Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion
Due to the impact of climate change, the groundwater level (GWL) has been declining recently in Malaysia, which is essential to protect the groundwater aquifer against depletion. Therefore, the current study aimed to propose an accurate GWL prediction model using advanced machine learning (ML) algor...
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my.uniten.dspace-365862025-03-03T15:43:14Z Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion Ahmed Osman A.I. Latif S.D. Wee Boo K.B. Ahmed A.N. Huang Y.F. El-Shafie A. 57221644207 57216081524 58939630300 57214837520 55807263900 16068189400 Malaysia Aquifers Climate change Climate models Forecasting Groundwater resources Hydrogeology Learning algorithms Learning systems Location Long short-term memory Mean square error Rain Support vector machines 'current Ground water level Groundwater aquifer Machine learning algorithms Machine-learning Malaysia Prediction modelling Root mean squared errors Support vector regressions Waters resources aquifer climate change groundwater resource machine learning prediction regression analysis Wetlands Due to the impact of climate change, the groundwater level (GWL) has been declining recently in Malaysia, which is essential to protect the groundwater aquifer against depletion. Therefore, the current study aimed to propose an accurate GWL prediction model using advanced machine learning (ML) algorithms in five populated towns, namely Jenderam, Bangi, Beranang, Kajang, and Paya Indah Wetland which are in Selangor, Malaysia. The models developed, used 11 months of previously recorded daily meteorological data of rainfall, temperature, evaporation, and GWL data from one selected well for each town, to predict 1-day, 3-day, and 5-day horizons GWL. For all five locations, four ML algorithms have been trained, tested, and then evaluated: long short-term memory (LSTM), extreme gradient boost (XGBoost), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Further, the best model among the four proposed models is used to predict daily GWL from January 2030 to December 2039 using projected rainfall and temperature data extracted from the Intercomparison Project Phase 5 (CMIP5) climate model. Applying the same 3 different input combinations for the models, the results showed that all the locations, including the GWL time-series data, improved the prediction accuracy significantly in all four models. Using testing data for 1-day ahead GWL prediction at Paya Indah Wetland as the best-performing location, XGBoost achieved the highest prediction performance with root mean squared error (RMSE) of 0.026 followed by LSTM, ANN, and SVR with RMSE of 0.027, 0.050, and 0.085 respectively. Ultimately, the results obtained in this study serve as a great benchmark for future GWL prediction using LSTM and XGBoost algorithm and give an insight into the influence of climate change on future GWL. Further, the findings can help local water resource managers draft resealable accuracy water resource plans in the state of Selangor for the next decade. ? 2024 Elsevier B.V. Final 2025-03-03T07:43:14Z 2025-03-03T07:43:14Z 2024 Article 10.1016/j.gsd.2024.101152 2-s2.0-85187784558 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187784558&doi=10.1016%2fj.gsd.2024.101152&partnerID=40&md5=6c1d0a9affa61e24479852e4319719b6 https://irepository.uniten.edu.my/handle/123456789/36586 25 101152 Elsevier B.V. Scopus |
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Malaysia Aquifers Climate change Climate models Forecasting Groundwater resources Hydrogeology Learning algorithms Learning systems Location Long short-term memory Mean square error Rain Support vector machines 'current Ground water level Groundwater aquifer Machine learning algorithms Machine-learning Malaysia Prediction modelling Root mean squared errors Support vector regressions Waters resources aquifer climate change groundwater resource machine learning prediction regression analysis Wetlands |
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Malaysia Aquifers Climate change Climate models Forecasting Groundwater resources Hydrogeology Learning algorithms Learning systems Location Long short-term memory Mean square error Rain Support vector machines 'current Ground water level Groundwater aquifer Machine learning algorithms Machine-learning Malaysia Prediction modelling Root mean squared errors Support vector regressions Waters resources aquifer climate change groundwater resource machine learning prediction regression analysis Wetlands Ahmed Osman A.I. Latif S.D. Wee Boo K.B. Ahmed A.N. Huang Y.F. El-Shafie A. Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
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Due to the impact of climate change, the groundwater level (GWL) has been declining recently in Malaysia, which is essential to protect the groundwater aquifer against depletion. Therefore, the current study aimed to propose an accurate GWL prediction model using advanced machine learning (ML) algorithms in five populated towns, namely Jenderam, Bangi, Beranang, Kajang, and Paya Indah Wetland which are in Selangor, Malaysia. The models developed, used 11 months of previously recorded daily meteorological data of rainfall, temperature, evaporation, and GWL data from one selected well for each town, to predict 1-day, 3-day, and 5-day horizons GWL. For all five locations, four ML algorithms have been trained, tested, and then evaluated: long short-term memory (LSTM), extreme gradient boost (XGBoost), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Further, the best model among the four proposed models is used to predict daily GWL from January 2030 to December 2039 using projected rainfall and temperature data extracted from the Intercomparison Project Phase 5 (CMIP5) climate model. Applying the same 3 different input combinations for the models, the results showed that all the locations, including the GWL time-series data, improved the prediction accuracy significantly in all four models. Using testing data for 1-day ahead GWL prediction at Paya Indah Wetland as the best-performing location, XGBoost achieved the highest prediction performance with root mean squared error (RMSE) of 0.026 followed by LSTM, ANN, and SVR with RMSE of 0.027, 0.050, and 0.085 respectively. Ultimately, the results obtained in this study serve as a great benchmark for future GWL prediction using LSTM and XGBoost algorithm and give an insight into the influence of climate change on future GWL. Further, the findings can help local water resource managers draft resealable accuracy water resource plans in the state of Selangor for the next decade. ? 2024 Elsevier B.V. |
| author2 |
57221644207 |
| author_facet |
57221644207 Ahmed Osman A.I. Latif S.D. Wee Boo K.B. Ahmed A.N. Huang Y.F. El-Shafie A. |
| format |
Article |
| author |
Ahmed Osman A.I. Latif S.D. Wee Boo K.B. Ahmed A.N. Huang Y.F. El-Shafie A. |
| author_sort |
Ahmed Osman A.I. |
| title |
Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| title_short |
Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| title_full |
Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| title_fullStr |
Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| title_full_unstemmed |
Advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| title_sort |
advanced machine learning algorithm to predict the implication of climate change on groundwater level for protecting aquifer from depletion |
| publisher |
Elsevier B.V. |
| publishDate |
2025 |
| _version_ |
1825816237959020544 |
| score |
13.244109 |