Coastal regions are increasingly threatened by climate change, which amplifies the frequency and severity of extreme weather events and exacerbates environmental vulnerabilities. Quang Ngai Province in central Vietnam, characterized by its complex terrain and high exposure, represents a critical case where climate-induced risks require systematic evaluation and prioritization. In this study, coastal environmental risks were prioritized through the integration of the Analytic Network Process (ANP) with the Risk Score (RS) index. A network structure of risk criteria was developed, and expert knowledge from twelve specialists with substantial academic and practical experience was elicited to perform pairwise comparisons. A hyperlink matrix was constructed, and aggregate weights were derived using a constraint matrix. These weights were linearly interpolated to determine impact levels, which were subsequently combined with probability estimates to compute the RS for each criterion. The results revealed that coastal erosion and landslides represent the most critical risk (RS = 11.45), followed by flooding in low-lying areas (RS = 8.99), while economic and livelihood losses in coastal communities and the occurrence of strong storms and extreme weather events were ranked equally (RS = 5.00). These risks are both highly probable and capable of producing extensive ecological, infrastructural, and socioeconomic disruptions. The methodological framework offers a robust basis for adaptive policymaking, the prioritization of resource allocation, and the incorporation of climate risk management into coastal development planning. The findings underscore the necessity of proactive, evidence-based interventions to safeguard vulnerable coastal systems and communities against intensifying climate change impacts.
Groundwater in the Sudda Vagu basin, located in the Bhainsa region of Nirmal District, Telangana, serves as a critical source of water for both drinking and irrigation. To evaluate its quality and suitability, 25 groundwater samples were systematically collected during the pre-monsoon (May 2022) and post-monsoon (November 2022) periods and analyzed for major cations and anions. The concentrations of sodium (Na⁺), potassium (K⁺), carbonate (CO₃²⁻), bicarbonate (HCO₃⁻), and sulfate (SO₄²⁻) were found to remain within the permissible limits recommended by the Bureau of Indian Standards (BIS), whereas elevated levels of calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), nitrate (NO₃⁻), and fluoride (F⁻) were detected in several samples, exceeding the prescribed thresholds. The pH of the groundwater ranged from 6.5 to 8.5, indicating alkaline conditions, and was deemed generally acceptable for drinking based on BIS guidelines. Hydrochemical facies classification using the Piper trilinear diagram revealed the predominance of Ca²⁺-HCO₃⁻, Na⁺-Cl⁻, and mixed water types. Irrigation suitability was further assessed through indicators including the Sodium Adsorption Ratio (SAR), Kelly Ratio (KR), and Residual Sodium Carbonate (RSC), along with the Wilcox diagram. Pre-monsoon evaluation indicated that 12 samples were categorized under the S1C2 class (low sodium hazard–medium salinity hazard), while 13 samples were assigned to the S1C3 class (low sodium hazard–high salinity hazard). Post-monsoon analysis revealed that four samples remained in S1C2, whereas 21 shifted into S1C3. The findings indicate that the majority of samples are suitable for drinking and irrigation. Continuous monitoring and the implementation of sustainable groundwater management strategies are therefore essential to ensure water security in this region.
The spatiotemporal dynamics of urban expansion and its impact on forest fragmentation within Dhaka City Corporation (DCC), a rapidly urbanizing megacity in South Asia, were critically investigated in this study. While prior research has predominantly focused on broad land-use changes and general vegetation loss, detailed analysis of forest fragmentation and its direct correlation with urban expansion intensity remains limited. This gap was addressed by integrating high-resolution Landsat satellite imagery from 2016, 2020, and 2024 with advanced landscape metrics and urban expansion indices, enabling a comprehensive and replicable assessment of urban-driven ecological disruption. Land use and land cover (LULC) classifications were generated through supervised classification in Google Earth Engine. Urban growth was quantified using the Urban Expansion Intensity Index (UEII) and the Annual Urban Expansion Rate (AUER), while forest fragmentation was evaluated via patch density, edge density, and a comprehensive fragmentation index derived from FRAGSTATS. Results indicated a marked intensification of urban expansion, with the urban area increasing from 133 km² in 2016 to 139 km² in 2024. This growth was accompanied by a rise in UEII from 0.67% to 1.35% and in AUER from 0.37% to 0.73%. Concurrently, forest ecosystems experienced significant fragmentation, as evidenced by an increase in the fragmentation index from 33 to 80 and edge density from 4 to 9 per km², indicating a progressive decline in forest continuity and heightened ecological vulnerability. Pearson correlation analysis revealed strong positive relationships between urban expansion and both edge density (r = 0.953) and the fragmentation index (r = 0.922), confirming the direct influence of urban sprawl on forest disintegration. These findings underscore the urgent need for ecologically informed urban planning. By providing a replicable methodological framework for quantifying urbanization-driven ecological disruption, this study contributes to the broader discourse on sustainable urban development and forest conservation in rapidly transforming urban landscapes.
Urban expansion, driven by rapid population growth, has increasingly encroached upon agricultural land and contributed to the degradation of ecological systems. In this study, the spatiotemporal dynamics of urban growth in Narayanganj District, Bangladesh, were assessed over a 20-year period (2003–2023) using integrated Geographic Information System (GIS) and remote sensing techniques. Land Use and Land Cover (LULC) changes were quantified, and their ecological consequences were evaluated through vegetation indices including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), alongside the Normalized Difference Built-up Index (NDBI). An LULC classification revealed a net increase of 5.61% in built-up areas, accompanied by reductions of 7.61% and 1.61% in barren land and agricultural land, respectively. The spatial pattern of urban expansion was found to be uneven, with pronounced growth observed from the northern to north-northwestern sectors of the district. A two-phase conversion analysis indicated that 15.68% of agricultural land was transformed into urban areas between 2003 and 2013, followed by a slightly lower conversion rate of 14.74% from 2013 to 2023. Notably, a statistically significant inverse correlation was detected between NDBI and both NDVI and SAVI, suggesting a measurable decline in vegetation health associated with urban intensification. These findings provide empirical and geographically grounded evidence of the adverse ecological impacts of urbanization in a peri-urban context. The integration of multi-temporal satellite images with vegetation and built-up indices enabled a comprehensive evaluation of land transformation processes and their environmental implications. The insights gained from this research may inform sustainable land use planning, urban policy formulation, and conservation strategies aimed at mitigating the loss of agricultural land and safeguarding vegetation health in rapidly urbanizing regions.
Accurate assessment of Global Navigation Satellite System (GNSS) observation data quality is essential for ensuring the reliability of positioning and navigation applications. Traditional evaluation methods, which rely on single-index weighting or simplistic combinations of multiple indicators, have proven insufficient in capturing the multifaceted nature of observation quality. To address these limitations, a comprehensive evaluation framework was developed based on a combined weighting strategy that integrates the information entropy weight method and the coefficient of variation method. This hybrid approach enhances the objectivity and sensitivity of index weighting by leveraging the strengths of both methods. Furthermore, fuzzy mathematics theory was incorporated to model the uncertainty and vagueness inherent in GNSS observations, thereby enabling the systematic identification and exclusion of low-quality and low-confidence data. This integration allows for the robust evaluation of multi-constellation GNSS observation data, accommodating complex and variable observational environments. The proposed method was validated through empirical analysis, demonstrating superior performance in distinguishing high-quality data compared to conventional single-indicator and single-weighting approaches. Experimental results confirm that the proposed framework yields more reliable and scientifically grounded quality assessments, contributing to improved accuracy and stability in downstream GNSS applications.
Côte d’Ivoire is recognized as one of the principal gold-producing countries in West Africa, where artisanal and small-scale gold mining (ASGM) constitutes the second most prevalent livelihood activity after agriculture, particularly within rural communities. As a result, high concentrations of ASGM activity have been recorded in 78% of the country’s regions. In this context, the ecological impacts of ASGM on biodiversity in the Daoukro region were examined. A diachronic geospatial analysis was conducted using satellite imagery from 2010 to 2020, in conjunction with field-based spatial data collection and semi-structured interviews. The findings reveal that extensive environmental degradation has been driven by the unregulated techniques and substances employed in gold extraction processes, including the widespread use of mercury and cyanide. These practices have resulted in severe soil contamination, structural weakening due to erosion, and inhibited vegetative regeneration. Over the decade-long period, the proportion of bare soil increased at an annual growth rate of +7.90%, while forested areas declined markedly from 31,258 hectares to 24,750 hectares—representing a cumulative reduction of 20.34%. This deforestation has contributed to the disruption and loss of native biodiversity that relies on forest ecosystems for survival. Additionally, land fragmentation and habitat degradation have reduced ecological resilience, further intensifying species vulnerability in the region. These findings underscore the urgent need for sustainable land management policies and biodiversity conservation strategies tailored to mitigate the ecological footprint of ASGM in Côte d’Ivoire.
The traditional view that the production indicator curve of water-drive gas reservoirs exhibits an upward trend is not entirely consistent with production practices. Additionally, the classical method of calibrating gas recovery using the water invasion intensity indicator curve in conjunction with the endpoint equation has shown limited applicability. To address these issues, a material balance-based dynamic prediction approach that accounts for water production was employed in this study. Both the Carter-Tracy unsteady-state model and the Schithuis steady-state model were used to calculate water invasion volumes, followed by a sensitivity analysis of the factors influencing the production indicator curve of water-drive gas reservoirs. A reassessment of the water invasion intensity indicator curve, the endpoint equation, and gas recovery in water-drive gas reservoirs was conducted, and the findings were validated using field production data from a typical reservoir. The results indicate that (a) When water production is considered, the overall production indicator curve of water-drive gas reservoirs exhibits a smooth convex shape, intersecting the cumulative gas production axis at the dynamic reserves point. The early-stage characteristics may appear as an upward trend, an approximately linear segment, or a downward bend. (b) The water invasion intensity indicator curve is only applicable for short-term predictions in the early development stage. It is more suitable for strongly water-driven gas reservoirs under steady-state conditions. The endpoint equation may vary depending on different aquifer conditions and development scenarios. (c) The larger the aquifer radius, the higher the aquifer permeability (i.e., the greater the water invasion index), the greater the compressibility coefficients of the rock and formation water, the lower the gas production rate, the deeper the gas reservoir burial depth, the more pronounced the convexity of the dimensionless production indicator curve, the higher the abandonment pressure, and the lower the gas recovery. These findings provide insights into the production indicator curve and recovery of water-drive gas reservoirs, which align with production practices, offering valuable guidance for development patterns, recovery calibration, and enhanced recovery techniques.
This study investigates the landscape dynamics and management practices affecting the Association for Forestry and Environmental Education (AFEE) forest massif, located in the monomodal agroecological zone of Cameroon. Using remote sensing data, including Landsat 8 imagery from 2014, 2019, and 2024, in conjunction with field observations, the spatio-temporal changes in land use over the past decade were mapped. Additionally, interviews were conducted with 30 local residents selected through snowball sampling to assess their perceptions of the forest's degradation and the impact of their livelihood activities on the surrounding environment. The results reveal a significant decline in the forest's ecological integrity, with the AFEE massif, originally covered entirely by mature secondary forest in 2014 (200 ha), experiencing a 77.7% reduction in forest cover by 2024. This loss has been primarily replaced by anthropogenic land uses, including young secondary forests (22.9%, 45.95 ha), swamps (17.6%, 35.35 ha), fallow lands (16.8%, 33.7 ha), rubber and oil palm plantations (1.46%, 2.91 ha), and agricultural plots (18.7%, 37.48 ha). Activities such as agriculture, hunting, artisanal sawmilling, and fishing, although central to the livelihoods of local people, have contributed significantly to the degradation of the natural landscape. These practices, while essential for local economic well-being, have negatively impacted the forest ecosystem. Given the critical role of the AFEE massif in environmental education, these findings are essential for the development of strategies that can balance the conservation of natural ecosystems with the socio-economic needs of local populations. The results underscore the need for integrated management approaches that promote both environmental preservation and sustainable livelihoods to ensure the continued provision of ecosystem services for future generations.
Aeromagnetic and Digital Elevation Model (DEM) data were analyzed to identify subsurface water-bearing zones and examine the topographical trends of surface and basement complex rocks in a portion of Kano State, Nigeria, bounded by latitudes 8°00'00''N to 9°00'00''N and longitudes 11°30'00''E to 12°30'00''E. The aeromagnetic data, sourced from the Nigerian Geological Survey Agency (NGSA), were subjected to filters, including Residual Magnetic Intensity (RMI) and Source Parameter Imaging (SPI), to estimate residual magnetic fields and depths to the basement complex rocks. The SPI results revealed two distinct depth classes: deeper and shallow regions. Deeper zones, characterized by depths ranging from 123.1 m to 414.4 m, were identified in the following areas: between 8°00'00''N and 8°38'24''N, 12°12'00''E to 12°30'00''E; 8°49'48''N to 9°00'00''N, 12°12'00''E to 12°30'00''E; 8°00'00''N to 8°07'12''N, 11°40'48''E to 12°00'00''E; 8°21'36''N to 8°37'48''N, 11°40'48''E to 12°00'00''E; 8°51'03''N to 9°00'00''N, 11°40'48''E to 12°00'00''E; and 8°14'24''N to 8°22'12''N, 11°33'36''E to 11°38'24''E. These regions, characterized by depression-like features, were suggested as optimal zones for groundwater exploration. The topographical analysis of the surface indicates that rainwater and leachates were transported toward the northern region of the study area, which exhibits relatively low elevations (448 m to 468 m above mean sea level). This region was identified as a likely accumulation area for surface water. The fresh basement complex rocks were observed to gently slope from south to north, with depth values ranging from 112.6 m to 117.7 m in deeper areas and 91.6 m to 109.8 m in shallower zones. The flow direction of surface water aligns with the underlying basement rock structure, suggesting that surface water runoff is likely influencing aquifer recharge processes. A cross-correlation coefficient of -0.99981 was observed between the surface and basement complex rock trends, indicating a strong inverse relationship between the two topographies. Consequently, the surface water accumulation zone was inferred to be a critical aquifer recharge area, though it may also facilitate the leaching of contaminants into the groundwater system, raising potential concerns for aquifer quality.
Drilling and blasting are essential operations within the mining industry, playing a critical role in material fragmentation. Despite advancements in various blasting technologies, the process remains a dominant contributor to overall mining costs. Achieving cost efficiency requires the precise configuration of blast design parameters, including explosive charge quantity, to attain desired outcomes in fragmentation, ground vibrations, fly rock, and air over-pressure. This study introduces a novel artificial intelligence (AI)-driven model, XGBoost-PSO-T, which combines eXtreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) through the integration of the Tri-Weight technique. The PSO-Tri-Weight method optimizes the hyperparameters of the XGBoost model, enhancing its predictive capabilities. The model's performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²), with the results demonstrating that the XGBoost-PSO-T system outperforms the standard XGBoost approach, achieving an RMSE of 0.657 and an R² of 0.922. These findings suggest that the XGBoost-PSO-T model is a valuable tool for predicting fragmentation outcomes and optimizing blast designs in surface mining operations. The implementation of this system is recommended to improve blasting efficiency and reduce operational costs.
The Jaintiapur-Jaflong region, strategically positioned between the subsiding Surma Basin to the south and the uplifting Shillong Massif to the north, presents a unique geological setting. This study employed geological clinometers and other field methods to ascertain the geological characteristics of the area. The regional strike was determined to be N66˚W, with a dip direction of S24˚W and a dip angle of 42.25˚. Through extensive field investigations, including geological mapping, stratigraphic logging, rock sampling, fossil analysis, and structural analysis, complemented by Global Positioning System (GPS), photography, remote sensing, and Geographic Information System (GIS) technologies, seven lithostratigraphic units were identified. These include the variegated color sandstone, mottled clay, yellowish to reddish-grey sandstone, sandy shale with intercalated silty shale, pinkish sandstone, bluish to blackish-grey shale, and limestone units, corresponding sequentially to Dupi Tila, Girujan Clay, Tipam Sandstone, Surma Group, Jenum Shale Fm, Kopili Shale, and Sylhet Limestone Fm, respectively. Five critical geological contact boundaries were delineated, with notable boundaries identified at the Dupigaon-Sari River Section, the Lalakhal-Tetulghat Section, the Nayagang-Gourishankar Section, and between the Barail and Jaintia groups at the Tamabil-Jaflong Highway Road Cut Section. These findings elucidate the geological contacts and stratigraphic units, providing significant implications for paleoenvironmental reconstruction, resource potential assessment, and stratigraphic correlation, thus enhancing the understanding of regional geological history and laying a foundation for future research endeavors.
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