Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

Freeze-thaw cycles significantly affect soil behavior, leading to pavement failures and infrastructure damage, especially in seasonally freezing regions. The application of road salt for deicing operations introduces high salt concentrations into soils, which can alter their physical properties. Salt in soils affects their freezing point, moisture migration, and overall freeze-thaw behavior. This study investigates the effects of varying sodium chloride (NaCl) concentrations on sandy soil using both the ASTM and low-temperature-gradient methods to simulate different freezing protocols. The methodology involved subjecting soil specimens with 0%, 0.2%, 1%, and 5% salt concentrations to freeze-thaw cycles and measuring parameters such as heave rate, maximum heave, water intake, moisture content, and salt migration. The results revealed that increasing salt concentration leads to a reduction in the freezing point, with the 5% NaCl concentration showing the most significant depression at 2.96 degrees C. The heave rate and maximum heave decreased with higher salt concentrations: the 5% NaCl concentration reduced the heave rate to 11.3 mm/day (ASTM method) and 1.5 mm/day (low-temperature-gradient method) from 22.5 mm/day (ASTM method) and 17.2 mm/day (low-temperature-gradient method) in control. Salt migration analysis indicated more variability in salt distribution within the soil profile under the low-temperature-gradient method, especially at higher salt concentrations. This variability is linked to osmotic suction effects, which retain more water within the soil matrix during freeze-thaw cycles. The study highlights the importance of considering both salinity and freezing protocols in understanding soil behavior under freeze-thaw conditions.

The intensification of land use has contributed to the emergence of environmental impacts such as soil loss, silting of water bodies, and reduction of biodiversity, among others. Using models capable of seasonally diagnosing environmental damage is essential in territorial planning and management, demonstrating the spatial distribution of the environment's sensitivity to developing erosion processes and quantitatively valuing soil loss. Thus, assuming a significant relationship exists between the seasonal variation in environmental fragility and the validated estimate of soil loss, reflecting the conservation status of the river basin. Therefore, this work aims to analyze the seasonal Environmental Fragility (EF) from the autumn of 2019 to the summer of 2020 using the soil loss estimate. Data such as slope, erodibility, erosivity, and the normalized difference vegetation index (NDVI) were used to achieve this. Statistical tests were also applied to assess the significance level of the models in the seasonal evaluation and the validation based on ground truth points. The results showed seasonal differentiation in the EF and the soil loss estimation. Spring was the one that resulted in the most extensive area classified as high EF (27%) and with an estimated soil loss of 0.3733 t.ha-1month-3. The summer presented the highest soil loss estimation with an average value of 0.4393 t.ha -1month-3. Autumn (0.07683 t.ha-1 month-3) and winter (0.0569 t.ha-1 month-3) showed the lowest rates of soil loss, and the most prominent areas were classified in the low class of EF, as a result, mainly of the erosivity of the rains. The results indicated by the seasonal models of EF and soil loss were validated through erosion points using spatial statistics tests.

Influenced by a warm and humid climate, the permafrost on the Qinghai-Tibet Plateau is undergoing significant degradation, leading to the occurrence of extensive thermokarst landforms. Among the most typical landforms in permafrost areas is thaw slump. This study, based on three periods of data from keyhole images of 1968-1970, the fractional images of 2006-2009 and the Gaofen (GF) images of 2018-2019, combined with field surveys for validation, investigates the distribution characteristics and spatiotemporal variation trends of thaw slumps in the Hoh Xil area and evaluates the susceptibility to thaw slumping in this area. The results from 1968 to 2019 indicate a threefold increase in the number and a twofold increase in total area of thaw slumps. Approximately 70% of the thaw slumps had areas less than 2 x 104 m2. When divided into a grid of 3 km x 3 km, about 1.3% (128 grids) of the Hoh Xil region experienced thaw slumping from 1968 to 1970, while 4.4% (420 grids) showed such occurrences from 2018 to 2019. According to the simulation results obtained using the informativeness method, the area classified as very highly susceptible to thaw slumping covers approximately 26% of the Hoh Xil area, while the highly susceptible area covers about 36%. In the Hoh Xil, 61% of the thaw slump areas had an annual warming rate ranging from 0.18 to 0.25 degrees C/10a, with 70% of the thaw slump areas experiencing a precipitation increase rate exceeding 12 mm/10a. Future assessments of thaw slump development suggest a possible minimum of 41 and a maximum of 405 thaw slumps occurrences annually in the Hoh Xil region. Under rapidly changing climatic conditions, apart from environmental risks, there also exist substantial potential risks associated with thaw slumping, such as the triggering of large-scale landslides and debris flows. Therefore, it is imperative to conduct simulated assessments of thaw slumping throughout the entire plateau to address regional risks in the future.

Engineered loess-filled gullies, which are widely distributed across China's Loess Plateau, face significant stability challenges under extreme rainfall conditions. To elucidate the regulatory mechanisms of antecedent rainfall on the erosion and failure processes of such gullies, this study conducted large-scale flume experiments to reveal their phased erosion mechanisms and hydromechanical responses under different antecedent rainfall durations (10, 20, and 30 min). The results indicate that the erosion process features three prominent phases: initial splash erosion, structural reorganization during the intermission period, and runoff-induced gully erosion. Our critical advancement is the identification of antecedent rainfall duration as the primary pre-regulation factor: short-duration (10-20 min) rainfall predominantly induces surface crack networks during the intermission, whereas long-duration (30 min) rainfall directly triggers substantial holistic collapse. These differentiated structural weakening pathways are governed by the duration of antecedent rainfall and fundamentally control the initiation thresholds, progression rates, and channel morphology of subsequent runoff erosion. The long-duration group demonstrated accelerated erosion rates and greater erosion amounts. Concurrent monitoring demonstrated that transient pulse-like increases in pore-water pressure were strongly coupled with localized instability and gully wall failures, verifying the hydromechanical coupling mechanism during the failure process. These results quantitatively demonstrate the critical modulatory role of antecedent rainfall duration in determining erosion patterns in engineered disturbed loess, transcending the prior understanding that emphasized only the contributions of rainfall intensity or runoff. They offer a direct mechanistic basis for explaining the spatiotemporal heterogeneity of erosion and failure observed in field investigations of the engineered fills. The results directly contribute to risk assessments for land reclamation projects on the Loess Plateau, underscoring the importance of incorporating antecedent rainfall history into stability analyses and drainage designs. This study provides essential scientific evidence for advancing the precision of disaster prediction models and enhancing the efficacy of mitigation strategies.

Landslides are recognized as major natural geological hazards in the mountainous region, and they are accountable for enormous human causalities, damage to properties, and environmental issues in the Teesta River basin, Sikkim, India. GIS approaches are widely used in landslide susceptibility mapping (LSM) that can help relevant authorities to mitigate landslide risk. The binary logistic regression is applied to estimate the landslide susceptibility zonation (LSZ) in the upper Teesta River basin areas. The landslide inventory data are subdivided into training data sets (70%) for applying algorithms in models and testing data sets (30%) for testing model accuracy. The LSZ mapping is designed after analyzing multicollinearity test of 14 landslide CFs and the result shows that the VIF value is less than 10, and TOL is greater than 0.1, respectively. There is no multicollinearity for the 14 conditioning landslides factors. The upper Teesta River basin is categorized into five groups: very low-to-very high landslide susceptibility zones. The results highlighted that most of the middle and southern parts of the study region are highly prone to landslides compared to the other parts. The susceptibility of landslide in the upper Teesta River basin areas validated by performing the Receiver Operating Characteristics (ROC) curve, which showed an 83% confidence level. The present research demonstrated landslide vulnerability circumstances for the Teesta River basin, Sikkim, an area prone to landslides, emphasizing the need for an effective mitigation and management roadmap.

Replacing soil with waste materials offers significant opportunities for advancing geoenvironmental practices in the construction of large-scale geostructures. The present study investigates the viability of utilizing sugarcane bagasse, a massively produced agricultural waste material, as a partial replacement for soil and its potential to control soil liquefaction. Utilization of bagasse in large geostructures not only aids in the management of a significant volume of bagasse but also facilitates the conservation of natural soil resources. Experimental investigations were conducted through a series of isotropically consolidated, stress-controlled, undrained cyclic triaxial tests. Various volumetric proportions of bagasse to sand, extending up to 50:50 (bagasse: sand), were examined to evaluate the performance of the mix under different cyclic loading conditions. The study evaluates the cyclic strength, stiffness degradation, cycle retaining index, etc., for different bagasse sand mixes across the expected cyclic stresses corresponding to Indian seismic zones 3, 4, and 5. Variation of these properties with relative density has also been studied. Results indicate that the bagasse can effectively be utilized as a geomaterial to partially replace the soil in large proportions ranging from 19 % to 41 % without compromising the initial cyclic strength of the natural soil. Notably, at an optimal content of 30 %, the bagasse sand mix exhibits higher resistance to the accumulation of excess pore water pressure, maximizing its liquefaction resistance. Furthermore, the utilization of bagasse as a partial replacement for soil increased the cyclic degradation index within the suggested range of bagasse content.

In this research, the mud diapirism phenomenon in the Membrillal sector in Cartagena is characterized to analyze its spatiotemporal evolution. The goal is to geomorphologically, geotechnically, and geologically characterize the area to zone regions with the greatest susceptibility to geological hazards and provide an updated diagnosis of the phenomenon. This study is conducted due to the risks that mud diapirism poses to infrastructure and the safety of local communities. Understanding the behavior of these structures is essential for designing effective mitigation measures and optimizing urban planning in areas affected by this phenomenon. The methodology used includes collecting secondary data and implementing geophysical, geotechnical, and laboratory tests. Among the techniques employed are the Standard Penetration Test (SPT), the excavation of test pits, and electrical resistivity tomography, which revealed mud deposits at different depths. Laboratory studies also evaluated the physical and mechanical properties of the soil, such as Atterberg limits, grain size distribution, moisture content, and expansion tests, in addition to physic-chemical analyses. Among the most relevant findings is the presence of four active mud vents and four mud ears, representing an increase compared to the previous study that only recorded three mud vents. The tests revealed mud deposits at 1.30 m and 10 m depths, consistent with the geotechnical results. Laboratory tests revealed highly plastic soils, with Liquid Limits (LL) ranging from 44% to 93% and Plastic Limits (PL) ranging from 14% to 46%. Soil classification showed various low- and high-plasticity clays (CL and CH) and silty clays (MH), presenting challenges for structural stability and foundation design. Additionally, natural moisture content varied between 15.8% and 89%, and specific gravity ranged from 1.72 to 2.75, reflecting significant differences in water retention and soil density. It is concluded that diapirism has increased in the region, with constant monitoring recommended, and the Territorial Planning Plan (POT) has been updated to include regulations that mitigate the risks associated with urban development in affected areas.

Floods pose a significant risk for Bangladesh due to the country's geographical and climatic conditions. Traditional methods of predicting flood risk often fail to do justice to the complex dynamics of flood vulnerability in this region. This report provides a comprehensive overview of the use of advanced machine learning (ML) algorithms for flood risk prediction in Bangladesh. It addresses four primary areas of research: (a) factors influencing floods considered in ML-based studies, (b) performance metrics of ML models, and (c) research gaps and future challenges in ML-based flood risk prediction. This review identified 42 unique factors that influence flooding, with precipitation, distance from the river, elevation, orientation, land use and land cover, and soil type emerging as the most important. ML models showed high predictive performance with an accuracy of 82% to 95%, depending on the algorithm and dataset used. However, there are still problems with data quality and regional variability that affect the reliability of the models. To improve flood forecasting, integrating real-time data, combining ML with physical models and promoting stakeholder engagement are crucial. Future research should focus on improving data quality, combining ML and physical models, and integrating future climate projections to refine flood hazard mapping. By considering these aspects, this study contributes to improving flood risk assessment and sustainable flood management strategies in Bangladesh, which could reduce socio-economic losses and environmental damage -in high-risk areas by 20-30.

The Hindukush region in Northwest Pakistan is a mountainous area that often faces natural disasters, such as landslides, flash floods, glacial lake outbursts, and debris flow, that alter the landscape and damage property. This study focused on the Chitral area of the Hindukush region to assess the landslide distribution and susceptibility using field observations and factor analysis. Nine landslide causative factors were selected and weighted using Geographic Information System (GIS)-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) techniques. The factors included slope, aspect, rainfall, land cover, lithology, seismicity, distance to faults, streams, and roads. Landslide susceptibility maps were generated and classified into five categories: very high, high, moderate, low, and very low. Various landslides were observed in the field comprising debris flow, debris slide, soil erosion, and rockfall. Rockfall in the study area indicates active seismicity in the Hindukush region. Furthermore, the area under the curve method validated the results, which gave 0.80 for FR and 0.73 for AHP. The results showed that most of the landslides in the study area were caused by steep slopes of mountains, followed by precipitation. The high landslide susceptibility zones in the study area matched well with the field-based landslide collections, which showed the reliability of the mapping methods. These findings can help plan and implement measures in the Hindukush region to reduce the risk and impact of landslides, such as early warning systems, slope stabilization, land use regulation, and evacuation plans.