Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models
This provides an incremental solution for marine ecologists and environmental managers needing accurate, high-frequency predictions of chlorophyll levels to monitor ecosystem health and issue warnings.
The paper tackled the problem of predicting marine chlorophyll concentration, a key indicator for ecosystem health and red tide warnings, by proposing an LSTM-RF hybrid model that achieved an R^2 of 0.5386, MSE of 0.005806, and MAE of 0.057147 on the test set, significantly outperforming individual LSTM and RF models.
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.