Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Department of Civil Eng., Faculty of Eng., Urmia University, PO Box 165, Urmia, Iran.
Environ Monit Assess. 2019 Sep 6;191(10):620. doi: 10.1007/s10661-019-7784-6.
Groundwater aquifers have always been confronted with significant challenges around the world such as climate change, over-extraction, pollution by wastewaters, and saltwater intrusion in coastal areas. Prediction of groundwater level under the effects of climate change is more important in water resource management. This study has therefore been evaluated the effects of two climate parameters (i.e., precipitation and temperature) in groundwater level for the Shabestar Plain, Iran. For this end, four models from General Circulation Models (GCM) were then used to evaluate future climate change scenarios of the Representative Concentration Pathway (i.e., RCP2.6, RCP4.5, RCP8.5). In the next phase, to reduce the spatial complexity of observation wells, clustering analysis was used. In case of groundwater level modeling, time series in the base period, Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Network with Exogenous inputs (NARX) were also used. To improve the prediction accuracy, time series preprocessing made by wavelet-based de-noising approach was used. Analysis of the results illustrates an increase in temperature and a decrease in precipitation for study region in the future period times. The results also reveal that hybrid techniques of the wavelet-NARX give best results in comparison with the other models. A simulation result illustrates that the groundwater level declines in RCP2.6, 4.5, and 8.5, which gives average levels of 0.61, 0.81, and 1.53 m, respectively, for the future period years (i.e., 2020-2024). These results would lead to continuous groundwater depletion. These findings emphasize the necessity of the importance of extraction policies in water resource management.
地下水含水层在全球范围内一直面临着重大挑战，如气候变化、过度开采、废水污染以及沿海地区的海水入侵。在水资源管理中，预测气候变化对地下水位的影响更为重要。因此，本研究评估了伊朗沙巴斯坦平原两个气候参数（即降水和温度）对地下水位的影响。为此，使用了来自通用环流模型（GCM）的四个模型来评估代表性浓度途径（即 RCP2.6、RCP4.5、RCP8.5）的未来气候变化情景。在下一阶段，为了降低观测井的空间复杂性，使用了聚类分析。在地下水位建模方面，还使用了基期时间序列、最小二乘支持向量机（LSSVM）、自适应神经模糊推理系统（ANFIS）和具有外部输入的非线性自回归网络（NARX）。为了提高预测精度，使用了基于小波的去噪方法进行时间序列预处理。结果分析表明，研究区域未来时期的温度升高，降水减少。结果还表明，与其他模型相比，基于小波的 NARX 混合技术具有更好的结果。模拟结果表明，在 RCP2.6、4.5 和 8.5 情景下，地下水位下降，未来几年（即 2020-2024 年）的平均水位分别为 0.61、0.81 和 1.53 米。这些结果将导致地下水不断枯竭。这些发现强调了在水资源管理中制定提取政策的重要性。
