Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 241 - 264
Опубликована: Ноя. 29, 2024
Язык: Английский
Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown
Опубликована: Март 1, 2025
Abstract Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates spatiotemporal evolution chemistry Debrecen area, Hungary, from 2019 to 2024, using indexing, machine learning, multivariate statistical techniques. These techniques include self-organizing maps (SOM), hierarchical cluster analysis (HCA), principal component (PCA), indexing (GWQI). The hydrochemical revealed that Ca-Mg-HCO₃ is dominant type, with a temporal shift toward Na-HCO₃, reflecting increased salinity driven by ongoing rock-water interactions. SOM showed transition heterogeneous more uniform time, suggesting greater stability aquifer system. Elevated zones shifted spatially due recharge flow patterns, while hardness intensified expanded, indicating continued carbonate dissolution. HCA highlighted shifts composition, six clusters identified five gradual homogenization quality. PCA further confirmed this trend, linking it underlying processes, such as water–rock interactions, limited contributions anthropogenic influences. GWQI indicated general improvement most regions meeting drinking standards. However, specific areas exhibited signs localized contamination, requiring targeted management. findings underscore importance continuous monitoring detect emerging trends guide resource highlights need for sustainable practices safeguard resources ensure long-term security area.
Язык: Английский
Процитировано
3
Earth Science Informatics, Год журнала: 2025, Номер 18(2)
Опубликована: Март 26, 2025
Язык: Английский
Процитировано
0
Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 1 - 18
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 22, 2025
Identifying unauthorized agricultural wells is a critical issue for the sustainable management of water resources. Unregulated groundwater extraction through illegal has resulted in significant environmental challenges, including declining tables and ecosystem degradation. This study was conducted Bastam region Shahroud, Iran, utilizing satellite imagery spatial analysis techniques to address this issue. Satellite images from Landsat 8 Sentinel-2 were fused using ENVI software, with focus on preprocessing image integration. Subsequently, analyses, proximity density evaluations, performed ArcMap examine distribution wells. Three methods compared: The weighted sum method, kernel estimation (KDE) combined Euclidean distance, hybrid approach integrating both techniques. Through technical evaluations correlation scatter plot analysis, method identified as most effective solution. final output generated probability map depicting likelihood wells, represented by color gradient green red. Green areas indicated lower probabilities while red zones highlighted regions higher risk. validated resampling methods, confirming its potential reliable tool identifying within area.
Язык: Английский
Процитировано
0
Environmental Processes, Год журнала: 2025, Номер 12(2)
Опубликована: Апрель 25, 2025
Язык: Английский
Процитировано
0
Research in Transportation Business & Management, Год журнала: 2024, Номер 57, С. 101232 - 101232
Опубликована: Окт. 31, 2024
Язык: Английский
Процитировано
2
Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 241 - 264
Опубликована: Ноя. 29, 2024
Язык: Английский
Процитировано
0