Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors
arXiv:2607.07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and evaluate a spatio-temporal machine-learning framework to predict harmful algal bloom (HAB) occurrence using exclusively satellite-derived predictors under realistic forecasting constraints. We characterised environmental and biological variability across shellfish production zones (L1-L9) using 5,882 observations, providing system-wide context. Predictive models were developed for zones L1-L2, a hot
The increasing availability of satellite data and advancements in machine learning are making real-time environmental prediction more feasible and accurate, especially for critical ecological events.
This development indicates a growing capacity to predict harmful natural phenomena using AI and remote sensing, which has significant implications for public health, aquaculture, and coastal economies.
The ability to predict harmful algal blooms with satellite-derived data allows for proactive management and mitigation strategies, moving from reactive responses to preventative action.
- · Coastal communities
- · Aquaculture industry
- · Environmental monitoring agencies
- · Satellite data providers
- · Shellfish harvesters without early warning systems
- · Tourism reliant on pristine coastal environments
Improved early warning systems for harmful algal blooms will reduce economic losses and health risks in affected regions.
This success could accelerate the application of similar AI-satellite frameworks to predict other environmental hazards, such as wildfires or pest outbreaks.
The broader adoption of such predictive models could redefine environmental resource management and impact global food security through more resilient aquaculture and agriculture.
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Read at arXiv cs.LG