预测进水特性，在任何治疗之前，对于污水处理厂（WWTP）的运营和管理非常重要。在这项研究中,四种机器学习模型，包括多层感知器(MLP)，长短期记忆网络(LSTM)，K-最近邻(KNN),和随机森林(RF)，被引入以利用来自北美三个污水处理厂的实时废水数据(即，TresRios,伍德沃德,和一个机密工厂)，用于预测每小时进水特性。使用自相关分析和来自RF的变量重要性度量来选择输入变量。研究了单变量和多变量分析以提高模型准确性。比较了一步和多步模型的性能。预测范围很短，从单变量和多变量分析得出的所有模型都显示出出色的性能。结果发现，随着预测范围的扩大，性能恶化可以通过包括额外的变量来显著缓解，比如气象变量。这项工作可以为污水处理厂废水进水特性的高时间分辨率预测提供有价值的支持。拟议的模型还可以弥合废水部门数据和决策之间的差距。
Prediction of influent characteristics, before any treatment takes place, is of great importance to the operation and management of wastewater treatment plants (WWTPs). In this study, four machine-learning models, including multilayer perceptron (MLP), long short-term memory network (LSTM), K-nearest neighbour (KNN), and random forest (RF), are introduced to utilize real-time wastewater data from three WWTPs in North America (i.e., Tres Rios, Woodward, and one confidential plant) for predicting hourly influent characteristics. Input variables are selected using an autocorrelation analysis and a variable importance measure from RF. Both univariate and multivariate analyses are investigated to improve model accuracy. The performances of one- and multiple-step-ahead models are compared. With a short prediction horizon, all the models derived from both univariate and multivariate analyses show excellent performance. It was found that the performance deterioration as the prediction horizon expands could be mitigated significantly by including extra variables, such as meteorological variables. This work can provide valuable support for the high-temporal-resolution prediction of wastewater influent characteristics for WWTPs. The proposed models can also bridge the gap between data and decision-making in the wastewater sector.
