Understanding phytoplankton distribution and variation in the Arabian Sea and Persian Gulf: A cloud computing and machine learning approach using chlorophyll-a satellite data (2018-2022)

Phytoplankton, which play a vital role in marine ecosystems, require a thorough evaluation to assess the impact of climate change. This study focuses on the Arabian Sea and Persian Gulf, employing remote sensing and machine learning (ML) analysis from 2018 to 2022. Through the meticulous analysis of monthly fluctuations and interannual variations in chlorophyll-a (Chl-a) concentration, the research unravels crucial insights into the dynamics of phytoplankton biomass. Hotspots identified in the northern and western regions strategically pinpoint areas characterized by intense phytoplankton activity. ML models, including random forest (RF), support vector regressor (SVR), and linear regression (LR) are employed for precise time series forecasting, enhancing the accuracy of predictions. The study comparatively evaluates these models, identifying the SVR as the most effective, boasting an R2 value of 0.500 and minimal errors [root mean square error (RMSE) = 0.0945, mean absolute error (MAE) = 0.0714]. Demonstrating their potential, the ML models adeptly forecast Chl-a concentrations, providing valuable insights into phytoplankton dynamics. Exploring the intricate relationship between Chl-a concentration and environmental factors, such as nutrient availability and water temperature, provides further insights into the drivers of phytoplankton variability. Spatial pattern analysis reveals hotspots of elevated concentration along the coasts of Pakistan, India, and the Persian Gulf, indicating favorable conditions for phytoplankton growth. The study not only enhances the field of climate change impact assessment but also contributes to informed decision-making in ecosystem management and sustainable resource practices. Integrating ML with environmental data enhances the accuracy of time series forecasting for phytoplankton behavior. The application of spatial analysis provides additional context to phytoplankton distribution patterns and environmental influences, emphasizing the importance of considering these factors for a more nuanced understanding of their variability. In summary, this research significantly advances our knowledge of the intricate interplay within marine ecosystems, underscoring the paramount role of phytoplankton in global climate systems.