Publication: Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia
Date
2021
Authors
Ibrahem Ahmed Osman A.
Najah Ahmed A.
Chow M.F.
Feng Huang Y.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Ain Shams University
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 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
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