Integrative Assessment of Surface Water Contamination Using GIS, WQI, and Machine Learning in Urban–Industrial Confluence Zones Surrounding the National Capital Territory of the Republic of India

Water, Год журнала: 2025, Номер 17(7), С. 1076 - 1076

Опубликована: Апрель 4, 2025

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence impact of organic inorganic pollutants across urban–industrial confluence zones (UICZ) surrounding National Capital Territory (NCT) India. Surface samples (n = 118) were systematically collected from Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, Baghpat districts assess physical, chemical, microbiological parameters. The application spatial interpolation techniques, such as kriging inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional assessments remediation planning. GIS mapping highlighted stark disparities, with industrial hubs, like Faridabad exhibiting values exceeding 600 due untreated discharges wastewater, while rural regions, Jhajjar Baghpat, recorded below 200, reflecting minimal anthropogenic pressures. employed four ML models—linear regression (LR), random forest (RF), Gaussian process (GPR_PUK), support vector machines (SVM_Poly)—to predict high precision. SVM_Poly emerged most effective model, achieving testing CC, RMSE, MAE 0.9997, 11.4158, 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging models, this prediction beyond conventional computation, enabling extrapolation early contamination detection data-scarce regions. Sensitivity analysis identified total suspended solids critical predictor influencing WQI, underscoring its relevance monitoring programs. research uniquely integrates algorithms analytics, providing a novel methodological contribution assessment. findings emphasize urgency mitigating fate transport protect Delhi’s hydrological ecosystems, presenting robust decision-support system for policymakers environmental managers.

Язык: Английский

---

Groundwater quality assessment for irrigation in coastal region (Güzelyurt), Northern Cyprus and importance of empirical model for predicting groundwater quality (electric conductivity)

Environmental Earth Sciences, Год журнала: 2025, Номер 84(8)

Опубликована: Апрель 1, 2025

Язык: Английский

---

Evaluation of the performance and complexity of water quality models for peatlands

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132421 - 132421

Опубликована: Ноя. 1, 2024

Язык: Английский

---

Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models

Journal of Cleaner Production, Год журнала: 2024, Номер unknown, С. 144300 - 144300

Опубликована: Ноя. 1, 2024

Язык: Английский

---