Pollution source intelligent location algorithm in water quality sensor networks

Abstract

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.