Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

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Predicting groundwater levels in coastal aquifers using deep learning models: a comparative study of sedimentary and metamorphic aquifers in nova scotia

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 3, 2025

Язык: Английский

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

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

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Assessment and Modeling of Groundwater Quality Using GIS and Machine Learning Techniques for Drinking Purpose

World Environmental and Water Resources Congress 2011, Год журнала: 2023, Номер unknown, С. 1092 - 1112

Опубликована: Май 18, 2023

Groundwater contamination is a severe problem that deteriorates ecosystems, human health, and plant/animal life. Assessment modeling of groundwater quality possible solution to tackle this problem. In study, 449 samples during the year 2018 in Haryana State, India, were analyzed for 13 water parameters such as pH, electrical conductivity (EC), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), carbonate (CO32–) bicarbonate (HCO3–), nitrate (NO3–), chloride (Cl–), sulfate (SO42–), fluoride (F–). Three machine learning techniques, say generalized linear model (GLM), distributed random trees (DRF), extremely (XRT), applied estimate index (WQI) drinking purposes. The prediction performances these three models are determined by using four error metrics, namely, coefficient determination (R2), root mean square (RMSE), maximum absolute (MAE), squared logarithmic (RMSLE). GLM has shown accuracy (R2 = 0.999964, RMSE 0.759963, MAE 0.525975, RMSLE 0.005606) best estimating WQI compared DRF XRT models. Further, results suggested approximately 53% fall under excellent good category drinking. For better assessment parameters, spatial distribution map also been plotted ArcGIS. expected will contribute effective management worldwide.

Язык: Английский

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Acceptability of MEREC criteria compared to existing weighted WQI models to assess coastal groundwater quality in eastern India

Journal of Coastal Conservation, Год журнала: 2023, Номер 27(5)

Опубликована: Сен. 6, 2023

Язык: Английский

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Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain

Heliyon, Год журнала: 2024, Номер 10(11), С. e31493 - e31493

Опубликована: Май 17, 2024

Groundwater pollution can occur due to both anthropogenic and natural causes, leading a decline in water quality posing threat human health the environment. The of ground resources with chemical pollutants is often considered. To manage sustainably, ensuring their quantity crucial. Yet, testing groundwater be expensive time-consuming. So, using modeling predict parameters considered an efficient economical method. In this study, we examined three models dry regions by R programming language. random forest (RF) outperformed other developing predictive for quality. Also, multiple linear regression (MLR) model demonstrated strong performance, particularly predicting total hardness (TH) Aran Va Bidgol resources. decision tree (DT) did well but had lower performance than RF parameters. This approach efficacious field effective management protection enables assessment risks related

Язык: Английский

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Application of machine learning and Fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

Groundwater for Sustainable Development, Год журнала: 2024, Номер 27, С. 101329 - 101329

Опубликована: Сен. 6, 2024

Язык: Английский

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Beach nourishment for coastal aquifers impacted by climate change and population growth using machine learning approaches

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122535 - 122535

Опубликована: Сен. 26, 2024

Язык: Английский

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Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model

Groundwater for Sustainable Development, Год журнала: 2024, Номер 25, С. 101098 - 101098

Опубликована: Янв. 21, 2024

Язык: Английский

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Illuminating groundwater flow modeling uncertainty through spatial discretization and complexity exploration

Acta Geophysica, Год журнала: 2024, Номер unknown

Опубликована: Май 16, 2024

Язык: Английский

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