As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine (OS‐ELM) in predicting daily values of turbidity in Brandywine Creek, Pennsylvania, is evaluated. For this purpose, in addition to the developed OS‐ELM, several data‐driven models, that is, multilayer perceptron neural network (MLPANN), the classification and regression tree (CART), the group method of data handling (GMDH) and the response surface method (RSM) have been applied. The general findings of the study confirm the superiority of the OS‐ELM model over the other applied models so that the OS‐ELM improved the averaged RMSE of the predicted values 9.1, 11.7, 20.5 and 29.3% over the MLPANN, GMDH, RSM and CART models, respectively.

中文翻译：

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在线顺序极限学习机在河流水质（浊度）预测中的应用：不同数据挖掘方法的比较研究

作为水质的一种度量，水的浊度可能是饮用水资源中水污染的来源。今后，具有一种可靠的工具来根据常见的水量/水质测量参数预测浊度值非常重要。在本文中，评估了在线顺序极限学习机（OS‐ELM）在预测宾夕法尼亚州布兰迪万溪的浊度每日值方面的性能。为此，除了已开发的OS‐ELM外，还有几种数据驱动的模型，即多层感知器神经网络（MLPANN），分类和回归树（CART），数据处理的分组方法（GMDH）和响应面法（RSM）已被应用。