A Water Quality Classification Method Based on the Fusion of Multiple Classifiers and Dempster-Shafer Theory
A water quality classification method based on the fusion of multiple classifiers and evidence theory was proposed to address the is-sues of uneven recognition,low accuracy and poor adaptability of single classifiers for different water quality categories.This method selected three classifiers of deep neural network classifier,improved support vector machine classifier and Bayesian classifier.The reliability function is built by the full probability formula and based on evidence theory,the reliability function was fused to obtain a multi classifier fusion mod-el.It selected 3,558 pieces of water quality data from March 1-22,2022 released by the National Surface Water Quality Automatic Station as the sample set and used DNN water quality classification model,PSO-SVM water quality classification model,Bayesian water quality classifi-cation model and multi-classifier fusion model to test the samples.The results show that the average accuracy,precision,recall and F1 values of the multi classifier fusion model for water quality classification are 94.2%,93.8%,94.2%and 94.0%respectively.Compared to the DNN water quality classification model,PSO-SVM water quality classification model and Bayesian water quality classification model,the accuracy of multi-classifier fusion model has been improved by 5.6%,9.8%and 13.6%respectively,the precision by 5.2%,10.0%and 10.9%re-spectively,the recall by 5.6%,9.8%and 13.6%respectively and the F1 values by 5.4%,10.2%and 12.3%respectively.The multi classi-fier fusion model has better accuracy and adaptability in water quality classification.
water quality classificationmultiple classifiersneural networkintegration of evidence theory
国家科技期刊平台
NSTL
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