Machine Learning, Generalization, and Transfer Learning for Predicting the Exceedance of Fecal Indicator Bacteria Thresholds at Beaches.

Beach water testing for fecal indicator bacteria (FIB) is a key element of public health protection for beachgoers. Because the process can be expensive and time-consuming, many beaches are infrequently monitored, putting the health of the public at risk. Machine learning (ML) models using large sets of FIB, weather, and other types of environmental data have been applied to predict FIB levels at beaches. If ML models developed using data from frequently monitored beaches in one location could be effectively applied to another location (referred to as "generalization"), public health protections could be easily extended to those infrequently monitored beaches. We found that source to target generalization augmented by transfer learning (TL) can predict FIB threshold exceedance with a specificity of 0.70 to 0.81 and sensitivity ranging from 0.28 to 0.76, depending on the beaches and TL methods. This degree of specificity and the high end of the sensitivity range are comparable to the performance of regression and ML models developed by using data from a given beach and applied to that same beach. With the addition of TL, we observed statistically significant improvements in model performance over source to target generalization, with increases of 28.3% in WF1 scores and 5.4% in AUC. Future research into optimizing the selection of data-rich source beaches for developing models that can be applied to a given target beach may further improve transfer learning.