Prefecture and Chengdu Plain region. Calculating the occurrence probability of coseismic landslides under different peak ground motions. [ABSTRACT FROM AUTHOR]

This study aimed to develop a physical-based approach for predicting the spatial likelihood of shallow landslides at the regional scale in a transition zone with extreme topography. Shallow landslide susceptibility study in an area with diverse vegetation types as well as distinctive geographic factors (such as steep terrain, fractured rocks, and joints) that dominate the occurrence of shallow landslides is challenging. This article presents a novel methodology for comprehensively assessing shallow landslide susceptibility, taking into account both the positive and negative impacts of plants. This includes considering the positive effects of vegetation canopy interception and plant root reinforcement, as well as the negative effects of plant gravity loading and preferential flow of root systems. This approach was applied to simulate the regional-scale shallow landslide susceptibility in the Dadu River Basin, a transition zone with rapidly changing terrain, uplifting from the Sichuan Plain to the Qinghai–Tibet Plateau. The research findings suggest that: (1) The proposed methodology is effective and capable of assessing shallow landslide susceptibility in the study area; (2) the proposed model performs better than the traditional pseudo-static analysis method (TPSA) model, with 9.93% higher accuracy and 5.59% higher area under the curve; and (3) when the ratio of vegetation weight loads to unstable soil mass weight is high, an increase in vegetation biomass tends to be advantageous for slope stability. The study also mapped the spatial distribution of shallow landslide susceptibility in the study area, which can be used in disaster prevention, mitigation, and risk management. [ABSTRACT FROM AUTHOR]

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (Vslope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

Shallow geological flows such as landslides, debris flows, and floods are frequently modeled mathematically in the depth-averaged framework. As the bed topography becomes steep and irregular, the classical shallow-water model loses accuracy. How to handle bed topography and gravity effects in depth-averaged models becomes important, and the validity of the model needs verifying. In this study, a differential transformation called the G2L transformation linking the global height to the local depth of the surface material is proposed. This transformation helps to reconstruct the landslide/avalanche surface for one-dimensional and two-dimensional terrains that are obviously curved as well as real landslide examples such as the Huangtian and Rongsong landslides with shallowness ratios as high as 1/4. With this G2L transformation and closures of the lateral and bed stress, two versions of a model governing the geological flow dynamics are developed to avoid difficulties in terrain treatment when using differential geometry. Version 1 of the model is a coupled hyperbolic–differential system; version 2 is a modified hyperbolic system. The two proposed models are verified by a simple one-dimensional granular flow problem on a steep slope. Both mitigate problems associated with computational differential geometry of geological flows and the singularity problem of shallow-water models. In particular, the reduced gravity of several current models in landslide dynamics is a special instance of version 2. The two proposed models are well able to mathematically model the gravity geological hazards and may provide the tools required for prediction and mitigation of natural geological hazards. [ABSTRACT FROM AUTHOR]

Simulation of the process of landslide occurrence, including deformation and failure, material movement and energy dissipation of slopes, can help in predicting the features, affected area and risk of potential landslides induced by earthquakes, thus providing support to decision-makers, which is of great practical significance in hazard prevention and mitigation. Taking the Chenjiaying potential landslide in Mian County, Shaanxi Province, China, as the scenario, this study used the discrete element method to develop a numerical simulation. The results showed that deformation of the slope started from the trailing edge, then gradually extended to the middle and leading edge until the failure occurred, via a progressive process. With the increase of the friction coefficient, the movement distance, velocity and failure time of the landslide were shortened, reduced and delayed, respectively, but the peak velocity remained at about 7.5 s after the seismic loading. The proportions of kinetic energy and collision energy in the total energy dissipation also decreased as the friction coefficient increased, but friction energy increased gradually, becoming dominant. The simulation also suggested that the landslide could not only bury houses to a depth of more than 3.8 m but also generate an impact of between 1.18 × 105 and 5.50 × 105 kN on the unit width of houses at the leading edge, which would pose a major threat to life and property safety for more than 50 households. These findings will aid in the assessment of landside risk caused by earthquakes. [ABSTRACT FROM AUTHOR]

This study is a revised inventory of landslides triggered by the Rudbar-Manjil earthquake (Mw = 7.3), 1990, obtained through the study of Komak Panah and Hafezi Moghadas (1993), interpretations of the stereoscopic aerial photos, and field data. We mapped the 223 coherent landslides with a total area of 11.3 km2 and a calculated volume of 3.8 × 108 m3 using a digital elevation model (DEM, 12.5 m) within the geographical information system (GIS) software. The correlation between landslide occurrence (size and distribution) and the controlling factors was analyzed using the bivariate model and the linear automated modeling (LINEAR) procedure in the SPSS software. Both LINEAR models of landslide volume (LV, m3) and landslide area (LA, m2) indicate the distance from the fault surface rupture is the most influencing factor on size prediction of coseismic landslides. The LA is more dependent on seismic factors, whereas the LV is on the lithology and topographic factors. The result of principal component analysis (PCA) model, using the GIS, showed that most landslides occurred in the red band (including 95% of the variance of controlling factors). Thus, the selected factors to investigate the landslide occurrence are valid and the PCA can be used to identify regions prone to coseismic landslides. The curves of landslide number density (LND, landslides/km2), landslide area percentage (LAP, landslide area/total area%), LA, and LV have a multimodal distribution (several patterns of response) by the topographic and seismic factors. It is recommended that other models be applied to better investigate the local effects of factors on the coseismic landslides. [ABSTRACT FROM AUTHOR]

Landslide is a complex natural hazard that sometimes causes disaster resulting in loss of life, assets and infrastructure, especially in the Himalayas. Recent studies suggest that for effective mitigation and resilience through proper planning and policymaking, it is equally important to justify and select a suitable scientific technique that most appropriately addresses the salient causes of a landslide in any area. The principal objective of this study is to carry out a comparative assessment between two contemporary statistical techniques, i.e., the statistical information value (SIV) and index of entropy (IOE), to find out the effectiveness of the two said methods in landslide susceptibility mapping in Bhanupali-Beri region. During the analysis, the higher-resolution satellite images, i.e., World view-2 image of 2017 and Landsat-8 OLI image of 2018, have been used for delineation of various triggering parameters used for landslide susceptibility. The contemporary GIS technique integrated with the remote sensing applications was distinct in preparing the prominent landslide conditioning factor layers such as slope, slope aspect, thrust and fault proximity, geomorphology, landuse–landcover, stream power index, topographic wetness index, geology, roads proximity, lineament density and past landslide inventory. The final assessment was performed using GIS software through raster re-sampling, and the values derived for each conditioning factors were combined using defined SIV and IOE equations. The study area was categorized into five distinct landslide susceptible zones (very low, low, moderate, high and very high) using the Jenk's Natural Breaks algorithm. Index of entropy model has given better results compared to SIV. The utmost vital factors triggering landslide (estimated for entropy values) in the area are landuse–landcover with barren land and sparse vegetation followed by TWI, lineament density, geomorphology, and slope. [ABSTRACT FROM AUTHOR]

Considering the ever-increasing landslide incidences in Indian Himalayas, a methodology has been presented to assess the risk to buildings constructed in the landslide-prone areas. Since landslide is a dynamic phenomenon, an inter-disciplinary approach is required for the assessment of elements at risk (buildings in this case). Therefore, a novel remote sensing and GIS-based semi-quantitative technique has been developed by integrating the concepts of landslide susceptibility zonation (LSZ), physical vulnerability (PV) and the proximity (Prox) of buildings from the influence zone (i.e. LSZ and drainage channels). In order to understand the acceptability of risk, the landslide risk (LR) has been categorized into three risk classes as class I (low risk), class II (moderate risk) and class III (high risk). This study aims to develop a systematic and easy to adopt methodology for hilly terrains of India in a scenario of historical data scarcity and also in line with the codal provisions of the country as well as the geographical conditions. The developed methodology is implemented in a test site of Gopeshwar Township, Chamoli District Headquarter, Uttarakhand State of India, covering an area of 8.39 km2 situated in the upper Alaknanda valley. This study will be useful in increasing the safety aspects of the infrastructures and lives and also for strategic governance of developmental activities in the times ahead, especially in developing countries. [ABSTRACT FROM AUTHOR]

In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models. [ABSTRACT FROM AUTHOR]

Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes. [ABSTRACT FROM AUTHOR]

Rwanda as a landlocked country has been recurrently facing tremendous and devastating landslides having serious impacts on the environment and socioeconomic development. Analyzing landslide susceptibility and its influencing factors could ease landslide disaster control and, hence, contributes to environmental sustainability. Unfortunately, detailed information on influencing factors per respective classes and their level of correlation to the probability of landslide occurrence have not been fully analyzed in Rwanda. The purpose of this study is to reveal the spatial correlation between landslide occurrence and different classes of influencing factors using the frequency ratio (FR) approach with geographical information system (GIS) and remote sensing techniques. Initially, a landslide inventory map was prepared using 423 landslide locations that were randomly split into 75% of training datasets (318 points) and 25% (105 points) to validate the model. A multicollinearity analysis was performed among ten influencing factors using the tolerance and variance inflation factor method. These factors include elevation, slope, aspect, distance to roads, distance to rivers, the normalized difference vegetation index, land use land cover, stream power index, rainfall and soil texture. The analysis revealed no multicollinearity among these factors, and therefore, all of them were suitable for modeling process. The FR model evaluated the relationship between landslide incidence and influencing factors in their respective classes and then generated the landslide susceptibility map using GIS which revealed the western, northern and some parts of southern province as the most susceptible areas owing to the slope, LCLU, rainfall and elevation as the main influencing factors. The generated susceptibility map was validated using the area under curve which portrayed 81.2% and 84.6% for success and prediction rate, respectively. To conclude, the results of this study are essential for future initiatives regarding landslide risk reduction toward sustainability in Rwanda. However, these can be broad and may reflect the most landslide influencing factors projected in other central-east African regions. [ABSTRACT FROM AUTHOR]

The article presents the authors comments on the research study on the performance of the susceptibility model debris than the rock fall/rockslide susceptibility model in susceptibility maps in Canada. The authors evaluates the landslide susceptibility mapping for land-use planning, hazard mitigation and disaster management. The authors also discusses the importance of preparation of a landslide inventory for future failures.

We applied differential InSAR analysis to the Shiretoko Peninsula, northeastern Hokkaido, Japan. All the interferograms of long temporal baseline (~ 3 years) processed from SAR data of three L-band satellites (JERS-1, ALOS, ALOS-2) commonly indicate remarkable phase changes due to the landslide movement at the southeastern flank of Mt. Onnebetsu-dake, a Quaternary stratovolcano. The area of interferometric phase change matches to known landslide morphologies. Judging from the timing of the SAR image acquisitions, this landslide has been moving at least from 1993 to the present. Successive interferograms of 1-year temporal baseline indicate the temporal fluctuation of the landslide velocity. Especially for the descending interferograms, the positive line-of-sight (LOS) length change, which indicates large subsidence relative to the horizontal movement, is observed in the upslope section of the landslide during 1993–1998, while the negative LOS change is observed in the middle and the downslope section after 2007 indicating less subsidence. The landslide activity culminates from 2014 to 2017: the eastward and the vertical displacement rates reach ~ 6 and ~ 2 cm/yr, respectively. Utilizing high spatial resolution of ALOS and ALOS-2 data, we investigated velocity distribution inside the landslide. During 2007–2010, the eastward component of surface displacement increases toward the east, implying that the landslide extends toward the east. During 2014–2017, the vertical displacement profile exhibits spatially periodic uplift and subsidence consistent with surface gradient, which indicates the ongoing deformation driven by gravitational force. Heavy rainfall associated with three typhoons in August 2016 might have brought about an increase in the landslide velocity, possibly due to elevated pore-fluid pressure within and/or at the base of the landslide material. Also, annual rainfall would be an important factor that prescribes the landslide velocity averaged over 3 years. [ABSTRACT FROM AUTHOR]