Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence

Prediction of the index of refraction structure constant Cn2 in the low-altitude maritime environment is challenging. To improve predictive models, deeper understanding of the relationships between environmental parameters and optical turbulence is required. To that end, a robust data set of Cn2 as well as numerous meteorological parameters were collected over a period of approximately 15 months along the Chesapeake Bay adjacent to the Severn River in Annapolis, Maryland. The goal was to derive new insights into the physical relationships affecting optical turbulence in the near-maritime environment. Using data-driven machine learning feature selection approaches, the relative importance of 12 distinct, measurable environmental parameters was analyzed and evaluated. Random forest nodal purity analysis was the primary machine learning approach to relative importance determination. The relative feature importance results indicated that air temperature and pressure were important parameters in predicting Cn2 in the maritime environment. In addition, the relative importance findings suggest that the air-water temperature difference, temporal hour weight, and time of year, as measured through seasonality, have strong associations with Cn2 and could be included to improve model prediction accuracy.