Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review
Abstract
:1. Introduction
2. Machine Learning History and Groundwater
2.1. Symbolism
2.2. Statistical Learning
2.3. Connectionism
2.3.1. Neural Network
2.3.2. Deep Learning
2.4. ML Basic Tasks and Groundwater
3. Hybrid AI in Groundwater
3.1. More Common Hybrid AI Models
3.1.1. Artificial Neural Networks (ANN) and Support Vector Machines (SVM)
3.1.2. Genetic Algorithm (GA) and Artificial Neural Networks (ANN)
3.1.3. Wavelet Transform (WT) and Artificial Neural Networks (ANN)
3.1.4. Adaptive Neuro-Fuzzy Inference System and Genetic Programming
3.1.5. Support Vector Machines (SVM) and Random Forest (RF)
3.1.6. Artificial Neural Networks (ANN) and Kriging
3.1.7. Genetic Algorithm (GA) and Decision Tree (DT)
3.1.8. Deep Belief Networks (DBN) and Support Vector Regression (SVR)
3.1.9. Particle Swarm Optimisation (PSO) and Support Vector Regression (SVR)
3.1.10. Rough Set Theory (RST) and Support Vector Machines (SVM)
3.2. Less Commonly Hybrid AI Models
4. Discussion and Prospective
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hybrid AI Model | Most Common Applications in Groundwater Sciences (2009 to 2022) | Citations Count |
---|---|---|
Artificial Neural Networks (ANN) and Support Vector Machines (SVM) | Groundwater monitoring network optimisation | 1127 |
Genetic Algorithm (GA) and Artificial Neural Networks (ANN) | Groundwater level prediction, groundwater pumping optimisation | 795 |
Wavelet Transform (WT), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) | Groundwater level forecasting and trend/pattern identification | 658 |
Adaptive neuro-fuzzy inference system and genetic programming | Groundwater level prediction in complex hydrogeological conditions | 462 |
Support Vector Machines (SVM) and Random Forest (RF) | Impact of land use changes on groundwater resources prediction | 353 |
Artificial Neural Networks (ANN) and Kriging | Groundwater quality parameter mapping and identification of contamination risk areas | 310 |
Genetic Algorithm (GA) and Decision Tree (DT) | Groundwater quality data classification, groundwater remediation | 254 |
Deep Belief Networks (DBN) and Support Vector Regression (SVR) | Groundwater level prediction, assessment of climate change impacts on groundwater resources | 241 |
Particle Swarm Optimisation (PSO) and Support Vector Regression (SVR) | Groundwater recharge rate prediction | 201 |
Rough Set Theory (RST) and Support Vector Machines (SVM) | Decision-making for groundwater quality | 156 |
Self-Organizing Map (SOM) and Decision Tree (DT) | Groundwater data classification and identification of contamination risk areas | 112 |
Neural Network (NN) and Principal Component Analysis (PCA) | Groundwater quality assessment and identification of contamination sources | 109 |
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) | Groundwater level prediction in the urban area | 78 |
Artificial Neural Networks (ANN) and Markov Chain Monte Carlo (MCMC) | Groundwater data classification and identification of contamination risk areas | 68 |
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Zaresefat, M.; Derakhshani, R. Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review. Water 2023, 15, 1750. https://doi.org/10.3390/w15091750
Zaresefat M, Derakhshani R. Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review. Water. 2023; 15(9):1750. https://doi.org/10.3390/w15091750
Chicago/Turabian StyleZaresefat, Mojtaba, and Reza Derakhshani. 2023. "Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review" Water 15, no. 9: 1750. https://doi.org/10.3390/w15091750
APA StyleZaresefat, M., & Derakhshani, R. (2023). Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review. Water, 15(9), 1750. https://doi.org/10.3390/w15091750