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

Water, Год журнала: 2024, Номер 16(19), С. 2748 - 2748

Опубликована: Сен. 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.

Язык: Английский

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An Improved Groundwater Vulnerability Evaluation Model Based on Random Forest Algorithm and Spatio-Temporal Change Prediction Method

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106781 - 106781

Опубликована: Янв. 1, 2025

Язык: Английский

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Identification of hydrogeochemical processes in shallow groundwater using multivariate statistical analysis and inverse geochemical modeling

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Янв. 6, 2025

Язык: Английский

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An application of remote sensing and GIS used in groundwater potential zones for sustainable urban development in coastal areas between Chettikulam and Kolachal, southern India

Applied Geomatics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Язык: Английский

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Optimizing groundwater potential assessment: uncertainty reduction through sample balancing and enhanced hybrid modeling

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Язык: Английский

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Low-carbon urban development hot topics and frontier evolution: a bibliometric study from a global perspective

Frontiers in Built Environment, Год журнала: 2024, Номер 10

Опубликована: Авг. 30, 2024

The escalating challenges of global climate change have made the development low-carbon cities—urban areas committed to reducing carbon emissions through sustainable energy use, enhanced building efficiency, and transport solutions—a critical area study. However, there remains a significant gap in systematic review thematic evolution emerging frontiers within this field. This study addresses by analyzing data from Web Science database, initially retrieving 1,743 articles articles. Following PRISMA guidelines, we refined selection 1,648 high-quality publications. Using tools such as CiteSpace VOSviewer, conducted an in-depth analysis identify core authors, prolific countries/regions, leading institutions, key journals. Our revealed three evolutionary stages research on international city development. Additionally, identified seven predominant topics recent studies: land emissions, ecological environment quality, ecosystem services, human health, consumption, economic costs. These findings contribute clearer more comprehensive framework for cities, serving valuable reference scholars practitioners involved both theoretical practical aspects

Язык: Английский

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Coastal vulnerability assessment using the GALDIT model: a case study of Mukkani and Palayakayal regions in the Thamirabarani delta, Southern Tamil Nadu, India

Modeling Earth Systems and Environment, Год журнала: 2024, Номер 10(4), С. 5257 - 5271

Опубликована: Июнь 14, 2024

Язык: Английский

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GROUNDWATER POTENTIAL ASSESSMENT IN GIA LAI PROVINCE (VIETNAM) USING MACHINE LEARNING, REMOTE SENSING AND GIS

Geographia Technica, Год журнала: 2024, Номер 19(2/2024), С. 13 - 32

Опубликована: Май 15, 2024

Population growth, urbanization and rapid industrial development increase the demand for water resources.Groundwater is an important resource in sustainable socio-economic development.The identification of regions with probability existence groundwater necessary helping decision makers to propose effective strategies management this resource.The objective study construct maps potential groundwater, based on machine learning algorithms, namely deep neural networks (DNNs), XGBoost (XGB), CatBoost (CB), Gia Lai province Vietnam.In study, 12 conditioning factors, elevation, aspect, curvature, slope, soil type, river density, distance road, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), Normal Built-up (NDBI), Water (NDWI), rainfall were used, along 181 inventory points, models.The proposed models evaluated using receiver operating characteristic (ROC) curve, area under curve (AUC), root-mean-square error (RMSE), mean absolute (MAE).The results showed that predictions most accurate XGB model; CB came second, DNN was performed least well.About 4,990 km² found be category very low potential; 3,045 category; 2,426 classified as moderate, 2,665 high, 2,007 high.The methodology used creating maps.This approach, can provide valuable information factors influencing assist decisionmakers or developers managing resources sustainably.It also supports territory, including tourism.This other geographic a small change input data.

Язык: Английский

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Evaluating the influencing factors of groundwater evolution in rapidly urbanizing areas using long-term evidence

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 136, С. 103728 - 103728

Опубликована: Сен. 6, 2024

Язык: Английский

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Mapping key areas to protect high-value and high-vulnerability groundwater from pollution load: Method for management

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 123155 - 123155

Опубликована: Ноя. 2, 2024

Язык: Английский

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