ArticleEnvironmental monitoring and assessment2025
A hybrid ACO-random forest optimization framework for scalable microalgae biomass estimation using multispectral imaging.
Article in Environmental monitoring and assessment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate estimation of algal biomass is essential for monitoring ecosystem productivity, managing aquaculture systems, and optimizing bioresource applications. However, traditional in situ methods are labor-intensive and spatially limited, while remote sensing approaches struggle with nonlinear spectral-biological relationships and the complexity of high-dimensional models. This study develops a hybrid ant colony optimization-random forest regression (ACO-RFR) framework that integrates feature selection with hyperparameter optimization to improve biomass prediction from multispectral imagery. The preprocessing pipeline combined reflectance normalization, multicollinearity screening, and outlier detection to reduce redundancy and noise. The ACO-RFR achieved both feature reduction and robust optimization, yielding high predictive accuracy (R
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