Interpretable Machine Learning-Based Spring Algal Bloom Forecast Model for the Coastal Waters of Zhejiang  

作　　者：HUANG Guoqiang BAO Min ZHANG Zhao GU Dongming LIANG Liansong TAO Bangyi 

机构地区：[1]State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography,Ministry of Natural Resource,Hangzhou 310012,China [2]Wenzhou Marine Center,Ministry of Natural Resources,Wenzhou 325000,China [3]Observation and Research Station of Yangtze River Delta Marine Ecosystems,Ministry of Natural Resources,Zhoushan 316000,China [4]Key Laboratory of Marine Ecological Monitoring and Restoration Technologies,Ministry of Natural Resource,Shanghai 201206,China

出　　处：《Journal of Ocean University of China》2025年第1期1-12,共12页中国海洋大学学报(英文版)

基　　金：the Zhejiang Provincial Natural Science Foundation of China(No.LY21D 060003);the Project of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Ocean-ography,MNR(No.SOEDZZ2103);the National Natural Science Foundation of China(No.42076216);the Open Research Fund of the Key Laboratory of Marine Ecological Monitoring and Restoration Technologies,MNR(No.MEMRT202210)。

摘　　要：The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study.A long short-term memory recurrent neural network was employed,and a predictive model for spring algal bloom in this region was developed.This model integrated various inputs,including temperature,wind speed,and other pertinent variables,and chlorophyll concentration served as the primary output indicator.The model training used chlorophyll concentration data,which were supplemented by reanalysis and forecast temperature and wind field data.The model demonstrated proficiency in forecasting next-day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold.The historical validation from 2016 to 2019 corroborated the model's accuracy with an 81.71%probability of correct prediction,which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023.An interpretable machine learning-based model for spring algal bloom prediction,displaying effective forecasting with limited data,was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables.The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.

关 键 词：spring algal bloom FORECAST LSTM interpretable 

分 类 号：F42[经济管理—产业经济]