Risk assessment model for algal bloom of open channel based on dynamic Naïve Bayes classifier
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Abstract:
Algal bloom risk is not only an environmental issue to be considered in water conservancy project planning, but also a monitoring item that cannot be ignored in the operation of water conservancy facilities. In order to improve the prediction accuracy for algal bloom risk of open channels, a risk assessment model for algal bloom of open channels was proposed based on the dynamic Na?ve Bayes classifier, with consideration to the uncertainty of the cause of algal bloom and sequential nature of its development. The risk grade nodes of the proposed model correspond to the concentration of chlorophyll a (Chla), and take into consideration 9 factors affecting the growth of algae. Network parameters were designed according to the results of expert consultation using the principal component analysis method. Based on the 53 cases of consecutive monitoring data observed from June 2011 to September 2011 at Beimen Bridge on Suzhou River, comparison was made between the proposed model and the assessment model based on Na?ve Bayes classifier. Confusion matrix results showed that the prediction accuracy for medium risks increased by 15.625%. Single tailed paired t-test showed that the recognition rates of the two models were significantly different when the significance level was 0.05. The assessment model based on dynamic Na?ve Bayes classifier with consideration to time sequence has significantly higher prediction and recognition rates for medium algal bloom risk of open channels.
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