Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting

Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation and management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in the past...

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Bibliographic Details
Main Authors: Jung Min Ahn, Jungwook Kim, Kyunghyun Kim
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Toxins
Subjects:
harmful algal blooms
Gradient Boosting
attention-based CNN-LSTM
Bayesian optimization
ensemble techniques
Online Access:https://www.mdpi.com/2072-6651/15/10/608

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https://www.mdpi.com/2072-6651/15/10/608

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