## 6 Smart Water Methods (SWMs)  
Implementing information and communication technologies (ICT) in urban networks has given rise to the concept of a Smart City, in which infrastructure components are made more intelligent, interconnected, and effective [107]. After the successful implementation of the smart grid in the electrical sector [108], water utilities were motivated to adopt a similar approach in water distribution systems (WDSs). This led to the emergence of the concept of smart water networks. This concept is based on equipping pipes with sensors that monitor pipe performance and condition [109, 110]. The primary aim of a smart water network is to establish a comprehensive monitoring system that integrates sensor technology, data acquisition, data interpretation, and decision-making processes for the real-time management of pipeline assets [111]. The concept and architecture of a smart water network are demonstrated in Figure 8. \begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Methods** & **Limitations** & **Improvements for Future Research** \\ \hline Acoustic & Leak noise loggers & Interference between the leak signals and environmental noise [2]. & Improve sensitivity by focusing on advanced signal-processing algorithms. \\ \hline \multirow{4}{*}{Inline} & \multirow{2}{*}{Sahara} & Need for suitable access points in the pipeline [98]. & Enhance the system to cover pipelines with restricted access. \\ \cline{2-3}  & & \multirow{2}{*}{SmartBall} & Difficulty navigating complex pipeline [99]. & Improve adaptability for undigable pipelines. \\ \cline{2-3}  & & & \\ \cline{2-3}  & \multirow{2}{*}{Fiber optic} & Accuracy affected by pipe materials and complex geometries [100]. & Optimize fiber optic technology for a wider range of pipeline materials and configurations. \\ \hline \multirow{4}{*}{Software-based} & \multirow{2}{*}{Flow variation method} & Changes related to consumer behavior or water demand rather than leaks [101]. & Develop advanced algorithms to differentiate between various causes of flow changes. \\ \cline{2-3}  & & \multirow{2}{*}{Pressure point analysis} & Pressure fluctuations caused by water hammer rather than leaks [102]. & Integrate pressure analysis algorithms to account for transient pressure effects. \\ \cline{2-3}  & & \multirow{2}{*}{Water balance method} & Inaccuracies in meter data, apparent losses [103]. & Combine this method with other techniques to improve data accuracy and identify hidden sources of water loss. \\ \cline{2-3}  & & & & Complement the model with adaptive techniques that can integrate real-time data for effective calibration. \\ \hline \multirow{4}{*}{Software-based} & \multirow{2}{*}{Numerical methods} & \multirow{2}{*}{Calibration [104].} & Enhance SVM algorithms with unsupervised learning or online training methods. \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \cline{1-1} \cline{2-3}  & & & \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._A smart water network can be broken down into five distinct layers, each with its own set of responsibilities [113]:  
* Physical layer--includes all physical components of the WDS, such as pipes, valves, reservoirs, and pumps. * Sensing and control--involves measuring water parameters, such as flow, pressure, and quality, among other important characteristics. * Collection and communication--entails collecting and transmitting measured data automatically and continuously. * Data management and display--requires creating a database platform to organize collected data and presenting it through visualization tools, such as GIS, and setting up a customer information system. * Data fusion and analysis--involves using modeling software to study network responses, applying predictive analytics for event detection, leak detection and localization, decision support, etc. The entire network can be managed remotely and automatically through communication channels. [PERSON] et al.