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

Abstract

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.

Spatiotemporal Deep-Learning-Based Algal Bloom Prediction for Lake Okeechobee Using Multisource Data Fusion

“…From the analysis of the above articles, the prediction of HAB must take into account its spatio-temporal dependencies. In similar research, a convolutional LSTM (ConvLSTM) is used to build a trainable model for spatio-temporal sequence forecasting problems and applied to end-to-end precipitation nowcasting [15] and Chlorophyll-a concentration prediction [16], [17]. Results show that the ConvLSTM network can capture spatio-temporal correlations well, and this inspires the use of ConvLSTM for HAB prediction in this paper.…”