Hydrogeochemical evaluation of groundwater evolution and quality in some Voltaian aquifers of Kintampo South District, Bono East Region, Ghana: Implications from chemometric analysis, geochemical modeling and geospatial mapping techniques

HydroResearch, Journal Year: 2024, Volume and Issue: 8, P. 13 - 27

Published: Sept. 7, 2024

Language: Английский

---

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Language: Английский

---

Machine Learning-based Model for Groundwater Quality Prediction: A Comprehensive Review and Future Time–Cost Effective Modelling Vision

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Language: Английский

---

Overviewing the Machine Learning Utilization on Groundwater Research Using Bibliometric Analysis

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 936 - 936

Published: March 23, 2025

Groundwater, which constitutes 95% of the world’s freshwater resources, is widely used for drinking and domestic water supply, agricultural irrigation, energy production, bottled commercial use. In recent years, due to pressures from climate change excessive urbanization, a noticeable decline in groundwater levels has been observed, particularly arid semi-arid regions. The corresponding changes have analyzed using diverse range methodologies, including data-driven modeling techniques. Recent evidence shown notable acceleration utilization such advanced techniques, demonstrating significant attention by research community. Therefore, major aim present study conduct bibliometric analysis investigate application evolution machine learning (ML) techniques research. this sense, studies published between 2000 2023 were examined terms scientific productivity, collaboration networks, themes, methods. findings revealed that ML offer high accuracy predictive capacity, especially quality, level estimation, pollution modeling. United States, China, Iran stand out as leading countries emphasizing strategic importance management. However, outcomes demonstrated low international cooperation led deficiencies solving transboundary problems. aimed encourage more widespread effective use management environmental planning processes drew transparent interpretable algorithms, with potential yield rewarding opportunities increasing adoption technologies decision-makers.

Language: Английский

---

Irrigation Water Quality Prognostication: An Innovative Ensemble Architecture Leveraging Deep Learning and Machine Learning for Enhanced SAR and ESP Estimation in the East Coast of India

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 116433 - 116433

Published: April 1, 2025

Language: Английский

---

Assessment of Climate Change Impacts on Hydrology Using an Integrated Water Quality Index

Hydrology, Journal Year: 2024, Volume and Issue: 11(11), P. 178 - 178

Published: Oct. 24, 2024

Traditional Water Quality Indices (WQIs) often fail to capture the significant impact of flow velocity on water quality, especially under varying hydrological conditions. In this study, an Integrated Index (IWQI) was developed by combining quality parameters and rate, providing a more comprehensive assessment various Compared traditional indices, IWQI showed slightly lower correlations in individual parameter performance, but it performed well evaluating changes associated with variations. Parameters such as Total Phosphorus (TP), Coliforms (TC), Fecal (FC), which are prevalent pollutants Cheongmi River, significantly influenced scores. River evaluated using input data simulated climate change scenario. When precipitation abundant, score remained relatively stable even reduced rates. However, during periods insufficient rainfall, deteriorated sharply. While general exhibited approximately 10% decreased, TC FC rapid deterioration, rates ranging from 20% 60%. These findings underscore importance managing FC, particularly when rainfall is predicted, they major sources pollution.

Language: Английский

---

Using Random Forest Regression to Model the Spatial Distribution of Concentrations of Selected Metals in Groundwater in Forested Areas of the Wielkopolska National Park, Poland

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2191 - 2191

Published: Dec. 12, 2024

Monitoring groundwater pollution is an important issue in terms of analyzing threats to protected, environmentally valuable areas. The topographical and environmental characteristics a given area are often mentioned among the factors affecting dynamics chemistry groundwater. In this study, random forest regression (RFR) model was used determine spatial distribution selected metals, such as aluminum, calcium, iron, potassium, magnesium, manganese, sodium, zinc. role indicators describing terrain variability, derivatives digital elevation (DEM) were employed, with resolution 5 m, topography on local scale, as, others, slopes, aspect curvatures topographic position index, SAGA wetness well generalized values determined for each sampling point areas contributing their runoff. addition, parameters taken into consideration: habitat types, structure soil cover, seasons when samples collected. This study collected from 15 wells located forested Wielkopolska National Park seven dates. results obtained show that can be very good variability concentrations sodium However, case calcium zinc, no correlations found between adopted degree importance predictor order rank modeling concentration metals summary ranking predictors indicates strongest influence predicted exhibited by profile curvatures, planar multiscale TPI, then type forest. On other hand, curvature classifications, composition, seasonality exhibit smallest impact modeling.

Language: Английский

---