Accurate modeling of groundwater level (GWL) is a critical and challenging issue in water resources management. The GWL fluctuations rely on many nonlinear hydrological variables and uncertain factors. Therefore, it is important to use an approach that can reduce the parameters involved in the modeling process and minimize the associated errors. This study presents a novel approach for time series structural analysis, multi-step preprocessing, and GWL modeling. In this study, we identified the time series deterministic and stochastic terms by employing a one-, two-, and three-step preprocessing techniques (a combination of trend analysis, standardization, spectral analysis, differencing, and normalization techniques). The application of this approach is tested on the GWL dataset of the Kermanshah plains located in the northwest region of Iran, using monthly observations of 60 piezometric stations from September 1991 to August 2017. By removing the dominant nonstationary factors of the GWL data, a linear model with one autoregressive and one seasonal moving average parameter, detrending, and consecutive non-seasonal and seasonal differencing were created. The quantitative assessment of this model indicates the high performance in GWL forecasting with the coefficient of determination (R²) 0.94, scatter index (SI) 0.0004, mean absolute percentage error (MAPE) 0.0003, root mean squared relative error (RMSRE) 0.0004, and corrected Akaike's information criterion (AICc) 151. Moreover, the uncertainty and accuracy of the proposed linear-based method are compared with two conventional nonlinear methods, including multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The uncertainty of the proposed method in this study was ± 0.105 compared to ± 0.114 and ± 0.126 for the best results of the ANN and the ANFIS models, respectively.

地下水位 (GWL) 的准确建模是水资源管理中的一个关键且具有挑战性的问题。GWL 波动依赖于许多非线性水文变量和不确定因素。因此，重要的是使用一种可以减少建模过程中涉及的参数并最小化相关错误的方法。本研究提出了一种用于时间序列结构分析、多步预处理和 GWL 建模的新方法。在本研究中，我们通过采用一步、两步和三步预处理技术（趋势分析、标准化、光谱分析、差分和归一化技术的组合）来识别时间序列确定性和随机项。该方法的应用在位于伊朗西北部地区 Kermanshah 平原的 GWL 数据集上进行了测试，使用 1991 年 9 月至 2017 年 8 月 60 个测压站的月度观测值。 通过去除 GWL 数据的主要非平稳因素，创建了一个具有一个自回归和一个季节性移动平均参数、去趋势和连续非季节性和季节性差异的线性模型. 该模型的定量评估表明在 GWL 预测中具有较高的性能，决定系数为（R ² ) 0.94, 散射指数 (SI) 0.0004, 平均绝对百分比误差 (MAPE) 0.0003, 均方根相对误差 (RMSRE) 0.0004, 修正赤池信息准则 (AICc) 151。此外，所提出的不确定性和准确性基于线性的方法与两种传统的非线性方法进行了比较，包括多层感知器人工神经网络（MLP-ANN）和自适应神经模糊推理系统（ANFIS）。本研究中提出的方法的不确定性为 ± 0.105，而 ANN 和 ANFIS 模型的最佳结果分别为 ± 0.114 和 ± 0.126。