Modelling the impact of weather parameters on the microbial quality of water in distribution systems. (15th April 2021)

• Record Type: Journal Article
• Authors: Mohammed, Hadi; Tornyeviadzi, Hoese Michel; Seidu, Razak
• Is Part Of: Journal of environmental management. Volume 284(2021)
• Publication Date: 2021-04-15
• Subjects: Water quality -- Total bacteria -- Distribution network -- Weather variables -- Predictive modelling -- Bayesian network -- Long short-term memory (LSTM); Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.111997
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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 2 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. Highlights: The impact of weather variables on water quality in distribution pipes is evaluated. BNs are useful for evaluating the degrees of impact among water quality variables. Residual chlorine and water temperature have strong relations with bacteria in pipes. LSTM has high prospects for use in predicting the microbial quality of water in pipes. Weather variables improve the prediction accuracy of bacteria in distribution pipes.