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fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

Journal of Environmental Sciences 2021年第12期33卷 150-159页

作者： Kaifeng Rao Li Tang Xin Zhang Heyu Xiang Liang Tang Yong Liu Wei Wang Jie Jiang Mei Ma Yiping Xu Zijian Wang State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Key Laboratory of Drinking Water Science and Technology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Shenzhen Monitoring Centre of Ecology and Environment Shenzhen 518049China Department of Basic Courses Beijing Union UniversityBeijing 100101China Faculty of Mathematics and Statistics Hubei UniversityWuhan 430062China CASA(Wuxi)Environmental Technology Co.Ltd. Wuxi 214024China College of Resources and Environment University of Chinese Academy of SciencesBeijing 101407China

Environmental impact of pollutants can be analyzed effectively by acquiring fish behavioral signals in water with biological behavior sensors. However, a variety of factors, such as the complexity of biological organisms themselves, the device error and the environmental noise, may compromise the accuracy and timeliness of model predictions. The current methods lack prior knowledge about the fish behavioral signals corresponding to characteristic pollutants, and in the event of a pollutant invasion, the fish behavioral signals are poorly discriminated. Therefore, we propose a novel method based on Bayesian sequential,which utilizes multi-channel prior knowledge to calculate the outlier sequence based on wavelet feature followed by calculating the anomaly probability of observed values. Furthermore, the relationship between the anomaly probability and toxicity is analyzed in order to achieve forewarning effectively. At last, our algorithm for fish toxicity detection is verified by integrating the data on laboratory acceptance of characteristic pollutants. The results show that only one false positive occurred in the six experiments, the present algorithm is effective in suppressing false positives and negatives, which increases the reliability of toxicity detections, and thereby has certain applicability and universality in engineering applications.

关键词： Bayesian sequential method fish electrical signal Outlier detection Anomaly probability Time series forecasting

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