Document Type

Article

Publication Date

2025

DOI

10.22541/essoar.175492904.46217552/v1

Publication Title

ESS Open Archive

Pages

50 pp.

Abstract

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training 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 a 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 re-training proved most effective and minimal re-training 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 target data increased beyond 18 flood events, the benefit of TL diminished relative to training a 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.

Rights

© 2025 The Authors.

Published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

You are free to copy and redistribute the material in any medium or format for any purpose, even commercially.

Under the following terms: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Comments

This is a preprint and has not been peer reviewed. Data may be preliminary.

Original Publication Citation

Roy, B., Goodall, J. L., McSpadden, D., Kumar, C., Goldenberg, S., Wang, Y., & Schram, M. (2025). Applying transfer learning for street-scale nuisance flood forecasting in coastal-urban cities. ESS Open Archive. https://doi.org/10.22541/essoar.175492904.46217552/v1

ORCID

0000-0003-2755-7338 (Kumar)

Download

Share

COinS