Machine learning for anomaly detection in cyanobacterial fluorescence signals
نویسندگان
چکیده
Many drinking water utilities drawing from waters susceptible to harmful algal blooms (HABs) are implementing monitoring tools that can alert them the onset of blooms. Some have invested in fluorescence-based online probes measure phycocyanin, a pigment found cyanobacteria, but it is not clear how best use data generated. Previous studies focused on correlating phycocyanin fluorescence and cyanobacteria cell counts. However, all collect count data, making this method impossible apply some cases. Instead, paper proposes novel approach determine when utility needs respond HAB based machine learning by identifying anomalies without need for corresponding counts or biovolume. Four widespread open source algorithms evaluated collected at four buoys Lake Erie 2014 2019: local outlier factor (LOF), One-Class Support Vector Machine (SVM), elliptic envelope, Isolation Forest (iForest). When trained standardized historical 2018 tested labelled 2019 each buoy, SVM envelope models both achieve maximum average F1 score 0.86 among datasets. Therefore, promising detecting potential HABs using only.
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ژورنال
عنوان ژورنال: Water Research
سال: 2021
ISSN: ['0043-1354', '1879-2448']
DOI: https://doi.org/10.1016/j.watres.2021.117073