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

• URL: http://arxiv.org/abs/2508.05260v1
• Date: Thu, 07 Aug 2025 10:55:42 GMT
• Title: Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models
• Authors: Zhouyao Qian, Yang Chen, Baodian Li, Shuyi Zhang, Zhen Tian, Gongsen Wang, Tianyue Gu, Xinyu Zhou, Huilin Chen, Xinyi Li, Hao Zhu, Shuyao Zhang, Zongheng Li, Siyuan Wang,
• Abstract summary: Marine chlorophyll concentration is an important indicator of ecosystem health and carbon cycle strength.<n>In this paper, we propose a LSTM-RF hybrid model that combines the advantages of LSTM and RF.<n>The experimental results show that the LSTM-RF model has an R2 of 0.5386, an MSE of 0.005806 and an MAE of 0.057147 on the test set.
• Score: 16.4231225691788
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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