Water and Environmental Engineering Group, Institute of Marine Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU) in Ålesund, Larsgårdsvegen 2, 6009, Ålesund, Norway.
Water and Environmental Engineering Group, Institute of Marine Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU) in Ålesund, Larsgårdsvegen 2, 6009, Ålesund, Norway.
J Environ Manage. 2021 Apr 15;284:111997. doi: 10.1016/j.jenvman.2021.111997. Epub 2021 Jan 30.
In this study, a framework for integrating weather variables and seasons into the modelling and prediction of the microbial quality in drinking water distribution networks is presented. Statistical analysis and Bayesian network (BN) modelling were used to evaluate relationships among water quality parameters in distribution pipes and their dependencies on weather parameters. Two robust predictive models for Total Bacteria in the network were built based on a deep learning approach (Long Short-Term Memory (LSTM)). The first model included water quality parameters alone as inputs while the second model included weather parameters. The seven-year dataset used in this study constituted water quality parameters measured at seven location in the water distribution network for the city of Ålesund in Norway, and weather data for the same period. Results of the initial statistical analysis and the BN models showed that, air temperature, the summer season, precipitation, as well as water quality parameters namely, residual chlorine, water temperature, alkalinity and electrical conductivity have strong relations with the counts of Total Bacteria in the distribution networks studied. It was found that the integration of the weather parameters in the Total Bacteria prediction models significantly improved the quality of the predictions. Compared to the LSTM 1, LSTM 2 achieved MAE and MSE values as high as to 6.8 and 4.9 times respectively when the model was tested on the seven locations. In addition, the R values were marginally higher in LSTM 2 (0.92-0.95) than in LSTM (0.81-0.86). The prediction results demonstrate the relevance of integrating weather parameters such as air temperature seasons in predicting bacteria levels in water distribution systems. This suggests that changes in the microbial quality of water in distribution systems and potentially drinking water sources could be reliably assessed by integrating online sensors of water quality and weather parameters with efficient models such as the LSTM. Applying this efficient modelling approach in the management of water supply systems could offer immense support in addressing current challenges in assessing the microbial quality of water and minimizing associated health risks.
本研究提出了一种将天气变量和季节整合到饮用水管网微生物质量建模和预测中的框架。统计分析和贝叶斯网络（BN）建模用于评估管网中水质参数之间的关系及其对天气参数的依赖性。基于深度学习方法（长短期记忆（LSTM）），为管网中的总细菌建立了两个稳健的预测模型。第一个模型仅包含水质参数作为输入，而第二个模型则包含天气参数。本研究使用的七年数据集由挪威奥勒松市管网中七个位置的水质参数以及同期的天气数据组成。初始统计分析和 BN 模型的结果表明，空气温度、夏季、降水以及水质参数，如余氯、水温、碱度和电导率与管网中总细菌的计数具有很强的关系。研究发现，在总细菌预测模型中整合天气参数可显著提高预测质量。与 LSTM1 相比，LSTM2 在对七个位置进行测试时，其 MAE 和 MSE 值分别高达 6.8 倍和 4.9 倍。此外，LSTM2 的 R 值（0.92-0.95）略高于 LSTM 的 R 值（0.81-0.86）。预测结果表明，在预测配水系统中细菌水平时，整合空气温度等季节等天气参数具有相关性。这表明，通过将水质和天气参数的在线传感器与 LSTM 等高效模型相结合，可可靠地评估配水系统中水质的微生物质量变化，并降低相关健康风险。在供水系统管理中应用这种高效的建模方法，可以为评估水质微生物质量和最小化相关健康风险提供巨大支持。
