文献类型：期刊文献

英文题名：Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method

作者：Rao, Kaifeng[1,2];Tang, Li[3];Zhang, Xin[4];Xiang, Heyu[5];Tang, Liang[6];Liu, Yong[6];Wang, Wei[6];Jiang, Jie[6];Ma, Mei[2,7];Xu, Yiping[2];Wang, Zijian[1]

第一作者：Rao, Kaifeng

通讯作者：Ma, M[1];Zhang, X[2];Ma, M[3]

机构：[1]Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100085, Peoples R China;[2]Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Key Lab Drinking Water Sci & Technol, Beijing 100085, Peoples R China;[3]Shenzhen Monitoring Ctr Ecol & Environm, Shenzhen 518049, Peoples R China;[4]Beijing Union Univ, Dept Basic Courses, Beijing 100101, Peoples R China;[5]Hubei Univ, Fac Math & Stat, Wuhan 430062, Peoples R China;[6]CASA Wuxi Environm Technol Co Ltd, Wuxi 214024, Jiangsu, Peoples R China;[7]Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101407, Peoples R China

第一机构：Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100085, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Key Lab Drinking Water Sci & Technol, Beijing 100085, Peoples R China;[2]corresponding author), Beijing Union Univ, Dept Basic Courses, Beijing 100101, Peoples R China;[3]corresponding author), Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101407, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2021

卷号：110

起止页码：150-159

外文期刊名：JOURNAL OF ENVIRONMENTAL SCIENCES

收录：;EI(收录号：20212410509588);Scopus(收录号：2-s2.0-85107745556);WOS:【SCI-EXPANDED(收录号:WOS:000740434200017)】；

基金：This work was supported by the National Key R&D Program of China (No. 2019YFD0901100) and the Frontier Science Key Program of the Chinese Academy of Sciences (No. QYZDY-SSWDQC004).

语种：英文

外文关键词：Bayesian sequential method; Fish electrical signal; Outlier detection; Anomaly probability; Time series forecasting

摘要：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. (C) 2021 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.