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Water quality monitoring plays a crucial role in urban water supply systems for the production of safe drinking water. However, the traditional approach to water monitoring in Norway relies on a periodic (weekly/biweekly/monthly) sampling and analysis of biological indicators, which fails to provide a timely response to changes in water quality. This research addresses this issue by proposing a data-driven solution that enhances the timeliness of water quality monitoring. Our research team applied a case study in Ålesund Kommune. A sensor platform has been deployed at Lake Brusdalsvatnet, the water source reservoir in Ålesund. This sensor module is capable of collecting data for 10 different physico-chemical indicators of water quality. Leveraging this sensor platform, we developed a CNN-AutoEncoder-SOM solution to automatically monitor, process, and evaluate water quality evolution in the lake. There are three components in this solution. The first one focuses on anomaly detection. We employed a recurrence map to encode the temporal dynamics and sensor correlations, which were then fed into a convolutional neural network (CNN) for classification. It is noted that this network achieved an impressive accuracy of up to 99.6 % . Once an anomaly is detected, the data are calibrated in the second component using an AutoEncoder-based network. Since true values for calibration are unavailable, the results are evaluated through data analysis. With high-quality calibrated data in hand, we proceeded to cluster the data into different categories to establish water quality standards in the third component, where a self-organizing map (SOM) is applied. The results revealed that this solution demonstrated significant performance, with a silhouette score of 0.73, which illustrates a small in-cluster distance and large intra-cluster distance when the water was clustered into three levels. This system not only achieved the objective of developing a comprehensive solution for continuous water quality monitoring but also offers the potential for integration with other cyber–physical systems (CPSs) in urban water management. [ABSTRACT FROM AUTHOR]

• A climate-driven model for stream flow and E. coli discharge into an ungauged drinking water source is implemented. • Forest canopy and soil organic carbon content control runoff generation in temperate mountain catchments. • Instream sediment processes mostly control the occurrence of E. coli in streams. • Snowmelt induced peak stream flows in the temperate mountain catchments will occur earlier in winter in future. • Future shifts in stream flow will potentially lower concentrations of faecal indicator bacteria in surface water resources. The discharge of fecal indicator bacteria into surface water sources poses treatment challenges to drinking water utilities. Changes in the discharge regimes of streams resulting from extreme precipitation associated with climate change can exacerbate these challenges. The catchments of many surface water sources depended upon by water treatment plants are ungauged, thus making it difficult to predict the impacts of climate change on the microbial quality of such water sources. In this study, a hydrological model is implemented to assess the impact of local climate projections on flow regimes and E. coli discharge in the ungauged catchment of the drinking water source for the city of Ålesund in Norway. We performed separate sensitivity analysis for flow and E. coli, and assessed the variations in patterns of flow and E. coli concentrations in the streams in future using adjusted meteorological data. The calibrated flow showed good agreement with the observed data, but the E. coli model yielded lower performance indices. The results indicate that interception of precipitation by forest cover and soil organic carbon content mostly control flow generation in the catchments, and the variations in the concentrations of E. coli were more sensitive to instream sediment processes. Flow levels in the streams would remain the same in 2045 and 2075. Peak flows that currently occur in spring and autumn months may respectively shift back to winter and summer months. High concentrations of E. coli currently observed in summer in the streams may be lower in future, but the concentrations may be high at the start of autumn seasons. The results have management implications for watersheds and water utilities mostly in temperate climate regions. [ABSTRACT FROM AUTHOR]

Presently, concentrations of fecal indicator bacteria (FIB) in raw water sources are not known before water undergoes treatment, since analysis takes approximately 24 h to produce results. Using data on water quality and environmental variables, models can be used to predict real time concentrations of FIB in raw water. This study evaluates the potentials of zero-inflated regression models (ZI), Random Forest regression model (RF) and adaptive neuro-fuzzy inference system (ANFIS) to predict the concentration of FIB in the raw water source of a water treatment plant in Norway. The ZI, RF and ANFIS faecal indicator bacteria predictive models were built using physico-chemical (pH, temperature, electrical conductivity, turbidity, color, and alkalinity) and catchment precipitation data from 2009 to 2015. The study revealed that pH, temperature, turbidity, and electrical conductivity in the raw water were the most significant factors associated with the concentration of FIB in the raw water source. Compared to the other models, the ANFIS model was superior (Mean Square Error = 39.49, 0.35, 0.09, 0.23 CFU/100 ml respectively for coliform bacteria, E. coli , Intestinal enterococci and Clostridium perfringens ) in predicting the variations of FIB in the raw water during model testing. However, the model was not capable of predicting low counts of FIB during both training and testing stages of the models. The ZI and RF models were more consistent when applied to testing data, and they predicted FIB concentrations that characterized the observed FIB concentrations. While these models might need further improvement, results of this study indicate that ZI and RF regression models have high prospects as tools for the real-time prediction of FIB in raw water sources for proactive microbial risk management in water treatment plants. [ABSTRACT FROM AUTHOR]

[Display omitted] • A semi-supervised sequence to sequence BILSTM deep autoencoder is developed. • Robust data augmentation to enhance the generalisation power of the autoencoder. • Leakage identification through coupled reconstruction error & modified z-score. • BiLSTM deep AE is superior to MNF and ANF in identification of unreported leaks. • Identification of unreported leaks in all types of DMAs with limited false alarms. Night flow analysis is predominantly used to identify incipient (gradual) leakages in real-life Water Distribution Networks (WDNs). However, due to extreme stochastic demand especially in residentially dominated district metering areas (DMAs) and the emergence of sleepless cities, traditional night flow analysis methods have become inefficient. This study proposes a semi-supervised sequence-to-sequence Bidirectional Long Short-Term Memory (BiLSTM) deep Auto Encoder (AE) method for night flow analysis in WDNs. To enhance the generalisation power of the deep AE, a vigorous data augmentation procedure based engineering domain knowledge and inherent properties of DMA is presented. The proposed method learns the underlying benign patterns in night flow (2:00 am to 4:00 am) and identifies significant deviations that represent the development of incipient leakages. The method was validated on residential, commercial, and industrial DMAs in a real-life WDN in the Ålesund Municipality of Norway. The results from the study showed that the proposed method is a robust and superior alternative to traditional night flow under extreme stochastic consumer demand. The proposed BiLSTM deep AE method was able to identify unreported leakages in the DMAs within 1 day or at most 4 days compared with at least 15 days identification time by traditional minimum night flow (MNF) and average night flow (ANF) analyses. Additionally, traditional minimum night flow analysis also produced more false positives compared with the proposed method. The results also revealed the usage of point estimates to represent night flow as done in MNF and ANF analyses fail to capture all salient information regarding night flow compared to the BiLSTM deep AE. [ABSTRACT FROM AUTHOR]

• Machine-learning is applied to mimic a process-based hydrodynamic and water quality model. • Process-based models forced by highly variable inputs are well emulated by the LSTM. • Spatio-temporal variations of water quality in reservoirs can be efficiently predicted by the LSTM. • Compared to process-based models, shorter simulation time is required with the LSTM. • Real-time hydrodynamic and water quality simulations can be made easier with machine-learning. Process-based models are very efficient in simulating hydrodynamics and water quality in surface water bodies. However, their complex characteristics in terms of implementation, data requirements, and simulation time limit their application in regular drinking water source management. This study demonstrates the potential of a ML model (Long Short-Term Memory (LSTM)) as viable alternatives to process-based hydrodynamic and water quality models in water source management. Using meteorological and hydrological measurements, a hydrodynamic and water quality model was first calibrated to predict time series, profiles, and contours of water variables namely Escherichia coli (E. coli), faecal coliforms, zinc, and lead concentrations in the Brusdalsvatnet lake, which is the drinking water source for the city of Ålesund in Norway. The results obtained were combined with the input data to train a suite of LSTM models to emulate the results achieved with the process-based modelling. The results indicate that ML models can conveniently reproduce the spatio-temporal evolution of water quality in the lake that is achievable with the process-based model, particularly when specific locations within the lake are of interest. Compared to R2, NS and MSE ranges of 0.72–0.87, 0.68–0.85, and 0.21–0.44 achieved with the process-based model in the prediction of temperature in the lake, 0.78–0.95, 0.75–0.89, and 0.011–0.028 were respectively achieved in testing the LSTM models. Similar performance levels were achieved with the LSTM model in the prediction of Escherichia coli (E. coli), faecal coliforms, Zinc, and Lead concentrations at different depths in the lake. While setting up and training the LSTM model to emulate the process-based model simulations was very time-consuming, a validated model as developed in this study can offer an opportunity for real-time simulation of water quality in drinking water sources when integrated with cloud data transmission from field sensors. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]

Formulating effective management intervention measures for water supply systems requires investigation of potential long-term impacts. This study applies an integrated multiple regression, random forest regression, and quantitative microbial risk assessment (QMRA) modelling approach to assess the effect of climate-driven precipitation on pathogen infection risks in three drinking water treatment plants (WTPs) in Norway. Pathogen removal efficacies of treatment steps were calculated using process models. The results indicate that while the WTPs investigated generally meet the current water safety guidelines, risks of Norovirus and Cryptosporidium infection may be of concern in the future. The pathogen infections attributable to current projections of average precipitation in the study locations may be low. However, the pathogen increases in the drinking water sources due to the occurrence of extreme precipitation events in the catchments could substantially increase the risks of pathogen infections. In addition, without optimal operation of the UV disinfection steps in the WTPs, both the present and potential future infection risks could be high. Therefore, the QMRA models demonstrated the need for improved optimization of key treatment steps in the WTPs, as well as implementation of stringent regulations in protecting raw water sources in the country. The variety of models applied and the pathogen: E. coli used in the study introduce some uncertainties in the results, thus, management decisions that will be based on the results should consider these limitations. Nevertheless, the integration of predictive models with QMRA as applied in this study could be a useful method for climate impact assessment in the water supply industry., (Copyright © 2018. Published by Elsevier B.V.)