Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R² of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

利用新方法准确预测干旱地区的地下水位 (GWL) 至关重要。在本研究中，使用了支持向量回归（SVR）、高斯过程回归（GPR）以及它们与小波变换的结合（称为小波-支持向量回归（W-SVR）和小波-高斯过程回归（W-GPR））预测下个月塞姆南平原（干旱区）的地下水位。测试了三种不同的小波变换，即 Haar、db4 和 Symlet。四个统计指标，即均方根误差（RMSE）、平均绝对百分比误差（MAPE）、决定系数（R ²) 和 Nah-Sutcliffe 效率 (NS) 用于评估不同方法的性能。结果表明，RMSE 为 0.04790 (m)、MAPE 为 0.00199%、R ²为 0.99995 和 NS 为 0.99988 的 SVR 显着优于 GPR，RMSE 为 0.55439 (m)，MAPE 为 0.04363%，R2 为 6.92 0.98413。此外，混合W-GPR-1模型（即GPR与Harr小波）相比GPR显着提高了GWL预测的准确性。最后，混合 W-SVR-3 模型（即 SVR with Symlet）提供了最好的 GWL 预测，RMSE、MAPE、R2 和 NS 分别为 0.01290 (m)、0.00079%、0.99999 和 0.99999。总体而言，研究结果表明，混合模型可以准确预测干旱地区的 GWL。