Data-Driven Prediction of <i>Limnospira platensis</i> (<i>Spirulina</i>) Biomass from Experimental Time-Series Data

Accurate short-term forecasting of <i>Limnospira platensis</i> biomass is essential for optimizing experimental scheduling and cultivation strategies, yet small datasets and strong temporal autocorrelation pose significant challenges for model reliability. In this study, we developed a leakage-safe,...

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Autori principali:	Bartolomeo Cosenza, Marco Pomaré, Alessandro Concas, Giancarlo Cravotto, Alida Cosenza, Catalina Valencia Peroni, Luca Usai, Giovanni Antonio Lutzu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2026-05-01
Serie:	Biomass
Soggetti:	<i>Limnospira platensis</i> biomass growth data-driven modeling cultivation experiments time-series analysis gradient boosting
Accesso online:	https://www.mdpi.com/2673-8783/6/3/41
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