Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

Sang-Soo Baek,Jongcheol Pyo,Yong Sung Kwon,Hee-Mock Oh,Seong-Jun Chun,Kyung Hwa Cho,Chi-Yong Ahn,Seung Ho Baek,Young Ok Kim

doi:10.3389/fmars.2021.729954

Abstract

In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L–1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.

Highlights

The occurrence, period, and frequency of harmful algal blooms (HABs) have increased in recent years, thereby posing a serious threat to the aquatic ecosystem (Weiher and Sen, 2006; Gobler et al, 2017)
The growth and germination rates were more strongly influenced by temperature and salinity, respectively, whereas the operational taxonomic unit (OTU) related to A. catenella were affected by salinity and PO4-P
Previous studies have shown that salinity cannot improve model accuracy (Hjøllo et al, 2009; Martyr-Koller et al, 2017) because it is vulnerable to external sources, increasing simulation uncertainty (Arfib and Charlier, 2016)

Summary

Introduction

The occurrence, period, and frequency of harmful algal blooms (HABs) have increased in recent years, thereby posing a serious threat to the aquatic ecosystem (Weiher and Sen, 2006; Gobler et al, 2017). China and Japan have incurred enormous economic losses in northeast Asia (Wang and Wu, 2009; Itakura and Imai, 2014). These damages can be attributed to the changes in the aquatic environmental conditions due to climate change and/or nutrient enrichment caused by such human activities as agriculture, industrialization, tourism, and urbanization (Heisler et al, 2008; Gobler et al, 2017). HABs have escalated to become a global concern. Anthropogenic global warming is visible in the northward expansion of the warm pool to the northwestern Pacific. The Korean Peninsula, which is closed on the marginal sea of

Methods

Results

Conclusion