# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 4 Digital Technologies in the Water System: Key Drivers of Water 4.0
### Artificial Intelligence (AI)  
In recent years, machine learning, which is a subset of AI, has gained momentum in the water sector. Artificial intelligence involves the simulation by machines, particularly computers, of human intelligence processes such as learning, reasoning, and self-correction. It is an essential technology which, with the help of the computer, is programmed and controlled by machines [29]. Some of the potentials of AI in the water sector are shown in Figure 8.  
The optimization of current water resources can be achieved with the decision-making abilities of AI, where decision support systems [78; 79; 80; 81] are used. Another key potential of AI in the water sector is infrastructure planning. Water utilities can employ AI-driven planning to gain a better understanding of real-time water loss and usage, allowing them to build and implement a comprehensive and adaptive distribution network, as well as provide revenue for financial activities. Several studies have reported the use of AI for demand forecasts [82; 83; 84; 85], which set pace for future planning and expansion of infrastructure. In water supply systems, a human decision such as water quality awareness, shutting down pipes whose water quality has been compromised, etc., could be made by AI. In some cases, AI could be used to analyze complex water network data. For example, the data from a ground penetration radar used for leak localization can be analyzed. Machine learning approaches are the first step in AI-based frameworks. Several machine learning methodologies have been deployed to the water industry [86; 87; 88]. One such application is the estimation of the likelihood of event occurrence and the raising of corresponding alarms using Bayesian networks [87]. Elsewhere [86], advanced AI, machine learning, and statistical methods are used to established risk of pipe burst. One of the promising applications of AI in water supply networks is the provision of on-line monitoring through the development of robotic systems for in-pipe monitoring [89]. This system monitors the pipe for fractures, cracks, and areas of leak occurrence. The use of AI for water quality prediction has been reported in the literature [90; 91; 92; 93]. In this application, the water quality index (WQI) and water quality classification (WQC) may be achieved as illustrated in Figure 9.  
Figure 8: Some of the potentials of AI in the water industry. In [PERSON] et al. [90], an AI algorithm was developed for the prediction and classification of water quality. For the WQI prediction, a nonlinear autoregressive neural network (NARNET) and a long short-term memory (LSTM) deep learning algorithm were used. For the classification, support vector machine (SVM), K-nearest neighbour (K-NN), and naive Bayes were utilized. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values, and the SVM algorithm achieved the accuracy of 97.01% for the WQC prediction. Furthermore, the NARNET and LSTM models achieved similar accuracy in the testing phase with a slight difference in the regression coefficient. Because the AI technique employs early warning indications that allow for the detection of extreme events on a water distribution system [94], combining it with cutting-edge control devices allows for quick intervention and a reduction in the risk of contamination. [PERSON] et al. [95] proposed an artificial intelligence framework for improving water resource management. The proposed system makes use of data from a multisensory array that includes flow sensors, pH sensors, water pressure valves and ultrasound sensors. The experimental results show that the intelligent system permits the analysis of water quality with a root mean square value of 15.12%, and reliability of 98.24%. [PERSON] et al. [96] proposed a clustering and semi-supervised learning AI framework for leak detection and localization in water distribution networks.