In Journal of environmental management
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 R2 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.
Mohammed Hadi, Tornyeviadzi Hoese Michel, Seidu Razak
Bayesian network, Distribution network, Long short-term memory (LSTM), Predictive modelling, Total bacteria, Water quality, Weather variables