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.

马来西亚的地下水位最近一直在下降。这就是为什么，目前的研究旨在使用机器学习算法在马来西亚雪兰莪州人口稠密的城镇提出准确的地下水位预测模型。开发的模型使用了 11 个月之前记录的降雨、温度和蒸发数据来预测地下水位。三种机器学习模型已经过测试和评估；Xgboost、人工神经网络和支持向量回归。结果表明，对于仅将降雨数据延迟 1,2 天和 3 天作为输入的第一种情况，模型的性能最差。而在第二种情况下，所提出的 Xgboost 模型在所有不同的输入组合下都优于人工神经网络和支持向量回归模型。当使用延迟 1 天的地下水位作为输入时，在第三种情况下实现了性能的显着提高，其中 R在场景 3 中的 Xgboost 模型中²等于 0.92，在场景 2 和 1 中分别等于 0.16、0.11。本研究中获得的结果可作为使用 Xgboost 算法预测未来地下水位的重要基准。