用混合模型提升海洋叶绿素预测精度，助力赤潮预警
Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models
- LSTM与随机森林结合，兼顾时序与非线性特征建模
- 测试集R²达0.5386，显著优于单一模型
- 适合海洋生态监测与高频率环境变量预测
海洋叶绿素浓度是生态系统健康与碳循环强度的重要指标，其准确预测对赤潮预警和生态响应至关重要。本文提出一种LSTM-RF混合模型，融合LSTM的时间序列建模优势与随机森林的非线性特征刻画能力，克服单一模型的局限性。基于多源海洋数据（温度、盐度、溶解氧等）训练，实验结果显示，该模型在测试集上R²为0.5386，均方误差MSE为0.005806，平均绝对误差MAE为0.057147，显著优于单独使用LSTM（R²=0.0208）和RF（R²=0.4934）的表现。标准化处理与滑动窗口策略进一步提升了预测精度，为海洋生态变量的高频预测提供了创新解决方案。
原文摘要 · Abstract (English)
Marine chlorophyll concentration is an important indicator of ecosystem health and carbon cycle strength, and its accurate prediction is crucial for red tide warning and ecological response. In this paper, we propose a LSTM-RF hybrid model that combines the advantages of LSTM and RF, which solves the deficiencies of a single model in time-series modelling and nonlinear feature portrayal. Trained with multi-source ocean data(temperature, salinity, dissolved oxygen, etc.), the experimental results show that the LSTM-RF model has an R^2 of 0.5386, an MSE of 0.005806, and an MAE of 0.057147 on the test set, which is significantly better than using LSTM (R^2 = 0.0208) and RF (R^2 =0.4934) alone , respectively. The standardised treatment and sliding window approach improved the prediction accuracy of the model and provided an innovative solution for high-frequency prediction of marine ecological variables.
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