Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling. Issue 3 (29th March 2016)

• Record Type: Journal Article
• Authors: Quilty, John; Adamowski, Jan; Khalil, Bahaa; Rathinasamy, Maheswaran
• Is Part Of: Water resources research. Volume 52:Issue 3(2016:Mar.)
• Page Start: 2299
• Page End: 2326
• Publication Date: 2016-03-29
• Subjects: input variable selection -- conditional mutual information -- uncertainty -- bootstrap -- rank‐ordering -- regression models; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2015WR016959
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The input variable selection problem has recently garnered much interest in the time series modeling community, especially within water resources applications, demonstrating that information theoretic (nonlinear)‐based input variable selection algorithms such as partial mutual information (PMI) selection (PMIS) provide an improved representation of the modeled process when compared to linear alternatives such as partial correlation input selection (PCIS). PMIS is a popular algorithm for water resources modeling problems considering nonlinear input variable selection; however, this method requires the specification of two nonlinear regression models, each with parametric settings that greatly influence the selected input variables. Other attempts to develop input variable selection methods using conditional mutual information (CMI) (an analog to PMI) have been formulated under different parametric pretenses such as k nearest‐neighbor (KNN) statistics or kernel density estimates (KDE). In this paper, we introduce a new input variable selection method based on CMI that uses a nonparametric multivariate continuous probability estimator based on Edgeworth approximations (EA). We improve the EA method by considering the uncertainty in the input variable selection procedure by introducing a bootstrap resampling procedure that uses rank statistics to order the selected input sets; we name our proposed method bootstrap rank‐ordered CMI (broCMI). We demonstrate the superior performance of broCMI when compared to CMI‐based alternatives (EA, KDE, and KNN), PMIS, and PCIS input variable selection algorithms on a set of seven synthetic test problems and a real‐world urban water demand (UWD) forecasting experiment in Ottawa, Canada. Key Points: A new nonlinear and nonparametric input variable selection technique is developed Input variable selection uncertainty is addressed via bootstrap resampling Improved input variable sets are generated via rank‐ordering resampled sets