Harmful algal blooms can devastate marine ecosystems, contaminate fisheries, and threaten coastal economies. Because these events are driven by complex interactions among ocean currents, weather patterns, and nutrient transport, predicting their occurrence remains a major challenge. In this project, I developed a physics-guided machine learning framework to forecast harmful algal bloom conditions along the California coast. By combining satellite observations, oceanographic data, and physical transport equations, I investigated whether machine learning models could provide accurate forecasts while remaining consistent with the underlying physics governing bloom formation and movement.

Hurricanes are among the most destructive natural disasters in the world, causing immense economic damage and loss of life. Although modern forecasting methods have improved substantially, predictions beyond a few days remain challenging because of the atmosphere’s chaotic nature. In this project, I investigated whether atmospheric temperatures, polar motion data, and sunspot numbers could be combined with historical hurricane records in a multivariable machine learning framework. By incorporating these unconventional predictor variables into a time-series forecasting approach, I explored new ways to improve long-range hurricane prediction and better understand the environmental factors associated with hurricane occurrence.

Wildfires are becoming increasingly frequent and destructive, creating significant environmental, economic, and public health challenges. While modern machine learning models can predict wildfire risk, they often overlook factors that reduce fire severity, such as firefighting infrastructure and fuel management programs. In this project, I developed deep learning models that incorporate these human inhibition factors into wildfire forecasting across California. The results demonstrate that accounting for human intervention can improve predictive accuracy and provide new insights into strategies for reducing wildfire risk.