Regional land subsidence risk assessment is often constrained by low-resolution contour-based continuous data and subjective empirical grading, which may weaken the objectivity and spatial detail of evaluation results. To address this limitation, this study establishes an enhanced methodological framework for objective regional land subsidence risk assessment by integrating high-resolution remote-sensing raster data, an enhanced raster information quantity method, AHP-EWM combined weighting, natural-break risk zoning, and sensitivity analysis. The framework organizes 11 indicators into hazard, vulnerability, and exposure components. The raster information quantity method assigns objective class-level risk scores based on observed raster deformation intensity, whereas AHP-EWM weighting combines literature-based process understanding with data-driven variability. This enhanced methodological framework supports a complete evaluation process, from index construction and class-level scoring to integrated weighting, risk zoning, and stability verification. The results indicate that deep groundwater exploitation and land subsidence rate are the dominant hazard-related factors, whereas road proximity, population density, gross domestic product, and land use strongly influence exposure-related risk. The comprehensive risk pattern shows clear spatial heterogeneity, with higher-risk zones mainly concentrated where strong subsidence hazards, high vulnerability, and dense socioeconomic exposure overlap. Sensitivity analysis indicates that most areas remain unchanged or change by only one risk class under alternative classification schemes, demonstrating good stability and robustness. This study provides a reproducible methodological reference for high-resolution and objective assessment of regional land subsidence risk. Proposes a robust index system for regional land subsidence risk assessment. Formulates an enhanced raster information quantity method in individual index scoring of land subsidence risk assessment. Assesses zoning stability through sensitivity analysis and relates higher-risk zones to infrastructure exposure. Provides transferable methodology for global geohazard-prone regions
Land subsidence induced by excessive groundwater withdrawal has become one of the most significant geohazards affecting the Konya Closed Basin, Turkey. Although previous studies have successfully monitored ground deformation using geodetic and remote sensing techniques, limited attention has been devoted to transforming deformation measurements into quantitative, spatially classified hazard information. This study presents a severity-based framework for delineating land-subsidence hazard zones and critical hotspots in the Konya metropolitan area by integrating SBAS-InSAR observations and spatial statistical analyses. A total of 82 Sentinel-1 SAR acquisitions (41 ascending and 41 descending images) acquired between January 2023 and May 2026 were processed using the Small Baseline Subset (SBAS) technique. Ascending and descending line-of-sight deformation measurements were combined to derive vertical deformation rates, which were integrated with spatial statistical indicators and a composite severity index to quantify deformation clustering and classify subsidence severity. Hazard zones and critical hotspot areas were delineated through severity-based classification and spatial connectivity analyses. The results reveal a continuous north–south-oriented subsidence deformation belt extending across the eastern Konya. Maximum vertical subsidence rates exceeded 230 mm/yr, while spatial statistical analyses confirmed strongly clustered and statistically significant deformation patterns. Severity-based hazard zonation identified four hazard classes and a continuous high-hazard corridor. Clustering analysis further identified a dominant hotspot belt covering approximately 160 km2, with mean subsidence rates of approximately 142 mm/yr. A sensitivity analysis of the composite severity index weighting scheme, the spatial statistical neighborhood distance, the DBSCAN clustering parameters, and the number of Jenks severity classes confirmed that the resulting hazard zones and critical hotspot belt are robust to reasonable parameter variations. The findings demonstrate that land subsidence in Konya is organized as a spatially continuous regional-scale deformation system rather than a collection of isolated subsidence centers. The proposed framework transforms InSAR-derived deformation measurements into quantitative, decision-support hazard information and provides a transferable methodology for land-subsidence hazard assessment in groundwater-stressed urban environments.
S. Yalvaç, Olga Bjelotomić Oršulić· Remote Sensing· 0 citations
Typhoons often cause severe casualties, property losses, and infrastructure damage, and high-resolution spatial risk assessment is an important basis for developing effective disaster prevention and mitigation strategies. However, most existing typhoon risk assessments are conducted at relatively coarse spatial scales and provide limited representation of intra-urban differences in mitigation capacity. To address this gap, this study takes Haikou, China, as the study area and develops a spatially detailed mitigation-capacity indicator system. Mitigation capacity is incorporated as a key dimension into the conventional hazard–exposure–vulnerability framework. Based on multi-source geospatial data, data mining, and spatial analysis, a risk assessment system comprising 23 indicators was established. Indicator weights were determined using the analytic hierarchy process, and all indicator layers were harmonized to generate a typhoon risk map on a 30 m analytical grid. The results show that the high-resolution risk maps can effectively characterize the spatial extent and level differentiation of typhoon risk while revealing significant spatial heterogeneity in risk at the fine grid scale. In Haikou, 18.99% of the area is classified as being at high and very high risk levels, mainly distributed along the coastal zones of Shishan Town, Xixiu Town, Changliu Town, Lingshan Town, and Yanfeng Town. A preliminary plausibility check was conducted using eight georeferenced typhoon-related fatality locations recorded from 2015 to 2024. Five were located in high- or very-high-risk zones. Given the limited sample size, this comparison does not constitute formal statistical validation, but the observed spatial correspondence provides preliminary support for the plausibility of the assessment results. This study provides spatially explicit decision support for identifying intra-urban variations in typhoon risk, delineating priority areas for disaster mitigation, and optimizing the allocation of mitigation resources.
This study develops a GIS-driven framework for multi-scale discretization and cross-scale consistency verification. By converting metro protection zones into assessment units at the macro, meso, and micro scales and overlaying their respective risk layers, the framework identifies stable high-risk segments that are consistently detected across various spatial granularities. The proposed multi-scale risk assessment framework offers several advantages over traditional approaches. The inter-station segmentation allows for system-wide risk monitoring, while fixedlength segmentation facilitates localized risk analysis, and original construction method segmentation provides detailed engineering risk assessment. The evaluation index system, encompassing geological, structural, and operational factors, ensures comprehensive risk coverage. The application of fuzzy mathematics and heuristic weighting methods enhances the objectivity of risk quantification, reducing subjective biases in the assessment process. The empirical results from Guangzhou Metro case studies validate the framework's effectiveness in identifying high-risk sections consistently across different spatial scales. This multi-scale approach supports decision-makers in implementing targeted risk mitigation measures, optimizing resource allocation, and improving the overall safety and reliability of urban rail transit systems.
Ruwen Zhao, Sen He, Si-Huang Chen et al.· GEOINFORMATICS· 0 citations
This study performs a spatiotemporal analysis and multi-dimensional attribution of land subsidence within Guangzhou's urban rail transit zones. Utilizing TS-InSAR (SBAS and PS-InSAR) on multi-source SAR imagery, we identified a “Stable North, Subsiding South” paradigm. While the urban core remains stable, extreme subsidence exceeding $280 ~\text{mm} /$ year persists in the rapidly developing Panyu and Nansha districts. Integrating GIS-based spatial overlay analysis, we quantified the driving mechanisms: a superposition of thick (15-40m) marine-terrestrial soft soils and anthropogenic pressures, including heavy building loads, groundwater extraction, and metroinduced socio-agglomeration. Tectonic faults further catalyze localized differential settlement. We propose a lightweight, intuitive analytical framework that bypasses the complexity of traditional geotechnical modeling. This approach provides essential spatial decision support for operational safety, early warning, and sustainable infrastructural planning in high-density deltaic metropolises.
Background: Groundwater serves as a fundamental resource for drinking, agriculture, and industrial processes, yet anthropogenic activities and environmental changes increasingly threaten its sustainability. This study, conducted in the Limboto Alluvial Plain, Indonesia, aimed to identify and map groundwater vulnerability by enhancing the traditional DRASTIC framework through the integration of the Multi-Influencing Factor (MIF) method. Methods: Seven thematic layers—depth to water, net recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity—were generated and integrated using Geographic Information Systems (GIS). The research replaced the conventional static weighting system with a dynamic MIF approach to systematically evaluate the interdependencies between hydrogeological parameters. The model's accuracy was empirically validated using nitrate concentration proxies to correlate high-risk zones with actual contamination levels. Findings: The study revealed three distinct vulnerability classes: low, moderate, and high. High-vulnerability zones were primarily identified in the central and southern regions, characterized by shallow water tables and highly permeable media. Validation results showed a strong correlation between these delineated high-risk zones and elevated nitrate concentrations, confirming the model's reliability in predicting sensitive areas. Conclusion: The study strongly recommends adopting this enhanced MIF-DRASTIC framework as a robust tool for sustainable groundwater management and land-use planning. It provides policymakers with a precise tool for mitigating contamination in alluvial contexts, though further longitudinal data could refine the model's temporal accuracy. Novelty/Originality of this article: This research is novel because it systematically addresses the inherent limitations of the static DRASTIC model by introducing dynamic weighting through the MIF approach. It uniquely integrates hydrogeological interdependencies within the Limboto Alluvial Plain, offering a more site-specific and refined methodology for groundwater protection that is more adaptable than traditional frameworks.
Nurfaika, Y. I. Arifin, N. Safitri· Interaction, Community Engag...· 0 citations