Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions

Hygiene and Environmental Health Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100114 - 100114

Published: Oct. 1, 2024

Language: Английский

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Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 23, P. 101037 - 101037

Published: Nov. 1, 2023

The study try to evaluate the susceptibility of groundwater. DRASTIC model was implemented through GIS. Various input variables, such as water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, hydraulic conductivity, were evaluated within generate a groundwater vulnerability map. Subsequently, machine-learning algorithms (SVM, RF, GLM) employed using SDM package in R software optimize method. To assess performance pollution risk models, training validation datasets ROC curve. results revealed that approximately 40% area fell high range, while around 30% exhibited moderate risk. Evaluation machine learning models indicated their effectiveness development. RF demonstrated highest predictive power, achieving an AUC 0.98. Additionally, GLM SVM achieved values 76%. These can serve efficient techniques for evaluating managing resources. findings underscored relatively poor quality area, with excessive exploitation by agricultural sector infiltration urban sewage industrial waste identified primary causes pollution. implications these are crucial devising strategies implementing preventive measures mitigate resource associated health risks central Iran.

Language: Английский

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Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Energies, Journal Year: 2024, Volume and Issue: 17(11), P. 2738 - 2738

Published: June 4, 2024

Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning artificial intelligence, enables deeper understanding historical data efficient prediction future events based on IoT sensors. This study conducted multidimensional analysis air pollution to investigate impacts environmental policy determine long-term implications certain actions. Additionally, (ML) were employed prediction, taking spatial factors into account. By utilizing multiple low-cost sensors categorized as devices, this incorporated various locations assessed influence neighboring predictions. Different ML approaches analyzed, regression models, deep neural networks, ensemble learning. The possibility implementing such predictions publicly accessible IT mobile systems was explored. research Krakow, Poland, UNESCO-listed city that has had long struggle with pollution. Krakow also at forefront policies prohibit use solid fuels heating establishing clean transport zones. showed population growth within does not have negative impact PMx concentrations, transitioning coal-based sustainable sources emerges primary factor improving quality, especially PMx, while transportation remains less relevant. best results predicting rare smog can be achieved using linear models. Implementing actions significantly contribute building smart takes account quality life.

Language: Английский

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Artificial intelligence in groundwater management: Innovations, challenges, and future prospects

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 502 - 512

Published: Jan. 26, 2024

The integration of Artificial Intelligence (AI) in groundwater management is a transformative stage, characterized by innovation and challenges. This research paper explores the multilayered application AI this field, dividing its contributions, addressing associated challenges, revealing prospects future potential. AI-driven innovations are designed to revolutionize management, providing precise predictive modeling, real-time monitoring, data integration. However, these face challenges such as interpretability issues, specialized technical expertise requirements, limited quality quantity for effective model performance. In future, holds significant promise management. Advanced models can yield improved predictions behavior, identify vulnerable areas prone pollution depletion, prompt proactive interventions, foster collaborative platforms among scientists, policymakers, local communities. Collaborative driven offer potential synergistic engagement communities, collectively guiding resource Embracing AI's while remains pivotal sustainable resilient practices. By embracing landscape will continue evolve.

Language: Английский

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Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2250 - 2250

Published: March 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Language: Английский

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Integrating geospatial, hydrogeological, and geophysical data to identify groundwater recharge potential zones in the Sulaymaniyah basin, NE of Iraq

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 22, 2025

Abstract Groundwater is a critical resource for sustaining human activities, particularly in urban areas, where its importance exaggerated by growing water demands, expansion, and industrial activities. Ensuring future security necessitates an in-depth understanding of groundwater recharge dynamics, which are often complex influenced rapid urbanization. The alarming decline resources both rural regions underscore the urgency advanced management strategies. However, identifying evaluating potential zones (GWPZs) remains challenge due to dynamic interplay hydrogeological development factors. This study employs integrated approach combining geographic information system (GIS), remote sensing, multi-criteria decision analysis using analytical hierarchy process (MCDA-AHP) delineate GWPZs Sulaymaniyah Basin (SB). methodology further supported data validated through geophysical investigation electrical resistivity tomography (ERT) data. For MCDA-AHP, six thematic layers including rainfall, geology, lineament density, slope, drainage land use/land cover were derived from satellite imagery, geological surveys, well These ranked based on their relative influence GIS-based weighted overlay generate maps. results identified three recharge: low (11.26%), moderate (45.51%), high (43.23%). Validation ERT receiver operating characteristics (ROC) revealed strong agreement, with area under curve (AUC) accuracy 86%. findings demonstrate robustness approach, providing reliable tool minimizing hydrogeophysical exploration costs reducing number unsuccessful boreholes.

Language: Английский

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[Innovation and the Future of Hydrogeology: the point of view of ECHN-Italy]

Acque Sotterranee-Italian Journal of Groundwater, Journal Year: 2025, Volume and Issue: 14(1)

Published: April 1, 2025

[Article in Italian] Innovazione e Futuro dell’Idrogeologia: il punto di vista ECHN-Italy

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Geospatial assessment of groundwater vulnerability to pollution using the DRASTIC and AHP model in flood-affected area, Nowshera, Pakistan

Solid Earth Sciences, Journal Year: 2025, Volume and Issue: 10(2), P. 100239 - 100239

Published: April 8, 2025

Language: Английский

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Time lag effect of precipitation on groundwater level based on wavelet analysis in the People’s Victory Canal irrigation area, China

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 9, 2025

The People's Victory Canal irrigation area is an important agricultural region in the North China Plain, where groundwater resources play a crucial role both production and ecological environment. However, recent years, increasing depth of groundwater, influenced by climate change human activities, has posed significant challenges to sustainable use water region. Therefore, exploring lag effect its trends between precipitation essential for scientific management optimizing allocation. This study based on monthly average data from Xiazhuang 1993 2021. It uses methods such as continuous wavelet transform, cross-wavelet cross-correlation analysis systematically analyze changing patterns table response at different time scales. finds that during research period, generally showed downward trend, while continuously increased experienced sudden 1999, after which it rose significantly. Before 2000, there was strong correlation depth, with noticeable changes precipitation, about 58.97 days. this relationship gradually weakened, especially years other than those abundant rainfall, influence decreased significantly, 6 8 months. shows before led shallower more significantly precipitation. After reduced weakened thus enhancing effect. trend reflects mechanism recharge likely closely related intensified extraction methods, over-extraction groundwater. findings can provide basis resource area, helping formulate appropriate regulation measures ensure regional resources.

Language: Английский

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Integrating conventional and remote sensing with DC resistivity datasets to map groundwater potential areas using the analytical hierarchy process method, North Wadi Diit, Egypt

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 14, 2025

Abstract This study investigates the groundwater potential (GWP) in Wadi Diit, an arid region with promising resource development prospects, by integrating topographic, hydrogeological, and mineralogical parameters. To analyze area, a combination of conventional methods, remote sensing data from Sentinel-2, ASTER-GDEM, ASTER-L1B, as well DC resistivity datasets was utilized. The comprises Precambrian, Tertiary, Quaternary surface rock units, supporting lithosol Yermosol soil types. Barren lands dominate landscape, while southern portion experiences higher rainfall. Nine thematic layers (quartz index, carbonate slope, rainfall, drainage density, topographic wetness lineament land cover, mafic index) were classified weighted using GIS-based analytical hierarchy process, achieving model accuracy 0.0959. GWP zones categorized into very low (4.53%), (17.33%), moderate (27.05%), high (27.79%), (23.3%) categories, predominantly falling within to classifications. Validation through hydrogeological 11 wells receiver operating characteristic curve analysis (area under = 0.8) confirmed model’s reliability. measurements conducted at nine vertical electrical sounding (VES) sites Schlumberger array (AB/2 500 m) along two profiles. analyzed various inversion techniques, including unconstrained 1D-VES, laterally constrained (LCI-VES), spatially (SCI-VES), 2D-VES inversions. A 0.3 constraint factor applied assess parameters, their STDF derived SCI-VES determined be well-resolved. results identified four distinct geological layers; unconsolidated deposits, gravelly-sand sediments fresh-brackish aquifer (30–384 Ω m 3.7–15.9 depth), saturated clayey-sand saline Fractured Basement (10–137 33–90.4 depth). exhibits complex structure, characterized uplifted trending southeast southwest indicated models. north-central emerges most favorable location for substantial GWPZ, making it strategically ideal installation additional water wells.

Language: Английский

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