Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia
Forecasting; Groundwater; Learning algorithms; Machine learning; Neural networks; Rain; Support vector regression; Gradient boosting; Machine learning models; Malaysia; Prediction model; Rainfall data; Support vector regression models; Predictive analytics
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Ain Shams University
2023
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my.uniten.dspace-261862023-05-29T17:07:32Z Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia Ibrahem Ahmed Osman A. Najah Ahmed A. Chow M.F. Feng Huang Y. El-Shafie A. 57221644207 57214837520 57214146115 55807263900 16068189400 Forecasting; Groundwater; Learning algorithms; Machine learning; Neural networks; Rain; Support vector regression; Gradient boosting; Machine learning models; Malaysia; Prediction model; Rainfall data; Support vector regression models; Predictive analytics Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models� performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm. � 2020 Faculty of Engineering, Ain Shams University Final 2023-05-29T09:07:32Z 2023-05-29T09:07:32Z 2021 Article 10.1016/j.asej.2020.11.011 2-s2.0-85099701203 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099701203&doi=10.1016%2fj.asej.2020.11.011&partnerID=40&md5=825296cae0eba3d475d8a90b51c07a46 https://irepository.uniten.edu.my/handle/123456789/26186 12 2 1545 1556 All Open Access, Gold Ain Shams University Scopus |
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Malaysia |
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| description |
Forecasting; Groundwater; Learning algorithms; Machine learning; Neural networks; Rain; Support vector regression; Gradient boosting; Machine learning models; Malaysia; Prediction model; Rainfall data; Support vector regression models; Predictive analytics |
| author2 |
57221644207 |
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57221644207 Ibrahem Ahmed Osman A. Najah Ahmed A. Chow M.F. Feng Huang Y. El-Shafie A. |
| format |
Article |
| author |
Ibrahem Ahmed Osman A. Najah Ahmed A. Chow M.F. Feng Huang Y. El-Shafie A. |
| spellingShingle |
Ibrahem Ahmed Osman A. Najah Ahmed A. Chow M.F. Feng Huang Y. El-Shafie A. Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| author_sort |
Ibrahem Ahmed Osman A. |
| title |
Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| title_short |
Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| title_full |
Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| title_fullStr |
Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| title_full_unstemmed |
Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia |
| title_sort |
extreme gradient boosting (xgboost) model to predict the groundwater levels in selangor malaysia |
| publisher |
Ain Shams University |
| publishDate |
2023 |
| _version_ |
1806428008372764672 |
| score |
13.222552 |