Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Water, Journal Year: 2023, Volume and Issue: 15(9), P. 1750 - 1750

Published: May 2, 2023

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial improving water resources planning management. In the past 20 years, significant progress has been made in management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances this field, existing literature must cover ML. This article aims to understand current state-of-the-art ML used achievements domain. It most cited employed from 2009 2022. summarises reviewed papers, highlighting their strengths weaknesses, performance criteria employed, highly identified. worth noting that accuracy was significantly enhanced, resulting a substantial improvement demonstrating robust outcome. Additionally, outlines recommendations future research directions enhance of including prediction related knowledge.

Language: Английский

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Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand

Environmental Research, Journal Year: 2024, Volume and Issue: 252, P. 118952 - 118952

Published: April 16, 2024

Language: Английский

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Deep hierarchical cluster analysis for assessing the water quality indicators for sustainable groundwater

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101119 - 101119

Published: Feb. 15, 2024

Language: Английский

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Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 8, 2025

Predicting water levels in glacier-fed lakes is vital for resource management, flood forecasting, and ecological balance. This study examines the predictive capacity of multiple climate factors affecting Blue Moon Lake Valley, fed by Baishui River glacier on Yulong Snow Mountain. The introduces a novel quad-meta (QM) ensemble model that integrates outputs from four machine learning models – extreme gradient boosting (XGB), random forest (RF), (GBM), decision tree (DT) through meta-learning to improve prediction accuracy under complex environmental conditions. High-frequency depth data, recorded every five minutes using an RBR logger, alongside variables such as temperature, wind speed, humidity, evaporation, solar radiation, rainfall, were analyzed. Temperature was identified most significant factor influencing levels, with importance score 15.69, followed atmospheric pressure (14.08) radiation (12.89), which impacted surface conditions evaporation. Relative humidity (10.24) speed (8.71) influenced lake stability mixing. QM outperformed individual models, achieved RMSE values 0.003 m (climate data) 0.001 (water data), R2 0.994 0.999, respectively. In comparison, XGB GBM exhibited higher lower scores. RF struggled 0.008 0.962, while DT performed better (RMSE: 0.006 but remained inferior proposed model. These findings demonstrate robustness approach handling particularly where fall short. highlights potential enhanced systems, recommending future research directions incorporate deep long-term forecasting expand capabilities global scale.

Language: Английский

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Novel Groundwater Quality Index (GWQI) model: A Reliable Approach for the Assessment of Groundwater

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104265 - 104265

Published: Feb. 1, 2025

Language: Английский

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Interconnections, trend analysis and forecasting of water-air temperature with water level dynamics in Blue Moon Lake Valley: A statistical and machine learning approach

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 379, P. 124829 - 124829

Published: March 8, 2025

Language: Английский

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Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(2), P. 2866 - 2890

Published: Aug. 8, 2022

Language: Английский

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Groundwater quality appraisal using IWQI and PCA for irrigation uses

ISH Journal of Hydraulic Engineering, Journal Year: 2023, Volume and Issue: 29(sup1), P. 264 - 273

Published: June 4, 2023

This study used data collected over 19 sites to assess groundwater quality status of Kurukshetra (Haryana state, India) for irrigation purposes. Irrigation Water Quality Index (IWQI), Wilcox diagram, and principal component analysis were considered determine current water quality. ArcGIS was spatial distribution parameters. Results suggest that out total samples, 5.3% samples fall under 'High Restriction,' 68.42% 'Moderate 26.28% 'Low Restriction.' USSL diagrams suggested the suitability irrigation. diagram indicated 78.9% in Excellent-to-Good region, whereas classified all low-to-medium or from medium-to-high class range. The results PCA approach demonstrated first five components (PCs) consisting nine input variable represent 79.23% variance

Language: Английский

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A novel approach for prediction of groundwater quality using gradient boosting-based algorithms

ISH Journal of Hydraulic Engineering, Journal Year: 2024, Volume and Issue: 30(3), P. 281 - 292

Published: Feb. 14, 2024

This study explores the potential of GPBoost approach for groundwater quality assessment in comparison to three other gradient boosting-based algorithms. Three methods, random search, grid and Bayesian optimization were used find optimal values various hyperparameters with all four-gradient One hundred two samples Entropy weighted water index 14 input parameters are assessing quality. The calculated EWQI drinking range between 80.4 394.96 pre-monsoon 39.6 338.79 during post-monsoon period. Moreover, spatial distribution maps displayed that central portions area fall under medium performances models compared based on multiple statistical criteria, including Correlation Coefficient (CC), root mean square error (RMSE), absolute (MAE). results reveal CC value by modeling approaches is more than 0.93, suggesting a comparable performance methods. Results terms RMSE predicting suggest (random search) model performed better models, thus competitive approaches. Relative importance analysis provided search methods highlights significance NO3−, Mg2+, TDS, EC, TH as important EWQI.

Language: Английский

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Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(30), P. 42948 - 42969

Published: June 17, 2024

Language: Английский

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