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

Xuan Wang, Wenchong Tian and Zhenliang Liao ()
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Xuan Wang: Tongji University
Wenchong Tian: Tongji University
Zhenliang Liao: Tongji University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 11, No 15, 4217 pages

Abstract: 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.

Keywords: Water resources model; Feedforward neural network; Impact analysis; Hyperparameter selection; Surface water quality prediction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03248-4

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