Fish forewarning of comprehensive toxicity in water environment based on Bayesian sequential method
Autor: | Jiang Jie, Heyu Xiang, Li Tang, Mei Ma, Liu Yong, Wei Wang, Xin Zhang, Yiping Xu, Kaifeng Rao, Tang Liang, Zijian Wang |
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Rok vydání: | 2021 |
Předmět: |
Environmental Engineering
Computer science Bayesian probability Reproducibility of Results Water Bayes Theorem General Medicine computer.software_genre Wavelet Feature (computer vision) Outlier False positive paradox Water environment Animals Environmental Chemistry Anomaly detection Data mining Environmental noise computer Algorithms Probability General Environmental Science |
Zdroj: | Journal of Environmental Sciences. 110:150-159 |
ISSN: | 1001-0742 |
Popis: | 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. |
Databáze: | OpenAIRE |
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