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An important challenge with Machine Learning (ML) is itstransferability; i.e., whether a ML model trained on one set of data canbe applied to a second set of data without requiring full re-training ofthe model. Transfer Learning (TL) addresses this challenge bytransferring knowledge learned in the source domain (the data it wastrained on) to the target domain (a second set of data that isstatistically different but related, which the model was not trainedon). This study investigates the use of TL for street-scale nuisanceflood forecasting by exploring whether a ML model trained for one set ofstreets can effectively forecast flooding for another set of streets inthe 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 re-training proved mosteffective and minimal re-training of only the output layer wasinsufficient. The advantage of TL was most pronounced when target datawas limited, meaning data collected at the new water depth sensorlocation included generally less than 18 flood events. As target dataincreased beyond 18 flood events, the benefit of TL diminished relativeto training a ML model directly on the local flood events. Thesefindings can assist cities as they implement street-scale flood sensingsystems to create accurate forecasts for new sensing locations that donot yet have sufficient data records to train a local ML model.