A systematic review of neural network applications for groundwater level prediction
Abstract Physical models have long been employed for groundwater level (GWL) prediction. Recently, artificial intelligence (AI), particularly neural networks (NNs), has gained widespread use in forecasting GWL. Forecasting of GWL is essential to enable the analysis, quantifying, and management of gr...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06817-5 |
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| author | Samuel K. Afful Cyril D. Boateng Emmanuel Ahene Jeffrey N. A. Aryee David D. Wemegah Solomon S. R. Gidigasu Akyana Britwum Marian A. Osei Jesse Gilbert Haoulata Touré Vera Mensah |
| author_facet | Samuel K. Afful Cyril D. Boateng Emmanuel Ahene Jeffrey N. A. Aryee David D. Wemegah Solomon S. R. Gidigasu Akyana Britwum Marian A. Osei Jesse Gilbert Haoulata Touré Vera Mensah |
| author_sort | Samuel K. Afful |
| collection | DOAJ |
| description | Abstract Physical models have long been employed for groundwater level (GWL) prediction. Recently, artificial intelligence (AI), particularly neural networks (NNs), has gained widespread use in forecasting GWL. Forecasting of GWL is essential to enable the analysis, quantifying, and management of groundwater. This systematic review investigates the application of NNs for GWL prediction, focusing on the architectures of the various NN models employed. The study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to screen and synthesize relevant scientific articles. Various NN architectures, such as artificial neural networks (ANNs), feedforward neural networks (FFNNs), backpropagation neural networks (BPNNs), long short-term memory (LSTM), and hybrid models, were analyzed. The results from the systematic review indicate a growing preference for hybrid models, which effectively capture hidden relationships between GWL and environmental factors. The root mean square error (RMSE) emerges as the predominant performance metric, highlighting its significance in evaluating NNs. Results from the review also highlight the significance of comprehensive, long-term datasets covering a decade for robust trend analyses and accurate predictions. The findings contribute to a deeper understanding of new trends in groundwater research such as the application of neural networks for prediction problems in groundwater research. In conclusion, a hybrid metaheuristic algorithm produced more efficient results emphasizing their efficacy. In addition, lagged values were essential input for GWL prediction. The paper addressed both technical nuances and broader environmental implications. |
| format | Article |
| id | doaj-art-6edacc5b08b04d3eb80a8a4bb406e227 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-6edacc5b08b04d3eb80a8a4bb406e2272025-08-24T11:45:25ZengSpringerDiscover Applied Sciences3004-92612025-08-017913010.1007/s42452-025-06817-5A systematic review of neural network applications for groundwater level predictionSamuel K. Afful0Cyril D. Boateng1Emmanuel Ahene2Jeffrey N. A. Aryee3David D. Wemegah4Solomon S. R. Gidigasu5Akyana Britwum6Marian A. Osei7Jesse Gilbert8Haoulata Touré9Vera Mensah10Department of Computer Science, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Physics, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Computer Science, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Physics, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Geological Engineering, College of Engineering, Kwame Nkrumah University of Science and TechnologyDepartment of Physics, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Geological Engineering, College of Engineering, Kwame Nkrumah University of Science and TechnologyDepartment of Geological Engineering, College of Engineering, Kwame Nkrumah University of Science and TechnologyAbstract Physical models have long been employed for groundwater level (GWL) prediction. Recently, artificial intelligence (AI), particularly neural networks (NNs), has gained widespread use in forecasting GWL. Forecasting of GWL is essential to enable the analysis, quantifying, and management of groundwater. This systematic review investigates the application of NNs for GWL prediction, focusing on the architectures of the various NN models employed. The study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to screen and synthesize relevant scientific articles. Various NN architectures, such as artificial neural networks (ANNs), feedforward neural networks (FFNNs), backpropagation neural networks (BPNNs), long short-term memory (LSTM), and hybrid models, were analyzed. The results from the systematic review indicate a growing preference for hybrid models, which effectively capture hidden relationships between GWL and environmental factors. The root mean square error (RMSE) emerges as the predominant performance metric, highlighting its significance in evaluating NNs. Results from the review also highlight the significance of comprehensive, long-term datasets covering a decade for robust trend analyses and accurate predictions. The findings contribute to a deeper understanding of new trends in groundwater research such as the application of neural networks for prediction problems in groundwater research. In conclusion, a hybrid metaheuristic algorithm produced more efficient results emphasizing their efficacy. In addition, lagged values were essential input for GWL prediction. The paper addressed both technical nuances and broader environmental implications.https://doi.org/10.1007/s42452-025-06817-5ReviewNeural networks (NNs)Artificial neural networks (ANNs)Groundwater level (GWL) forecastingClimate variables |
| spellingShingle | Samuel K. Afful Cyril D. Boateng Emmanuel Ahene Jeffrey N. A. Aryee David D. Wemegah Solomon S. R. Gidigasu Akyana Britwum Marian A. Osei Jesse Gilbert Haoulata Touré Vera Mensah A systematic review of neural network applications for groundwater level prediction Discover Applied Sciences Review Neural networks (NNs) Artificial neural networks (ANNs) Groundwater level (GWL) forecasting Climate variables |
| title | A systematic review of neural network applications for groundwater level prediction |
| title_full | A systematic review of neural network applications for groundwater level prediction |
| title_fullStr | A systematic review of neural network applications for groundwater level prediction |
| title_full_unstemmed | A systematic review of neural network applications for groundwater level prediction |
| title_short | A systematic review of neural network applications for groundwater level prediction |
| title_sort | systematic review of neural network applications for groundwater level prediction |
| topic | Review Neural networks (NNs) Artificial neural networks (ANNs) Groundwater level (GWL) forecasting Climate variables |
| url | https://doi.org/10.1007/s42452-025-06817-5 |
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