Abstract：
Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.
摘要：
分析用作饮用水和灌溉水源的地下水的变化对于监测含水层至关重要，规划水资源,能源生产，应对气候变化,和农业生产。因此,有必要对地下水位（GWL）波动进行建模以监测和预测地下水储量。基于人工智能的水资源管理模型由于在水文研究中获得了成功而变得普遍。本研究提出了一种结合人工神经网络(ANN)和人工蜂群优化(ABC)算法的混合模型,随着整体经验模式分解(EEMD)和局部均值分解(LMD)技术的发展，模拟埃尔祖鲁姆省的地下水位，蒂尔基耶.GWL估计结果采用均方误差(MSE)进行评估，决定系数(R2)，和残差平方和(RSS)，并在视觉上与小提琴，分散，和时间序列图。研究结果表明，EEMD-ABC-ANN混合模型在估计GWL方面优于其他模型，R2值范围为0.91至0.99，MSE值范围为0.004至0.07。还表明，可以使用先前的GWL数据进行有希望的GWL预测。
