Enhancing groundwater quality prediction through ensemble machine learning techniques DOI
Hadi Karimi,

Soheil Sahour,

Matin Khanbeyki

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 4, 2024

Language: Английский

Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters DOI
Majid Niazkar, Reza Piraei, Mohammad Reza Goodarzi

et al.

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

Citations

4
Leveraging machine learning in porous media DOI Creative Commons
Mostafa Delpisheh, Benyamin Ebrahimpour,

Abolfazl Fattahi

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(32), P. 20717 - 20782

Published: Jan. 1, 2024

Evaluating the advantages and limitations of applying machine learning for prediction optimization in porous media, with applications energy, environment, subsurface studies.

Language: Английский

Citations

9
A comparative hydrochemical assessment of groundwater quality for drinking and irrigation purposes using different statistical and ML models in lower gangetic alluvial plain, eastern India DOI

Sribas Kanji,

Subhasish Das,

Chandi Rajak

et al.

Chemosphere, Journal Year: 2025, Volume and Issue: 372, P. 144074 - 144074

Published: Jan. 13, 2025

Language: Английский

Citations

1
Conjunct application of machine learning and game theory in groundwater quality mapping DOI Creative Commons
Ali Nasiri Khiavi,

Mohammad Tavoosi,

Alban Kuriqi

et al.

Environmental Earth Sciences, Journal Year: 2023, Volume and Issue: 82(17)

Published: Aug. 9, 2023

Abstract Groundwater quality (GWQ) monitoring is one of the best environmental objectives due to recent droughts and urban rural development. Therefore, this study aimed map GWQ in central plateau Iran by validating machine learning algorithms (MLAs) using game theory (GT). On basis, chemical parameters related water quality, including K + , Na Mg 2+ Ca SO 4 2− Cl − HCO 3 pH, TDS, EC, were interpolated at 39 sampling sites. Then, random forest (RF), support vector (SVM), Naive Bayes, K-nearest neighbors (KNN) used Python programming language, was plotted concerning GWQ. Borda scoring validate MLAs, sample points prioritized. Based on results, among ML algorithms, RF algorithm with error statistics MAE = 0.261, MSE 0.111, RMSE 0.333, AUC 0.930 selected as most optimal algorithm. created algorithm, 42.71% studied area poor condition. The proportion region classes moderate high 18.93% 38.36%, respectively. results prioritization sites GT showed a great similarity between model. In addition, analysis condition critical non-critical based that aspects, carbonate balance, salinity general, it can be said simultaneous use MLA provides good basis for constructing Iran.

Language: Английский

Citations

17
Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest DOI
Ram Proshad, Md. Abdur Rahim, Mahfuzur Rahman

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175746 - 175746

Published: Aug. 23, 2024

Language: Английский

Citations

5
Particle swarm and grey wolf optimization: enhancing groundwater quality models through artificial neural networks DOI

Soheil Sahour,

Matin Khanbeyki,

Vahid Gholami

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 38(3), P. 993 - 1007

Published: Nov. 18, 2023

Language: Английский

Citations

12
Application of Bi-LSTM method for groundwater quality assessment through water quality indices DOI
Wafa F. Alfwzan, Mahmoud M. Selim, Saad Althobaiti

et al.

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 53, P. 103889 - 103889

Published: June 8, 2023

Language: Английский

Citations

11
Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region DOI

Loganathan Krishnamoorthy,

V. Lakshmanan

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Language: Английский

Citations

4
Comparison of extreme gradient boosting, deep learning, and self-organizing map methods in predicting groundwater depth DOI
Vahid Gholami, Mohammad Reza Khaleghi, E. Taghvaye Salimi

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(7)

Published: March 21, 2025

Language: Английский

Citations

0
Multi-spectral evaluation of total nitrogen, phosphorus and potassium content in soil using Vis-NIR spectroscopy based on a modified support vector machine with whale optimization algorithm DOI

Mochen Liu,

Yang Kuankuan,

Yinfa Yan

et al.

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 252, P. 106567 - 106567

Published: April 19, 2025

Language: Английский

Citations

0