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Water is the source of life and the link that binds all living beings on this planet. This paper takes the Minhang District of Shanghai as an example to analyze the residual chlorine and turbidity of the secondary water supply detected by the six monitoring points in the area. The influence factors of water quality are analyzed according to the test results, at the same time, the correlation between turbidity and residual chlorine is explored, and corresponding solutions are proposed to provide reference for solving these problems.

Secondary water supply, as the final component of the urban water supply system, is critical for ensuring the safety of urban water supply, and is directly related to customers’ water use. However, water stored in secondary water supply facilities will more likely to lose disinfectants (chlorine residual) which can lead to bacterial regrowth and water quality deterioration. Therefore, it is important to solve problems associated with secondary water supply and manage the system more effectively. In the context of the Internet of Things, the intelligent management model based on information technology has become the growing trend. This paper introduces the application of the secondary water supply intelligent management model in Shanghai.

A new water quality model calibration method using pfmGA (parallel fast messy genetic algorithm )was presented by adopting parallel mechanism of fmGA (fast messy genetic algorithm )in allusion to the disadvantage that traditional calibration method lies on experience, waste time and fmGA can be only adopted in small scale water distribution system. It deals with calibration of the model, gives general method for calibrating and analyses the results for the model. Optimal model parameters and simulation results were obtained by adjusting parameters in a reasonable scope and comparing results and field data automatically. The model could now be used as a tool to control the water quality of the network but it still need to have real-time precise information about all the network quality, and the development of these tools should be carried on.

This paper outlines the development of a Decision Support System for Water Supply Management (WSMDSS) in city A to emulate and enhance the ability of water planning experts in the decision making process of water distribution networks expansion with new water sources. The main objective of water supply system management is to provide the reliable supply under varied availability conditions. WSMDSS in city A includes two aspects, one is energy-saving operation for new and old drinking water treatment plants and the other is comparison analysis of system hydraulics for pre- and post-connection to new water sources which both are based on water distribution networks modeling.

In order to study how information and technology can be used to improve water management, smart grid technologies were introduced into the water industry. To achieve the goals of water distribution network optimization, and providing high quality of service to consumers, Advanced Metering Infrastructure (AMI) is proposed. A set of online monitor sensors including smart flow and pressure meters, water quality sensors, smart water consumption meters and leakage monitoring sensors were installed in the water distribution networks which was connected to the SCADA system. The online flow, pressure and water quality data is essential to the hydraulic and water quality model calibration which make the water system management more digital and reasonable.