Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Mo...

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Bibliographic Details
Main Authors: Jeremy Feinstein, Quentin Ploussard, Thomas Veselka, Eugene Yan
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Water
Subjects:
linear programming
unit hydrograph
deep learning
convolutional neural network
convolution methods
Online Access:https://www.mdpi.com/2073-4441/15/21/3843

Internet

https://www.mdpi.com/2073-4441/15/21/3843

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