Machine Learning Methods to Approximate Rainfall and Wind From Acoustic Underwater Measurements (February 2020)

Abstract

One method of measuring precipitation and wind over the ocean is through analysis of the underwater ambient acoustics. In this study, the ambient ocean noises recorded by a passive aquatic listener (PAL) in the Mediterranean are used to compare the effectiveness of the machine learning techniques for measuring the wind speed and precipitation rate with the empirical methods from previous works. The data were collected over the timeframe of June 2011 to May 2012 and included two storms that caused severe coastal flooding in Genoa, Italy. A spar buoy at the surface above the PAL provided high-quality in situ measurements to act as the reference data for model training and validation. The results using the machine learning models show correlation coefficients of 0.95 between the acoustic data and wind speed and a reduction in unexplained variance by over a third from previous methods. For precipitation, CatBoost and random forest models are used to measure the total precipitation for 12 events using leave-one-event-out cross-validation, demonstrating mean errors of 28% and 34% and median errors of 18% and 17%, respectively. The ability to measure wind and precipitation by applying machine learning on data from underwater acoustic recorders shows potential to help improve in situ measurements over oceans globally.