Vadiati M, Asghari-Moghaddam A, Nakhaei M, Adamowski J, Akbarzadeh A H
Department of Earth Sciences, University of Tabriz, Tabriz, Iran; Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, Quebec, H9X 3V9, Canada.
Department of Earth Sciences, University of Tabriz, Tabriz, Iran.
J Environ Manage. 2016 Dec 15;184(Pt 2):255-270. doi: 10.1016/j.jenvman.2016.09.082. Epub 2016 Oct 6.
Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes.
由于测量和分析中存在固有的不确定性，地下水质量评估是一项艰巨的任务。人工智能技术，特别是模糊推理系统，已被证明在评估不确定和复杂的水文地质系统中的地下水质量方面很有用。在本研究中，开发了一种基于Mamdani模糊逻辑的决策方法，以根据相关指标评估地下水质量。为了开发一套用于地下水质量评估的新的混合模糊指标，利用广泛接受的地下水质量指标：地下水质量指数（GQI）、水质指数（WQI）和地下水质量指数（GWQI）开发了一个Mamdani模糊推理模型。为了提出广义混合模糊指标，做出了重大努力，采用著名的地下水质量指数可接受范围作为模糊模型输出范围，而不是在输出参数的模糊化中采用专家知识。对所提出的方法进行了评估，以检验其评估2013 - 2014年期间从伊朗萨拉布平原地下水资源季节性采集的49个样本的饮用水质量的能力。基于专家知识以及世界卫生组织规定的标准和允许限值，将输入隶属函数定义为“理想”、“可接受”和“不可接受”。根据GQI（5类）、WQI（5类）和GWQI（3类）将输出数据分类为多个类别。鉴于模糊模型具有将不确定性降至最低的潜力，基于混合模糊的指标对地下水质量的评估比传统指标要准确得多。评估了所开发模型的准确性，性能指标的比较表明模糊地下水质量指数模型比模糊水质指数和模糊地下水质量指数模型都更准确。这表明本研究中开发的新的混合模糊指标在用于饮用水目的的地下水质量评估时是可靠且灵活的。
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