Adaptive Human--Machine Interfaces Based on Artificial Intelligence and Neuroergonomics for Reducing Human Errors in Complex Industrial Systems

Quita la maquetación en latex y deja el texto original: Algal blooms in tropical reservoirs can affect water quality and resource management, but continuous monitoring is often limited by the low frequency of \textit{in situ} sampling. This study assessed algal bloom potential through a reproducible workflow integrating remote sensing and meteorology. Satellite time series of chlorophyll-\textit{a} and a complementary indicator of floating cyanobacteria were analyzed together with precipitation and daily temperatures aggregated to the same temporal interval. Processing included clipping the area of interest, quality and coverage control, calculation of spatiotemporal statistics, recurrence maps, monthly synthesis, nonparametric lagged correlations, and an operational bloom-potential classification based on robust exceedances. The results showed intra-annual variability of chlorophyll-\textit{a} with areas of spatial recurrence, weak meteorological associations, and a minimal floating-cyanobacteria signal, supporting the use of this approach for regional monitoring and sampling prioritization.