# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### NFC
#### 3.1.4 Computing and Intelligent Decision System  
In this stage of CPS, the data acquired are reasoned and further analyzed. Advanced computing methodologies are used to understand, address, and communicate the event that occurs in the physical environment. This allows optimal decision to be made concerning the state of the monitored environment depending on the application, such as safeguarding water sustainability in water supply system applications. The optimal decision making could be carried out through the use of optimization algorithms or decision support systems [62; 63; 64; 65]. A decision support system is an interactive software-based system to assist decision makers compile useful information from a combination of raw/processed data to identify and solve problems and to make optimized decisions. In water supply network management applications, the data are the processed or preprocessed sensor data acquired from the physical process. In some cases, data mining techniques have been utilized for data-driven decision making. For example, linear regression, neural networks, and clustering algorithms were deployed for water quality event detection applications [66]. Also, since water supply systems are strongly affected by seasonal variations, [PERSON] et al. [67] proposed probabilistic forecasting methodologies for sequential decision-making during water contamination events detection. Due to the dynamic nature of water supply systems and consumer demand uncertainties, elsewhere [68; 69; 70; 71; 72; 73], advance modelling approaches that cover the complex interaction between water resource, water supply systems, and end user's demand unexpected pattern could improve the decision-making process. One such modelling tool that could be utilized is the agent-based modelling (ABM). ABM is a computational tool that could be used to simulate the actions and mutual interactions of autonomous agents to appraise the cumulative effect on the system considered [69]. From the water supply system management perspective, the agents are the water resource, the water supply system, the water utilities, and the end consumers. An agent is modelled to receive information about their physical environment and select actions to change the environment. Research studies have acknowledged the use of ABM to improve decision-making when an undesirable event occurs in water supply networks. For instance, the ABM approach has been used to assess the effect of contamination on public health for a small town having 5000 residents [70; 71]. In [72], an ABM framework was presented for simulating a contamination event in a water distribution network considering social-technical parameters. In this work, the consumers were represented as individual agents. Thus, an agent-based model was integrated with a water distribution system model to capture the dynamics between consumer behavior and that of the water distribution system for predicting the movement of contaminant and public exposure. As the water demand by consumer changed, the demand exerted on the water distribution network was updated, the location of the contaminant within the network was updated, and the quantity of contaminant consumed by each agent was estimated. The framework has been tested through simulating realistic contamination scenarios for a water distribution system. More recently, [PERSON] et al. [73] developed \"Aqual.MORE\", which is an agent-based system for water demand and supply at a catchment scale. In this system, the agents are classified as water, managers, and users. The water, which is the physical environment to be monitored for contaminant level, represents the discrete quantities of the resource. The users are the consumers that use the resource, while the managers are the water utilities that regulate and manage resource flows to the users.