Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Environ Sci Pollut Res Int. 2022 Apr;29(19):28414-28430. doi: 10.1007/s11356-021-17879-4. Epub 2022 Jan 6.
The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two hybrid neuro-fuzzy models are implemented for the prediction of groundwater level (GWL) fluctuations, as well as variations of Cl - and HCO3 - in the Karnachi well, Kermanshah, Iran in monthly intervals within a 13-year period from 2005 to 2018. In order to develop AI models, the adaptive neuro-fuzzy inference system (ANFIS), firefly algorithm (FA), and wavelet transform (WT) are used. In other words, two hybrid models including ANFIS-FA (adaptive neuro-fuzzy inference system-firefly algorithm) and WANFIS-FA (wavelet-adaptive neuro-fuzzy inference system-firefly algorithm) are utilized for the estimation of the quantitative and qualitative parameters. Firstly, influencing lags of the time-series of the qualitative and quantitative parameters are identified using the autocorrelation function. Then, four and eight separate models are developed for the approximation of GWLs and qualitative parameters (i.e. Cl - and HCO3 -), respectively. It is worth to mention that about 75% of observed values are assigned to train the hybrid AI models, while the rest (i.e. 25%) to test them. Sensitivity analysis results reveal that the WANFIS-FA models display more acceptable performance than the ANFIS-FA ones. Also, the estimations of MAE, NSC, and SI for the simulation of HCO3 - by the superior model of the WANFIS-FA are obtained to be 0.040, 0.988, and 0.022, respectively. In addition, the lags (t-1), (t-2), (t-3), and (t-4) are ascertained as the most effective time-series lags for the estimation of Cl - .
定量和定性地下水参数的估计是一项重要任务。在这方面，人工智能 (AI) 技术被广泛用作准确、可靠且具有成本效益的工具。在本文中，实施了两种混合神经模糊模型，用于预测伊朗克尔曼沙赫 Karnachi 井的地下水位 (GWL) 波动以及 Cl-和 HCO3-的变化，预测时间为 2005 年至 2018 年的 13 年内的每月间隔。为了开发 AI 模型，使用了自适应神经模糊推理系统 (ANFIS)、萤火虫算法 (FA) 和小波变换 (WT)。换句话说，使用两种混合模型，包括 ANFIS-FA（自适应神经模糊推理系统-萤火虫算法）和 WANFIS-FA（小波-自适应神经模糊推理系统-萤火虫算法），用于估计定量和定性参数。首先，使用自相关函数识别时间序列定性和定量参数的影响滞后。然后，分别为 GWL 和定性参数（即 Cl-和 HCO3-）的逼近开发了四个和八个单独的模型。值得一提的是，大约 75%的观测值用于训练混合 AI 模型，其余的（即 25%）用于测试。敏感性分析结果表明，WANFIS-FA 模型的性能优于 ANFIS-FA 模型。此外，通过 WANFIS-FA 的优势模型对 HCO3-的模拟，得出 MAE、NSC 和 SI 的估计值分别为 0.040、0.988 和 0.022。此外，确定滞后 (t-1)、(t-2)、(t-3) 和 (t-4) 为估计 Cl-的最有效时间序列滞后。
