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Deep Learning-Based Rolling Forecasting of Dissolved Oxygen in Shandong Peninsula Coastal Waters

文献类型：期刊论文

作者Wang, Yanjun1,2,3; Song, Jinming1,2; Li, Xuegang1,2; Zhong, Guorong1
刊名WATER
出版日期2025-10-30
卷号17期号:21页码:19
关键词dissolved oxygen coastal waters deep learning rolling forecasting time series modeling marine ranching
DOI10.3390/w17213102
通讯作者Song, Jinming(jmsong@qdio.ac.cn)
英文摘要Changes in nearshore water quality directly influence ecosystem stability and the sustainability of aquaculture production. Among these factors, rapid fluctuations in dissolved oxygen (DO) can compromise the physiological functions of aquatic organisms, often leading to mass mortality events and significant economic losses. To enhance the predictive capability of DO in marine ranching areas, this study evaluates multiple forecasting approaches, including AutoARIMA, XGBoost, BlockRNN-LSTM, BlockRNN-GRU, TCN, Transformer, and an ensemble model that integrates these methods. Using hourly DO observations from coastal buoys, we performed multi-step rolling forecasts and systematically assessed model performance across multiple evaluation metrics (MAPE, RMSE, and R2), complemented by residual and error distribution analyses. The results show that the ensemble model, based on deep learning techniques, consistently outperforms individual models, achieving higher forecast robustness and more effective variance control, with MAPE values maintained below 4% across all three buoys. Building upon these findings, we further developed and deployed a DO forecasting and early-warning system centered on the ensemble framework. This system enables end-to-end functionality, including automatic data acquisition, real-time prediction, hypoxia risk identification, and alert dissemination. It has already been applied in marine ranching operations, providing 1-3 day forecasts of DO dynamics, facilitating the early detection of hypoxia risks, and significantly improving the scientific support and responsiveness of aquaculture management.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; National Natural Science Foundation of China[42176200] ; Institute of Oceanology, Chinese Academy of Sciences ; National Key Research and Development Program of China[2022YFC3104305]
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001612403500001
出版者MDPI
源URL[http://ir.qdio.ac.cn/handle/337002/203791]  
专题海洋研究所_海洋生态与环境科学重点实验室
通讯作者Song, Jinming
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm, Qingdao 266071, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Dept Marine Sci Data Ctr, Qingdao 266071, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yanjun,Song, Jinming,Li, Xuegang,et al. Deep Learning-Based Rolling Forecasting of Dissolved Oxygen in Shandong Peninsula Coastal Waters[J]. WATER,2025,17(21):19.
APA Wang, Yanjun,Song, Jinming,Li, Xuegang,&Zhong, Guorong.(2025).Deep Learning-Based Rolling Forecasting of Dissolved Oxygen in Shandong Peninsula Coastal Waters.WATER,17(21),19.
MLA Wang, Yanjun,et al."Deep Learning-Based Rolling Forecasting of Dissolved Oxygen in Shandong Peninsula Coastal Waters".WATER 17.21(2025):19.

入库方式： OAI收割

来源：海洋研究所

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