Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea

中国海 环境科学 叶绿素a 人工神经网络 富营养化 叶绿素 对数 卫星 计算机科学 气象学 海洋学 人工智能 数学 生态学 地质学 生物 地理 植物 工程类 数学分析 营养物 航空航天工程
作者
Haobin Cen,Jiahan Jiang,Guoqing Han,Xiayan Lin,Yu Liu,Xiaoyan Jia,Qiyan Ji,Bo Li
出处
期刊:Remote Sensing [Multidisciplinary Digital Publishing Institute]
卷期号:14 (21): 5461-5461 被引量:27
标识
DOI:10.3390/rs14215461
摘要

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.
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