Osman, Ahmedbahaaaldin Ibrahem Ahmed and Ahmed, Ali Najah and Chow, Ming Fai and Huang, Yuk Feng and El-Shafie, Ahmed (2021) Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12 (2). pp. 1545-1556. ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2020.11.011.
Full text not available from this repository.Abstract
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 R-2 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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
| Item Type: | Article |
|---|---|
| Funders: | Institute of Postgraduate Studies and Research (IPSR) of Universiti Tunku Abdul Rahman, Malaysia, Innovation & Research Management Center (iRMC) of Universiti Tenaga Nasional [RJO10517844/088] |
| Uncontrolled Keywords: | Groundwater level prediction; Machine learning algorithm; Artificial neural network; Support vector regression; Cross-correlation |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 13 Apr 2022 07:46 |
| Last Modified: | 13 Apr 2022 07:48 |
| URI: | http://eprints.um.edu.my/id/eprint/27933 |
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