A Comparison of In-Sample and Out-of-Sample Model Selection Approaches for Artificial Neural Network (ANN) Daily Streamflow Simulation

A Comparison of In-Sample and Out-of-Sample Model Selection Approaches for Artificial Neural Network (ANN) Daily Streamflow Simulation

Artificial Neural Networks (ANN) have been widely applied in hydrologic and water quality (H/WQ) modeling in the past three decades. Many studies have demonstrated an ANN’s capability to successfully estimate daily streamflow from meteorological data on the watershed level. One major challenge of AN...

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Bibliographic Details
Main Authors: Xiaohan Mei, Patricia K. Smith
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Water
Subjects:
artificial neural network (ANN)
rainfall–runoff modeling
model selection
cross-validation
Akaike’s information criterion
Bayesian information criterion
Online Access:https://www.mdpi.com/2073-4441/13/18/2525

Internet

https://www.mdpi.com/2073-4441/13/18/2525

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