Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2748 - 2748

Published: Sept. 27, 2024

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as result of the rapid conversion rural agricultural land urban areas under chronic drought conditions. study aims enhance Pollution Risk (GwPR) mapping integrating DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), Multivariate Adaptive Splines (MARS), alongside hydrogeochemical investigations, promote water management Province. The RF model achieved highest accuracy an Area Under Curve (AUC) 0.995 predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), GLM (0.887). RF-based map identified new high-vulnerability zones northeast northwest showed expanded moderate vulnerability zone, covering 48.46% area. Analysis revealed exceedances WHO standards for total hardness (TH), sodium, sulfates, chlorides, electrical conductivity (EC) these areas, indicating contamination from mineralized aquifers unsustainable practices. findings underscore model’s effectiveness prediction highlight need stricter monitoring management, regulating extraction improving use efficiency riverine aquifers.

Language: Английский

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An integrated approach of support vector machine (SVM) and weight of evidence (WOE) techniques to map groundwater potential and assess water quality

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 31, 2024

This study addresses the critical need for effective groundwater (GW) management in Muzaffarabad, Pakistan, amidst challenges posed by rapid urbanization and population growth. By integrating Support Vector Machine (SVM) Weight of Evidence (WOE) techniques, this aimed to delineate GW potential zones assess water quality. fills gap applying advanced machine learning geostatistical methods accurate mapping. Eight thematic layers based on topography, hydrology, geology, ecology were utilized compute model. Additionally, quality analysis was performed collected samples. The findings indicate that flat gently sloping terrains, areas with an elevation range 611 –687 m, concave slope geometries are associated higher potential. proximity drainage high-density lineament contribute increased results showed 31.1% area had excellent according WOE model, whereas SVM model indicated only 20.3% fell zone. Results both models well delineating zones. Nevertheless, application method is highly recommended which will be benefited resources related urban planning. also evaluates spatial distribution quality, a focus physical chemical parameters, including electrical conductivity, pH, turbidity, total dissolved solids, calcium, magnesium, chloride, nitrate, sulphate. Bacterial contamination assessment reveals 76% spring samples (30 out 39 samples) contaminated E.coli, raising public health concerns. Based identified exceedances WHO guidelines calcium two samples, magnesium seven sulphate ten nitrate levels below guideline across all These highlight localized issues require targeted remediation efforts safeguard health.

Language: Английский

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Prediction of Groundwater Potential Zone Using Machine Learning and Geospatial Approaches for an Industry-Dominated Area in Narayanganj, Bangladesh

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

Language: Английский

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Pan India fluoride hazard assessment in groundwater

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 478, P. 135543 - 135543

Published: Aug. 22, 2024

Language: Английский

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Smart Water Management and Resource Conservation

Advances in electronic government, digital divide, and regional development book series, Journal Year: 2024, Volume and Issue: unknown, P. 235 - 262

Published: Nov. 15, 2024

Water is essential to every living being. management and resource conservation very important provide safe clean water all. Resources of have been polluted contaminated due increasing population urbanization. Irrigation hydropower reservoir are other sources responsible for stress on earth. The main aim smart cities urban development everyone at low cost in sustainable ways. Thus, it necessary conserve resources manage the smartly. Use non-conventional irrigation, aquaculture aquifer recharge one solutions decrease use fresh these purposes. Machine learning solution managing conserving resources. Various machine models applied prediction tasks. However, deep categorization regression task. chapter objective cities.

Language: Английский

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