Journal
ADVANCES IN WATER RESOURCES
Volume 32, Issue 4, Pages 582-593Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2009.01.009
Keywords
Mutual information; Input selection; MRMR; mMRMR; Modeling
Categories
Funding
- US National Science Foundation (NSF) [CBET 0747276]
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [0747276] Funding Source: National Science Foundation
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Input variable selection (IVS) is a necessary step in modeling water resources systems. Neglecting this step may lead to unnecessary model complexity and reduced model accuracy. In this paper, we apply the minimum redundancy maximum relevance (MRMR) algorithm to identifying the most relevant set of inputs in modeling a water resources system. We further introduce two modified versions of the MRMR algorithm (alpha-MRMR and beta-MRMR), where alpha and beta are correction factors that are found to increase and decrease as a power-law function, respectively, with the progress of the input selection algorithms and the increase of the number of selected input variables. We apply the proposed algorithms to 22 reservoirs in California to predict daily releases based on a set from a 121 potential input variables. Results indicate that the two proposed algorithms are good measures of model inputs as reflected in enhanced model performance. The alpha-MRMR and beta-MRMR values exhibit strong negative correlation to model performance as depicted in lower root-mean-square-error (RMSE) values. (C) 2009 Elsevier Ltd. All rights reserved.
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