Application of Improved Particle Swarm Optimization SVM in Water Quality Evaluation of Ming Cui Lake

Water quality directly determines our living environment. In order to establish a more scientific and reasonable water evaluation model, it needs lot of data support, but will lead large increase in the calculation time model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) solve this problem. paper, Pareto optimal solution concept is used sparsely process training set, which can ensure that number sets reduced without loss characteristics, thus reducing time. problem kernel parameter g penalty factor c on algorithm, affects accuracy difficult select why, algorithm optimize improve 480 from Ming Cui Lake 2014 2022 are taken as research object, examples analyzed MATLAB 2020a. The results show CPOS-SVM be completed within 2 s does not with volume. Meanwhile, by comparing POS-SVM POS-BP increases dramatically amount data. highest, basically consistent POS-SVM, reaching 94%, while slightly worse. indicates has good application value evaluation.