# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 5 Potential Applications of Water 4.0
### Pipeline Health Monitoring  
A major component of a water distribution network are pipelines for potable water delivery to end users that have been placed underground for several years. Due to ageing, or third-party intervention, damages occur to these structures which causes waste of a significant amount of water. Before that, a fracture may occur on the pipe surface due to vibrations of the surrounding environment or corrosion. Several causes of pipeline damages may be found in the literature [138; 139]. Due to the significance of these damages,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Digital Technology** & **Features** & **Applications** & **Application Rate** & **Success Rate** \\ \hline \multirow{4}{*}{**CPS**} & & Water quality detection & & \\  & & (WQD) [103; 104; 105], leak detection [106], pressure control and monitoring (PCM) [107], state & & \\  & Sensing, communication and control (through actuators), decision system & (SEM) [108; 109], demand prediction and monitoring (DPM) [110], pipe health monitoring (PHM) [111], water resource management (WRM) [112] & & \\ \hline \multirow{4}{*}{**IoT**} & Smart sensing, data processing and communication & Leak detection [113; 114], WQD [115], PHM monitoring [116], PHM & & Success rate depends on the sensing and communication devices used. \\  & & & (NQD) [116; 117], pressure control (PMM) [119] & & \\ \hline \multirow{4}{*}{**BD Analytics**} & Smart sensing, data analysis and decision making & Leak detection [120], WRM [121; 122] & Less employed & \begin{tabular}{c} Success rate depends on the quality of the data and complexity of the analytical algorithms. \\ \end{tabular} \\  & & & (NQD) [122; 122], PPM [122; 122], process automation for water treatment and desalination [129; 130] & Less employed: Current application focus & \
\begin{tabular}{c} Success rate depends on the quality of the data and machine learning algorithm used. \\ \end{tabular} \\ \hline \multirow{2}{*}{**Cloud Computing**} & Data processing, storage and decision support systems. & WRM [131; 132], WQD [133; 134]. & Seldom used &
\begin{tabular}{c} Success rate depends on the quality of the data and complexity of the analytical tools. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the key drivers of Water 4.0.  
the monitoring of the health status of pipes before deformations occur is crucial. This could help in reducing losses if active intervention takes place once a particular section of the pipes in the network is found close to deformation. Pipeline health monitoring involves monitoring of pipes for corrosion, deformation, and vibration, as well as leaks. In the past, several methodologies were used to monitor pipelines which range from the deployment of wireless sensor networks [140] to the use of accelerometers and soft capacitive sensors [141]. When deformation is detected, active control mechanisms and decision support systems are required to relay the problems in real-time. In recent years, with technology improvement, Water 4.0 could provide capabilities to report areas of deformation as well as active control mechanisms and decision support systems to help utilities improve on the previous system operations. A typical example is the use of smart robotics for pipe health monitoring [142]. Furthermore, edge AI has the potential to create automated closed-loop systems that continuously monitor the health of critical infrastructure. Thus, the sustainable operation of the water supply system is guaranteed.