A Novel Approach for Forecasting Algal Bloom: Long Short-Term Memory Artificial Neural Network Optimized by Arithmetic Mean Algorithm

Why It Matters

Accurate, low‑cost algal bloom forecasts help protect fisheries, tourism, and public health, and the AMOA‑enhanced LSTM provides a scalable solution for coastal monitoring worldwide.

Key Takeaways

• AMOA improves LSTM training efficiency and prediction accuracy
• LSTM‑AMOA outperforms traditional shallow and deep models on RMSE
• Statistical tests validate performance gains across 15 Black Sea stations
• Early algal bloom detection supports proactive ecosystem management
• Satellite‑derived data enables cost‑effective, wide‑area monitoring

Pulse Analysis

Artificial intelligence is reshaping environmental monitoring by turning massive satellite streams into actionable insights. Traditional statistical models often struggle with the nonlinear dynamics of algal blooms, leading to delayed or inaccurate alerts. By leveraging deep learning, especially recurrent architectures like LSTM, researchers can capture temporal dependencies in chlorophyll‑a time series, delivering forecasts that adapt to rapid ecological shifts. The integration of optimization techniques such as the arithmetic mean optimization algorithm (AMOA) further refines model training, reducing over‑fitting and accelerating convergence, which is critical for operational deployment in resource‑constrained agencies.

The LSTM‑AMOA framework demonstrated clear superiority in a rigorous head‑to‑head evaluation against both shallow machine‑learning methods and other deep networks. Across 15 monitoring stations in the Black Sea, the model achieved the lowest root‑mean‑square error and a mean rank of 1.33, indicating consistent performance. Robust statistical validation—including Friedman, Wilcoxon signed‑rank, and Holm‑Bonferroni corrections—confirmed that these gains are not random fluctuations but statistically significant improvements. Such evidence builds confidence for stakeholders seeking reliable, data‑driven tools to anticipate harmful algal events before they impact fisheries, tourism, and public health.

For industry and policymakers, the implications are twofold. First, the cost‑effective nature of satellite‑derived inputs combined with an automated LSTM‑AMOA pipeline lowers barriers to entry for regional monitoring programs, especially in developing coastal economies. Second, early detection enables proactive mitigation strategies—such as targeted water treatment, fishing advisories, and tourism alerts—thereby reducing economic losses and ecological damage. As climate change intensifies bloom frequency, scalable AI solutions like LSTM‑AMOA will become essential components of resilient marine management frameworks, prompting further investment in data infrastructure and interdisciplinary research.