Work

Transferability of machine-learning-based modeling frameworks across flood events for hindcasting maximum river water depths in coastal watersheds

Year: 2024

Type: article

Abstract: . Despite applications of machine learning (ML) models for predicting floods, their transferability for out-of-sample data has not been explored. This paper developed an ML-based model for hindcasting maximum flood depths during major events in coastal watersheds and evaluated its transferability across other events (out-of-sample). The model considered spatial distribution of influential factors, which explain underlying physical processes, to hindcast maximum river flood depths. Our model evaluation in a HUC6 watershed in Northeastern US showed that the model satisfactorily hindcasted maximum flood depths at 116 stream gauges during a major flood event, Hurricane Ida (R2 of 0.92). The pre-trained, validated model was successfully transferred to three other major flood events, Hurricanes Isaias, Sandy, and Irene (R2 > 0.71). Our results showed that ML-based models can be transferable for hindcasting maximum river flood depths across events when informed by the spatial distribution of pertinent features and underlying physical processes in coastal watersheds.

Source: Natural hazards and earth system sciences

DOI: 10.5194/nhess-24-3537-2024

Authors: Maryam Pakdehi, Ebrahim Ahmadisharaf, Behzad Nazari, Eunsaem Cho

Institutions: Florida State University, Florida A&M University - Florida State University College of Engineering, The University of Texas at Arlington

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,553

Funders: National Science Foundation, Florida State University, Directorate for Engineering, Division of Civil, Mechanical and Manufacturing Innovation