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Convolution Neural Network for the Prediction of Cochlodinium polykrikoides Bloom in the South Sea of Korea

Youngjin Choi    
Youngmin Park    
Weol-Ae Lim    
Seung-Hwan Min and Joon-Soo Lee    

Resumen

In this study, the occurrence of Cochlodinium polykrikoides bloom was predicted based on spatial information. The South Sea of Korea (SSK), where C. polykrikoides bloom occurs every year, was divided into three concentrated areas. For each domain, the optimal model configuration was determined by designing a verification experiment with 1?3 convolutional neural network (CNN) layers and 50?300 training times. Finally, we predicted the occurrence of C. polykrikoides bloom based on 3 CNN layers and 300 training times that showed the best results. The experimental results for the three areas showed that the average pixel accuracy was 96.22%, mean accuracy was 91.55%, mean IU was 81.5%, and frequency weighted IU was 84.57%, all of which showed above 80% prediction accuracy, indicating the achievement of appropriate performance. Our results show that the occurrence of C. polykrikoides bloom can be derived from atmosphere and ocean forecast information.

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Applied Sciences
AI
Aerospace

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