Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created.

准确预测水位将有助于防止过度开采地下水并有助于控制水资源。另一方面，水位预测是一个高度动态和非线性的过程，依赖于复杂的因素。因此，开发模型来预测水位以优化水库的水资源管理至关重要。因此，这项工作推荐了各种有监督的机器学习算法，用于预测与地下水位相关的水位。预测模型有线性回归 (LR)、支持向量机 (SVM)、高斯过程回归 (GPR) 和神经网络 (NN)。本研究包括四种情景；第一个场景 (SC1) 使用滞后 1；第二种情况 (SC2) 使用滞后 1 和滞后 2；第三个场景 (SC3) 使用滞后 1、滞后 2 和滞后 11，而第四个场景 (SC4) 使用滞后 1、滞后 2，滞后 11 和滞后 12。这些情景是使用自相关函数 (ACF) 确定的，这些滞后代表月份。结果表明，对于 SC1、SC2 和 SC4，GPR 中的所有模型性能都得到了良好的结果，其中使用 Matern 5/2 GPR 模型的最高 R 等于 SC1 中的 0.71、SC2 中的 0.78 和 SC4 中的 0.73。对于 SC3，Stepwise LR 模型给出了更好的结果，R 为 0.79。可以得出结论，高斯过程回归模型的 Matern 5/2 是预测水位的可靠模型，因为该方法在每个场景（SC3 除外）中都具有较高的性能，并且训练时间相对较快。与其他三个模型相比，NN 模型的性能最差，因为它在几乎所有四种输入组合场景中都具有最高的 MAE 值、RMSE 和最低的 R 值。