Experimental study on the failure process and modes of loess spoil slope induced by rainfall and engineering disturbance.

In this paper, indoor model tests were conducted using image analysis, pore pressure, and displacement measurement methods to investigate the failure evolution process and modes of loess spoil slopes with various components under the influence of rainfall and artificial excavation. The results of the experiments reveal that, under the action of rainfall, there are two types of cracks-to-failure modes for pure loess spoil slopes. One involves the formation of a large gully through the dominant channel, while the other is characterized by step-by-step retreating soil damage between cracks. The failure exhibits three distinct stages, and after failure, the slope angle is relatively large (>45°). The process of rainfall-induced destruction affecting loess spoil containing 25% coarse-grained content similarly unfolds in three stages, ultimately resulting in the formation of a regional landslide. This landslide typically encompasses a broader damage range compared to pure loess spoil, albeit with a shallower depth of damage. After the landslide stops and stabilizes, a tiny slope (45°) is created (<45°). The excavation at the toe of the slope induces loess spoil damage in a progressive multi-stage receding manner. This study provides a reference and basis for disaster prevention and warning of spoiled ground in loess areas.

Developing a regional scale construction and demolition waste landfill landslide risk rapid assessment approach.

In recent years, construction and demolition waste (CDW) landfills landslide accidents have occurred globally, with consequences varying due to surrounding environmental factors. Risk monitoring is crucial to mitigate these risks effectively. Existing studies mainly focus on improving risk assessment accuracy for individual landfills, lacking the ability to rapidly assess multiple landfills at a regional scale. This study proposes an innovative approach utilizing deep learning models to quickly locate suspected landfills and develop risk assessment models based on surrounding environmental factors. Shenzhen, China, with significant CDW disposal pressure, is chosen as the empirical research area. Empirical findings from this study include: (1) the identification of 52 suspected CDW landfills predominantly located at the administrative boundaries within Shenzhen, specifically in the Longgang, Guangming, and Bao'an districts; (2) landfills at the lower risk of landslides are typically found near the northern borders adjacent to cities like Huizhou and Dongguan; (3) landfills situated at the internal administrative junctions generally exhibit higher landslide risks; (4) about 70 % of these landfills are high-risk, mostly located in densely populated areas with substantial rainfall and complex topographies. This study advances landfill landslide risk assessments by integrating computer vision and environmental analysis, providing a robust method for governments to rapidly evaluate risks at CDW landfills regionally. The adaptable models can be customized for various urban and broadened to general landfills by adjusting specific indicators, enhancing environmental safety protocols and risk management strategies effectively.

A case study on soil slope landslide failure and parameter analysis of influencing factors for safety factor based on strength reduction method and orthogonal experimental design.

In civil engineering, stability analysis of slope is one of the main content of design. By using the finite element limit analysis software OptumG2, a landslide geological model is established to simulate the failure process of the landslide in Huadu District, Guangzhou City, China. The analysis focused on the deformation and failure characteristics, as well as the mechanical mechanism of landslide; the landslide mode of homogeneous soil is circular sliding. Additionally, investigating the influencing factors affecting slope stability is crucial in engineering implementation; in which the five influencing factors are considered as follow: slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight, respectively. A stability calculation model for a soil slope is established under 25 working conditions based on strength reduction method and orthogonal experimental design, in which the relationship between the safety factor and slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight is obtained. As the slope height increases from 5m to 45m, the safety factor of soil slope gradually decreases from 2.21 to 0.94; As the slope gradient increases from 20° to 60°, the safety factor of soil slope decreases approximately linearly from 1.80 to 0.95; As the cohesion of soil increases from 10kpa to 30kpa, the safety factor of soil slope increases approximately linearly from 1.04 to 1.60; As the internal friction angle of soil increases from 10° to 30°, the safety factor of soil slope increases approximately linearly from 1.00 to 1.81; As the unit weight of soil increases from 13kN/m3 to 21kN/m3, the safety factor of soil slope decreases approximately linearly from 1.50 to 1.21. The influencing factors affecting the safety factor of soil slope in descending order are slope height, slope angle, soil internal friction angle, soil cohesion, and soil unit weight. The research has reference significance for studying the stability and failure laws of soil slopes and conducting landslide control on soil slopes.

InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia.

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

Landslide susceptibility assessment based on frequency ratio and semi-supervised heterogeneous ensemble learning model.

Epistemic uncertainty in data-driven landslide susceptibility assessment often tends to be increased by the limited accuracy of an individual model, as well as uncertainties associated with the selection of non-landslide samples. To address these issues, this paper centers on the landslide disaster in Ji'an City, China, and proposes a heterogeneous ensemble learning method incorporating frequency ratio (FR) and semi-supervised sample expansion. Based on the superimposed results of 12 environmental factor frequency ratios (FFR), non-landslide samples were selected and input into light gradient boosting machine (LightGBM), random forest (RF), and convolutional neural network (CNN) models for prediction along with historical landslide samples. The predicted probability values are integrated by four heterogeneous ensemble strategies to expand samples from high-confidence results. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), partition frequency ratio (PFR), and other verification methods. The results demonstrate that the negative sample based on FFR sampling is more accurate than the random sampling method, and the FR-SSELR model based on frequency ratio sampling and semi-supervised ensemble strategy exhibits the highest performance (AUC = 0.971, ACC = 0.941). A more reasonable landslide susceptibility map was drawn based on this model, with the lowest percentage of landslides in the low and very low susceptibility zones (sum of PFR = 0.194), as well as the highest percentage of landslides in the high and very high susceptibility zones (sum of PFR = 6.800). Furthermore, the FR-SSELR model improved economic benefits by 3.82-14.2%, offering valuable guidance for decision-making regarding landslide management and the sustainability of Ji'an City.

Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area.

Changes in land use significantly impact landslide occurrence, particularly in mountainous areas in northern Thailand, where human activities such as urbanization, deforestation, and slope modifications alter natural slope angles, increasing susceptibility to landslides. To address this issue, an appropriate method using soilbags has been widely used for slope stabilisation in northern Thailand, but their effectiveness and sustainability require assessment. This research highlights the need to evaluate the stability of the soilbag-based method. In this study, a case study was conducted in northern Thailand, focusing on an area characterised by high-risk landslide potential. This research focuses on numerical evaluation the slope stability of soilbag-reinforced structures and discusses environmental sustainability. The study includes site investigations using an unmanned aerial photogrammetric survey for slope geometry evaluation and employing the microtremor survey technique for subsurface investigation. Soil and soilbag material parameters are obtained from existing literatures. Modelling incorporates hydrological data, slope geometry, subsurface conditions, and material parameters. Afterwards, the pore-water pressure results and safety factors are analysed. Finally, the sustainability of soilbags is discussed based on the Sustainable Development Goals (SDGs). The results demonstrate that soilbags effectively mitigate pore-water pressures, improve stability, and align with several SDGs objectives. This study enhances understanding of soilbags in slope stabilisation and introduces a sustainable landslide mitigation approach for landslide-prone regions.

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models.

Landslide susceptibility mapping is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility mapping (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility mapping and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study's findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

Effect of freeze‒thaw cycles on root-Soil composite mechanical properties and slope stability.

Natural disasters such as landslides often occur on soil slopes in seasonally frozen areas that undergo freeze‒thaw cycling. Ecological slope protection is an effective way to prevent such disasters. To explore the change in the mechanical properties of soil under the influence of both root reinforcement and freeze‒thaw cycles and its influence on slope stability, the Baijiabao landslide in the Three Gorges Reservoir area was taken as an example. The mechanical properties of soil under different confining pressures, vegetation coverages (VCs) and numbers of freeze‒thaw cycles were studied via mechanical tests, such as triaxial compression tests, wave velocity tests and FLAC3D simulations. The results show that the shear strength of a root-soil composite increases with increasing confining pressure and VC and decreases with increasing number of freeze‒thaw cycles. Bermuda grass roots and confining pressure jointly improve the durability of soil under freeze‒thaw conditions. However, with an increase in the number of freeze‒thaw cycles, the resistance of root reinforcement to freeze‒thaw action gradually decreases. The observed effect of freeze‒thaw cycles on soil degradation was divided into three stages: a significant decrease in strength, a slight decrease in strength and strength stability. Freeze‒thaw cycles and VC mainly affect the cohesion of the soil and have little effect on the internal friction angle. Compared with that of a bare soil slope, the safety factor of a slope covered with plants is larger, the maximum displacement of a landslide is smaller, and it is less affected by freezing and thawing. These findings can provide a reference for research on ecological slope protection technology.

Good heavens! Finally a landslide analysis of basal ganglia circuitry in teleosts.

Tanimoto et al.1 report essential information on teleostean basal ganglia circuitry. This analysis opens gateways into studying neurophysiology, neuropharmacology, and behavior in zebrafish, guided by this complex functional neural system common to all vertebrates.

Slope stability assessment in the seismically and landslide-prone road segment of Gerese to Belta, Rift Valley, Ethiopia.

Slope instability on several sections of the Gerese-Belta route in Southern Ethiopia poses a major risk to infrastructure and safety. This research was aimed at evaluating certain areas of the road susceptible to slope instability. Through intensive fieldwork including geological analysis, surveys, and testing, three crucial slope portions were determined. Both limit equilibrium and finite element calculations demonstrated that these sections are problematic under different circumstances. The slope modification analysis shows that the safety factor increases as bench widths and the number of benches increase. In the slope section D1S3, this factor reached 1.222 when two benches measuring 5 meters in width were used on slide 2D. This initially showed an unstable safety factor of 0.26. Three benches of the same width were used under slide 2D. This resulted in a safety factor of 1.219. At the slope section (D1S2), flattening of the slope angle from initial 45° to 35°, 28°, 25° and 18° increases the factor of safety of the slope from initial 0.284 to 0.77, 0.89, 1.022, and 1.151 respectively under slide 2D analysis. At the slope section (D2S1), flattening the slope angle from initial 46° to 35°, 25°, 23°, and 20° increases the safety factor from initial 0.412 to 0.684, 0.920, 1.02, and 1.315 respectively. Based on the analysis of the study results, it can be concluded that the identified slope sections are susceptible to failure under actual field scenarios, depending on the conditions under which they are predicted to occur. According to this study, the Benching method is an economical method for mitigating soil slopes, as a result of which it was recommended to be used.

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region.

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

Analysis of the spatial distribution of the landslides triggered by the 1923 Great Kanto Earthquake, Japan.

The Great Kanto Earthquake that occurred in the southern part of Kanto district, Japan, on September 1, 1923, was reported to have triggered numerous landslides (over 89,080 slope failures over an area of 86.32 km2). This study investigated the relationship between the landslide occurrence caused by this earthquake and geomorphology, geology, soil, seismic ground motion, and coseismic deformation. We found that a higher landslide density was mainly related to a larger absolute curvature and a higher slope angle, as well as to several geological units (Neogene plutonic rock, accretionary prism, and metamorphic rocks). Moreover, we performed decision tree analyses, which showed that slope angle, geology, and coseismic deformation were correlated to landslide density in that order. However, no clear correlation was found between landslide density and seismic ground motion. These results suggest that landslide density was greater in areas of large slope angle or fragile geology in the area with strong shaking enough to trigger landslides.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis.

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models-Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism-were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as 'Very High' susceptibility-more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN's 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted 'Soil Texture', 'Geology', 'Distance to Road', and 'Slope' as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

GIS-based landslide susceptibility zoning using a coupled model: a case study in Badong County, China.

Landslide susceptibility zoning is necessary for landslide risk management. This study aims to conduct the landslide susceptibility evaluation based on a model coupled with information value (IV) and logistic regression (LR) for Badong County in Hubei Province, China. Through the screening of landslide predisposing factors based on correlation analysis, a spatial database including 11 landslide factors and 588 historical landslides was constructed in ArcGIS. The IV, LR and their coupled model were then developed. To validate the accuracy of the three models, the receiver operating characteristic curves (ROC) and the landslide density curves were correspondingly created. The results showed that the areas under the receiver operating characteristic curve (AUCs) of the three models were 0.758, 0.786 and 0.818, respectively. Moreover, the landslide density increased exponentially with the landslide susceptibility, but the coupled model exhibited a higher growth rate among the three models, indicating good performance of the proposed model in landslide susceptibility evaluation. The landslide susceptibility map generated by the coupled model demonstrated that the high and very high landslide susceptibility area mainly concentrated along rivers and roads. Furthermore, by counting the landslide numbers and analyzing the landslide susceptibility within each town in Badong County, it was discovered that Yanduhe, Xinling, Dongrangkou and Guandukou were the main landslide-prone areas. This research will contribute to landslide prevention and mitigation and serve as a reference for other areas.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The results of this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.