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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Jung Min Ahn, Jungwook Kim, Kyunghyun Kim
Format: Artykuł
Język:English
Wydane: MDPI AG 2023-10-01
Seria:Toxins
Hasła przedmiotowe:
harmful algal blooms
Gradient Boosting
attention-based CNN-LSTM
Bayesian optimization
ensemble techniques
Dostęp online:https://www.mdpi.com/2072-6651/15/10/608

Internet

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

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