Water is the source of human life and water pollution is becoming more and more serious with the development of cities. The supervision and treatment of water resources have become a big problem of urban development. Water quality monitoring is not timely, flood warning is not timely is directly related to the livelihood of the people. And the development of smart water utilities can solve problems timely and accurately. By placing water quality sensors in the urban water supply network, real-time monitoring of water quality can be performed to prevent incidents of drinking water pollution. After an incident of drinking water pollution occurs, reverse locating the pollution source through the information detected by the water quality sensors represents a challenging problem because in the actual water supply network, the direction and speed of the water flow will change with the water demand of the residents, thus leading to uncertainty in this problem. In conventional studies of pollution source location problems, it is often assumed that the water demand is fixed. However, due to the variability of the water demand of residents, this problem is actually a dynamic change problem and thus can be considered as a dynamic optimization problem. In this study, a Poisson distribution model was used to simulate the change of water demand among urban residents. On this basis, we proposed an improved genetic algorithm to solve the pollution source location problem and implemented two different water supply networks to perform the simulation experiments, which could accurately locate the pollution sources. The simulation results were compared with the standard genetic algorithm to verify the accuracy and robustness of the proposed algorithm.

水是人类生命的源泉，随着城市的发展，水的污染越来越严重。水资源的监督管理已成为城市发展的大问题。水质监测不及时，洪水预警不及时直接关系到民生。智能水务公司的发展可以及时准确地解决问题。通过将水质传感器放置在城市供水网络中，可以对水质进行实时监控，以防止发生饮用水污染事件。在发生饮用水污染事件之后，通过水质传感器检测到的信息对污染源进行反向定位是一个具有挑战性的问题，因为在实际的供水网络中，水流的方向和速度将随着居民的用水需求而变化，从而导致该问题的不确定性。在对污染源位置问题的常规研究中，通常假设需水量是固定的。但是，由于居民用水需求的变化，该问题实际上是一个动态变化问题，因此可以视为动态优化问题。在这项研究中，使用泊松分布模型来模拟城市居民之间的需水量变化。在此基础上，提出了一种改进的遗传算法来解决污染源的定位问题，并实现了两个不同的供水网络进行仿真实验，可以准确地定位污染源。