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Impurity effect on clear water evaporation: toward modelling wastewater evaporation using ANN, ANFIS-SC and GEP techniques

机译:杂质对透明水蒸发的影响:使用ANN,ANFIS-SC和GEP技术对废水蒸发进行建模

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摘要

The growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic.
机译:淡水资源的日益短缺和对环境的认识不断提高,因此使用经处理的废水作为供水的替代资源。作为水资源流失的主要原因,准确估算废水蒸发量(WWE)对于适当的水资源管理是必要的。不幸的是,尽管WWE非常重要,但很少有研究专注于对WWE建模。这项研究调查了基因表达编程(GEP),自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)技术估算WWE作为变量的函数的能力,这些变量包括废水特性,清澈的水蒸发和气候因素。这项研究使用了在伊朗内沙博尔市政废水处理厂进行的实验得出的测量数据。结果表明,与使用F检验统计量的维分析模型相比,ANN模型在这三种方法中均表现出色,并且具有更高的准确性。

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