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首页> 外文期刊>International journal of nuclear desalination >Application Of An Artificial Neural Network In Wastewater Quality Monitoring: Prediction Of Water Quality Index
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Application Of An Artificial Neural Network In Wastewater Quality Monitoring: Prediction Of Water Quality Index

机译：人工神经网络在废水质量监测中的应用：水质指数的预测

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摘要

Water bodies have become more and more polluted owing to discharge of industrial waste. Therefore, it has been the chief concern of scientists, engineers and ecologists to decrease the water pollution level around the globe to maintain living viability and ecological balance. In this paper, the seasonal and positional variation of wastewater parameters in a natural flowing stream has been observed and an Artificial Neural Network (ANN) model is proposed to predict the water quality. Tolly's Canal was chosen as the purview of this case study. Wastewater and sediment samples were collected from Tolly's Canal and the River Ganges at different points and different seasons both at high and low tide conditions on a particular day. All the important water quality parameters were evaluated. To summarise and report river-water quality, a new term, 'Water Quality Index' (WQI), has been introduced. The WQI value is a dimensionless number ranging from 0 to 100 (best quality). In this study, the WQI is predicted by a simulative model using an ANN. This model has been developed for the assessment of the WQI and compared with the conventionally determined values of WQI. A Multilayer-Perceptron (MLP) network with a single hidden layer was used along with back-propagation algorithm. The results were found to be quite impressive. Thus, the ANN proved to be an efficient tool to assess the WQI of any sample.
机译：由于排放工业废物，水体变得越来越污染。因此，降低全球的水污染水平以维持生存能力和生态平衡一直是科学家，工程师和生态学家的主要关切。本文观察了自然流中废水参数的季节和位置变化，并提出了人工神经网络（ANN）模型来预测水质。 Tolly的运河被选为该案例研究的对象。在特定的一天，在高潮和低潮条件下，从托利运河和恒河的不同点和不同季节收集废水和沉积物样本。对所有重要的水质参数进行了评估。为了总结和报告河流水质，引入了一个新术语“水质指数”（WQI）。 WQI值是一个从0到100（最佳质量）的无量纲数。在这项研究中，WQI通过使用ANN的模拟模型进行预测。已经开发出该模型用于评估WQI，并将其与WQI的常规确定值进行比较。具有单个隐藏层的多层感知器（MLP）网络与反向传播算法一起使用。结果被发现是非常令人印象深刻的。因此，ANN被证明是评估任何样品的WQI的有效工具。

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