Applications of machine learning to water resources management: A review of present status and future opportunities

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 441, P. 140715 - 140715

Published: Jan. 11, 2024

Water is the most valuable natural resource on earth that plays a critical role in socio-economic development of humans worldwide. used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at global scale, coupled with predicted climate change-induced impacts, warrants need proactive effective management water resources. Over recent decades, machine learning tools have been widely applied to resources management-related fields often shown promising results. Despite publication several review articles applications water-related fields, this paper presents first time comprehensive techniques management, focusing achievements. study examines potential advanced improve decision support systems sectors within realm which includes groundwater streamflow forecasting, distribution systems, quality wastewater treatment, demand consumption, marine energy, drainage flood defence. This provides an overview state-of-the-art approaches industry how they can be ensure supply sustainability, quality, drought mitigation. covers related studies provide snapshot industry. Overall, LSTM networks proven exhibit reliable performance, outperforming ANN models, traditional established physics-based models. Hybrid ML exhibited great forecasting accuracy across all showing superior computational power over ANNs architectures. In addition purely data-driven physical-based hybrid models also developed prediction performance. These efforts further demonstrate Machine powerful practical tool management. It insights, predictions, optimisation capabilities help enhance sustainable use development, healthy ecosystems human existence.

Language: Английский

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Optimizing water resource management in tropical drought-prone regions through hybrid MCDM techniques: A water-stress mapping approach

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 57, P. 102171 - 102171

Published: Jan. 15, 2025

Language: Английский

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Assessment of soil heavy metal pollution and associated ecological risk of agriculture dominated mid-channel bars in a subtropical river basin

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 9, 2023

The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses pollution its threat to ecology from mid-channel bar's (char) agricultural Damodar River basin, India. For this, contamination factor (CF), enrichment (EF), geoaccumulation index (Igeo), index, risk (RI) were measured on 60 samples at 30 stations (2 each station, i.e., surface sub-surface) different parts bar. CF EF indicate that both levels char have low hence portray a higher potential for future by metals. Moreover, Igeo portrays are uncontaminated moderately contaminated. Further, indices all (both levels) unpolluted with mean 0.062 soils 0.048 sub-surface soils. Both potentiality an average RI 0.20 0.19 Technique order preference similarity ideal solution (TOPSIS) indicates lower than geostatistical modeling reveals simple kriging technique was estimated as most appropriate interpolation model. investigation exhibits reduced metal is due sandy nature frequent flooding. However, limited revealed intensive practices riverine chars. Therefore, would be helpful regional planners, engineers, stakeholders basin area.

Language: Английский

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Enhancing the accuracy of groundwater level prediction at different scales using spatio-temporal graph convolutional model

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Implications of rainfall variability on groundwater recharge and sustainable management in South Asian capitals: An in-depth analysis using Mann Kendall tests, continuous wavelet coherence, and innovative trend analysis

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 24, P. 101060 - 101060

Published: Dec. 16, 2023

Language: Английский

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Groundwater level fluctuations and associated influencing factors in Rangpur District, Bangladesh, using modified Mann-Kendall and GIS-based AHP technique

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 153(3-4), P. 1323 - 1339

Published: June 22, 2023

Language: Английский

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A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges

HydroResearch, Journal Year: 2024, Volume and Issue: 7, P. 285 - 300

Published: Jan. 1, 2024

The present study aims to thoroughly review GWL depletion monitoring studies completed between 2000 and 2023 based on data-driven models GIS approaches from a global perspective. summarizes the details of reviewed papers, including location, period, time scale, key objective, input parameter, applied model, performance metrics, research gaps, limitations, rate. mean rate varied worldwide 2.9 ± 1.56 1100 33.76 mm/yr using 7.6 2.98 2046 45.27 GIS-based approaches. This assesses strength relationships various keywords analyzed co-author networks Vos-viewer. It proposes groundwater development strategy evaluated papers provide long-term solution water scarcity problem. Overall, this highlights existing gaps suggests potential future paths boost associated new knowledge increase accuracy

Language: Английский

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Analyzing groundwater level with hybrid ANN and ANFIS using metaheuristic optimization

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(4), P. 3323 - 3353

Published: Sept. 4, 2023

Language: Английский

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Uncertainty-based saltwater intrusion prediction using integrated Bayesian machine learning modeling (IBMLM) in a deep aquifer

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120252 - 120252

Published: Feb. 22, 2024

Data-driven machine learning approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden. But the performance reliability strongly influenced by different sources of uncertainty. Conventional researches generally rely on a stand-alone surrogate approach fail account errors in model outputs resulting from structural deficiencies. To overcome this issue, study proposes flexible integrated Bayesian modeling (IBMLM) method explicitly quantify uncertainties originating structures parameters models. An Expectation-Maximization (EM) algorithm is combined with averaging (BMA) find out maximum likelihood construct posterior predictive distribution. Three representing complexity incorporated framework, including artificial neural network (ANN), support vector (SVM) random forest (RF). The proposed IBMLM demonstrated field-scale real-world "1500-foot" sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intrusion (SWI) issues. This adds understanding how chloride concentration transport responds multi-dimensional extraction-injection remediation strategies sophisticated model. Results show that most exhibit r values above 0.98 NSE 0.93, both slightly higher than individual learning, confirming well established provide better predictions models, while maintaining advantage high computing efficiency. found useful predict without running simulation We conclude an explicit consideration structure uncertainty along improves accuracy predictions, also corrects bounds. applicability framework can be extended regions physical hydrogeologic difficult build due lack subsurface information.

Language: Английский

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Eisenia fetida-driven vermitechnology for the eco-friendly transformation of steel waste slag into organic amendment: An insight through microbial diversity and multi-model approach

Environmental Research, Journal Year: 2024, Volume and Issue: 251, P. 118636 - 118636

Published: March 6, 2024

Language: Английский

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