Hindcasting Maximum Water Depths in Coastal Watersheds: The Importance of Incorporating Off‐Channel Data and Their Uncertainties in Machine Learning Models
Abstract In the absence of adequate observations on the off‐channel areas, flood models are typically trained and validated against stream water depths. This approach can be efficient for physics‐based models, which incorporate the underlying physical processes, but the efficiency for data‐driven mo...
Saved in:
| Main Authors: | Maryam Pakdehi, Ebrahim Ahmadisharaf |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR039244 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hindcasting the typhoon haiyan storm surge in coastal eastern leyte
by: Jeferson Zerrudo, et al.
Published: (2024-12-01) -
Understanding the Role of the North Pacific Victoria Mode in ENSO Predictability Based on the NMME Hindcasts
by: Zhengyi Ren, et al.
Published: (2025-02-01) -
First Successful Hindcasts of the 2016 Disruption of the Stratospheric Quasi‐biennial Oscillation
by: S. Watanabe, et al.
Published: (2018-02-01) -
Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models
by: Damjan Bujak, et al.
Published: (2025-07-01) -
A Holistic Approach for Coastal–Watershed Management on Tourist Islands: A Case Study from Petra–Molyvos Coast, Lesvos Island (Greece)
by: Stamatia Papasarafianou, et al.
Published: (2024-12-01)