Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Application of PCA and clustering methods in input selection of hybrid runoff models

Remesan, R., Bray, M. ORCID: and Mathew, J. 2018. Application of PCA and clustering methods in input selection of hybrid runoff models. Journal of Environmental Informatics 31 (2) , pp. 137-152. 10.3808/jei.201700378

Full text not available from this repository.


This study has proposed and investigated a novel input variable selection method for nonlinear modelling based on principle component analysis (PCA) and cluster analysis. The proposed approach was applied to daily rainfall-runoff modelling of the Brue catchment of the United Kingdom using wavelet based hybrid forms of two nonlinear models, Artificial Neural Networks (ANNs) and Local Linear Regression (LLR), to identify meaningful wavelet decomposed sub-series. The homogenous group formation capability of cluster analysis and redundancy assessment capability of PCA were applied effectively in this study to solve input selection uncertainties associated with wavelet based hybrid models. Though this concept has been represented in the selection of effective wavelet decomposed subseries in runoff modelling, the application has gotten wider implications in time series modelling with highly redundant and large input space. The study revealed the weakness of conventional forms of cross-correlation analysis and also suggested that input selection could be improved by making sufficient natural clusters (equal to the desired number of input data series) of input space and restricting the search within each cluster according to silhouette or correlation value. The study also highlighted the higher modelling capability of ANN over traditional LLR models in rainfall-runoff modelling of the Brue catchment.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: International Society for Environmental Information Sciences
ISSN: 1726-2135
Date of Acceptance: 15 March 2015
Last Modified: 23 Oct 2022 13:53

Citation Data

Cited 19 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item