Monitoring point optimization in lake waters

Abstract

In order to grasp the distribution of water quality index in lake water, taking Jinghu Lake of Guangxi University as the experimental object, an radial basis function (RBF) neural network was combined with a genetic algorithm on the basis of an unmanned ship to study the optimal selection of monitoring points. The single-objective and multi-objective optimization of water quality parameters were tested respectively and used to make the fitting distribution map. The results show that the genetic neural network has obvious advantages over the traditional isometric monitoring in the distribution error of water quality parameters, and the data reflected by the results are still accurate and effective at least six weeks after optimization. The results show that a genetic neural network can significantly improve the efficiency of water quality monitoring.