Abstract
An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.
Highlights
The overgrowth of algae, known as algal blooms, has been a continuous global issue in the management of freshwater systems for several decades
Four convolutional neural network (CNN) models were developed for the classification of representative algal genera of harmful algal blooms (HABs) from algal images obtained using flow cytometer and microscope (FlowCAM)
Our research showed that the well-defined CNN model generated from the neural architecture search technology can be an alternative technique to replace conventional manual CNN
Summary
The overgrowth of algae, known as algal blooms, has been a continuous global issue in the management of freshwater systems for several decades. It is affected by various physical factors (e.g., temperature and sunlight) [1,2,3,4] and other natural or anthropogenic factors (e.g., nutrient input, seasonal changes in water flow, and climate change) [5,6,7]. Direct and continuous monitoring is essential for rapid and effective operational responses in water management districts and utilities for processing drinking water against undesired algal bloom events.
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