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Water Resources Management

Erschienen in:

25.07.2022

Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network

verfasst von: Xuan Wang, Wenchong Tian, Zhenliang Liao

Erschienen in: Water Resources Management | Ausgabe 11/2022

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Abstract

The Feedforward Neural Network (FNN) is currently commonly used in problems involving water resources. However, the hyperparameters have received minimal attention: (i) Very little research has looked at how the hyperparameters affect FNN performance. (ii) Trial-and-error hyperparameter selection may have a significant degree of randomness. For the impact analysis and selection of hyperparameters, a model-based framework was proposed in this study. Through the framework, it was possible to (i) estimate how the hyperparameters affect model performance, offering theoretical direction for the hyperparameter selection, and (ii) select the hyperparameters based on the posterior hyperparameter distributions, with a certain degree of robustness. A case study of surface water quality prediction was carried out with 1,492,992 FNNs developed and trained using different hyperparameter sets. Theoretical inferences from the hyperparameter impact analysis were drawn, and a set of hyperparameters was chosen based on the obtained posterior hyperparameter distributions. The framework’s applicability in the research on water resources FNN for the impact analysis and selection of hyperparameters was demonstrated through the case study.

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Titel: Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network
verfasst von: Xuan Wang
Wenchong Tian
Zhenliang Liao
Publikationsdatum: 25.07.2022
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 11/2022
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03248-4

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