The use of landslide susceptibility assessment is a vital step for sustainable land-use planning and risk mitigation, especially in mountainous areas and in complex tectonic and geomorphological environments. The present study aims to assess the landslide susceptibility in the Bradost Anticline through an integrated Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approach. The following 10 conditioning factors were considered: geology, lineament density, slope, elevation, stream density, gully density, slope curvature, topographic wetness index, normalized difference vegetation index, land use/land cover. All the factors were treated, standardized and visualised as thematic GIS layers. The AHP was then applied to normalize the weights and weighted layers were combined to create a landslide susceptibility map. Very high, high and moderate stability zones cover 11.36%, 25.05% and 29.87% of the area, respectively. The low and very-low-stability areas constitute 23.05% and 10.67%, respectively, showing high or very high landslide susceptibility (33.72%) in the study area. The highest weights were recalculated for geology, lineament density, and slope (0.190, 0.188, and 0.179, respectively). An overall accuracy of 83.06%, a kappa coefficient of 0.787, and an area under the ROC curve of 0.894 were used to validate the model. These outcomes show high level of agreement with the observations in the field and high level of discriminatory performance. The framework can be used as a regional screening basis for landslide mitigation, land-use planning and infrastructure protection in vulnerable mountainous terrain.
This review examines validation and conversion of Sentinel-5P TROPOMI column data to near-surface concentrations, focusing on NO₂ and contextualizing CO, SO₂, O₃, and HCHO from 2018–2025. Following PRISMA guidelines, a three-stage Scopus search identified 1,022 studies, narrowed to 150 validation-focused studies. Studies were included if they (i) converted TROPOMI vertical column density to surface concentration and (ii) reported at least one quantitative metric (R², RMSE, MAE, or correlation) against ground observations. After full-text assessment, 15 met both criteria. A single reviewer conducted screening and extraction, a limitation. Extracted fields included conversion method, algorithm, reference type, region, and performance metrics. Bibliometric mapping showed a shift toward machine learning in 2023–2024, alongside a Global North–Global South disparity and limited research in maritime continental regions. In this predominantly mid-latitude corpus, Random Forest, XGBoost, neural networks, and deep lear
Phu Quoc Special Zone is one of the most developed island destinations in Vietnam. The island has undergone significant land use and land cover (LULC) changes due to urbanization, infrastructure development, and tourism. The objective of this study is to analyze LULC changes from 1995 to 2025 in Phu Quoc usiing Landsat satellite imagery and machine learning (ML) algorithms on the Google Earth Engine (GEE) platform. Four ML algorithms, namely Random Forest, Classification and Regression Tree, Gradient Tree Boosting, and Support Vector Machine, were employed to identify distinct LULC classes, including forest, agricultural land, built-up area, bare land and water body. The classification results yielded an overall accuracy ranging from 85.24 ± 2.81% to 94.41 ± 1.59% and Kappa coefficients ranging from 0.80 ± 0.03 to 0.93 ± 0.02 across the analyzed years, indicating that RF outperformed the other algorithms. Although forest remained the most dominant LULC class, its area decreased by 11.56% over the study period
Accurate estimates of reservoir storage and long-term capacity loss are essential for reliable operation, sediment-management planning, and risk-informed decision making. However, bathymetric point surveys are irregularly distributed, and the reconstruction of the submerged surface introduces interpolation uncertainty that propagates into stage–storage curves and capacity-loss estimates. This study investigates the influence of spatial interpolation uncertainty on reservoir capacity-loss estimates and evaluates long-term sedimentation by comparing reservoir-bed conditions in 1987 and 2023 using Kamashi Reservoir (Uzbekistan) as a case study. Historical reservoir-bed conditions were reconstructed from digitized and georeferenced engineering topographic maps prepared in 1987, while the 2023 reservoir bed was modelled using bathymetric survey data. Five surface modelling approaches Inverse Distance Weighting (IDW), Radial Basis Functions (RBF), Ordinary Kriging (OK), Universal Kriging (UK), and Empirical Bayesia
Rapid urbanization has significantly transformed land use and land cover (LULC) patterns in Indian cities, creating a need for reliable spatial information to support urban planning and sustainable land management. This study investigates LULC dynamics in Prayagraj, Uttar Pradesh, India, from 2005 to 2025 and projects potential changes for 2030, 2035, and 2040 using multi-temporal Landsat imagery and Google Earth Engine (GEE). Four LULC classes: water bodies, vegetation, built-up land, and barren land were mapped using three supervised machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART). Classification performance was evaluated using overall accuracy (OA) and the Kappa coefficient. Among the three classifiers, SVM demonstrated the most consistent performance, achieving a mean OA of approximately 0.93 across the five reference years. The historical analysis revealed substantial landscape transformation, particularly the expansion of buil
