Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach
The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2025
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/124880/2/124880_Optimizing%20radial%20basis%20function.pdf http://irep.iium.edu.my/124880/ https://ijece.iaescore.com/index.php/IJECE/article/view/40024 |
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| Summary: | The deployment of artificial intelligence in environmental monitoring
demands models balancing efficiency, interpretability, and computational
cost. This study proposes a hybrid radial basis function network (RBFN)
framework integrated with fuzzy c-means (FCM) clustering for predicting
harmful algal blooms (HABs) using water quality parameters. Unlike
conventional approaches, our model leverages localized activation functions
to capture non-linear relationships while maintaining computational
efficiency. Experimental results demonstrate that the RBFN-FCM hybrid
achieved high accuracy (F1-score: 1.00) on test data and identified
Chlorophyll-a as the strongest predictor (r=0.94). However, real-world
validation revealed critical limitations: the model failed to generalize
datasets with incomplete features or distribution shifts, predicting zero HAB
outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with
Random Forests confirmed the RBFN-FCM's advantages in training speed
and interpretability but highlighted its sensitivity to input completeness. This
work underscores the potential of RBFNs as lightweight, explainable tools
for environmental forecasting while emphasizing the need for robustness
against data variability. The framework offers a foundation for real-time
decision support in ecological conservation, pending further refinement for
field deployment. |
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