Application of a convolutional neural network to improve automated early warning of harmful algal blooms.
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abstract
Continuous monitoring and early warning together represent an important mitigation strategy for harmful algal blooms (HAB). The coast of Texas experiences periodic blooms of three HAB dinoflagellates: Karenia brevis, Dinophysis ovum, and Prorocentrum texanum. A plankton image data set acquired by an Imaging FlowCytobot over a decade of operation was used to train and evaluate two new automated image classifiers. A 112 class, random forest classifier (RF_112) and a 112 class, convolutional neural network classifier (CNN_112) were developed and compared with an existing, 54 class, random forest classifier (RF_54) already in use as an early warning notification system. Both 112 class classifiers exhibited improved performance over the RF_54 classifier when tested on three different HAB species with the CNN_112 classifier producing fewer false positives and false negatives in most of the cases tested. For K. brevis and P. texanum, the current threshold of 2 cells.mL-1 was identified as the best threshold to minimize the number of false positives and false negatives. For D. ovum, a threshold of 1 cell.mL-1 was found to produce the best results with regard to the number of false positives/negatives. A lower threshold will result in earlier notification of an increase in cell concentration and will provide state health managers with increased lead time to prepare for an impending HAB.
