Simulation study on Multi-parameter Detection method of polluted Water quality based on Sponge City

Abstract

With the accelerated pace of urbanization, the construction of sponge cities is particularly essential for maintaining the ecological environment. Given the traditional multiparameter detection of heavily polluted water quality in cities, there are many problems, such as long completion time, low accuracy, high cost. A multi-parameter detection method of water quality based on the random forest classification algorithm is proposed. Through the analysis of urban heavily contaminated water quality, the probability synthesis matrix of heavily polluted water quality index is obtained by combining the water quality fluctuation probability of a single parameter and the dynamic correlation coefficient of multi-parameters. The membership function of the fluctuation of the treated water quality index is obtained, and the weak correlation among the water quality indexes is removed by the obtained membership function, and the strong relationship characteristic quantity of the water quality index is obtained, which is input into the random forest classifier to complete the multi-parameter detection of river water quality.