Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models

TL;DR

This paper introduces a hybrid LSTM-RF model for marine chlorophyll prediction, combining strengths of both methods to improve accuracy in ecological monitoring using multi-source ocean data.

Contribution

The study develops a novel LSTM-RF hybrid model that outperforms individual models in marine chlorophyll prediction, offering a new approach for ecological variable forecasting.

Findings

LSTM-RF achieves an R^2 of 0.5386, outperforming LSTM and RF alone.

Standardised treatment and sliding window enhance prediction accuracy.

Model provides a high-frequency prediction solution for marine ecological variables.

Abstract

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.