Groundwater level fluctuation is a nonlinear and non-stationary system as it depends on several factors in the time and space scales. Conceptual models require several physical parameters whose estimation is delicate in poorly monitored areas. However, data-based models may be valuable for modelling and forecasting groundwater level over short and long terms. To that end, four machine learning models, namely: Support Vector Regression, k- Nearest Neighbour (k-NN), Random Forest (RF), and Artificial Neural Network (ANN), are trained, validated, and compared for predicting groundwater level (GWL) at seven piezometers on alluvial groundwater of Tanobart aquifer in Morocco. The results revealed that the ANN models succeeded properly in simulating GWL at five piezometers out of the total seven piezometers considered in this study (NSE = 0.69 to 0.8); the RF was satisfactory at five piezometers (NSE = 0.41 to 0.72) and SVR at three piezometers (NSE = 0.57 to 0.81); the k-NN was the poorest model among all the investigated models (NSE = −1.05 to −0.15). The uncertainty analysis showed that the selected models are accurate overall; the SVR model showed the best forecasting accuracy with the smallest 95% interval prediction error (−0.25 m and 0.11 m) at one piezometer. This study provides new insight to forecast the GWL under a semi-arid context such Tanobart aquifer in Khemesset province, Morocco.

地下水位波动是一个非线性且不稳定的系统，因为它取决于时间和空间尺度上的几个因素。概念模型需要几个物理参数，这些参数在监测较差的区域中很难精确估计。但是，基于数据的模型对于短期和长期地对地下水位进行建模和预测可能是有价值的。为此，对四种机器学习模型进行了训练，验证和比较，以支持预测地下水位：支持向量回归，k最近邻（k-NN），随机森林（RF）和人工神经网络（ANN）。 （GWL）在7点Tanobart含水层冲积地下水的测压仪在摩洛哥。结果表明，在本研究考虑的全部七个压力计中，ANN模型成功地在五个压力计上模拟了GWL（NSE = 0.69至0.8）；射频在五个压强计（NSE = 0.41至0.72）下令人满意，而SVR在三个压强计（NSE = 0.57至0.81）上令人满意；在所有研究的模型中，k-NN是最差的模型（NSE = -1.05至-0.15）。不确定性分析表明所选模型总体上是准确的。SVR模型在一个压力计上显示了最佳的预测精度，最小的95％区间预测误差（-0.25 m和0.11 m）。这项研究为在摩洛哥Khemesset省的Tanobart含水层等半干旱环境下预测GWL提供了新的见识。