Applying Transfer Learning for Street‐Scale Nuisance Flood Forecasting in Coastal‐Urban Environments

Abstract

An important challenge with Machine Learning (ML) is its transferability; that is, whether an ML model trained on one set of data can be applied to a second set of data without requiring full retraining of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street‐scale nuisance flood forecasting by exploring whether an ML model trained for one set of streets can effectively forecast flooding for another set of streets in the same city. TL is explored using a Long Short‐Term Memory (LSTM) model trained on data for the flood‐prone streets of Norfolk City, Virginia. The results show that full‐weight retraining proved most effective and minimal retraining of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As the target data increased beyond 18 flood events, the benefit of TL diminished relative to training an ML model directly on the local flood events. These findings can assist cities as they implement street‐scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.