An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models
JOURNAL OF HYDROLOGY, vol.542, pp.18-34, 2016 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 542
- Publication Date: 2016
- Doi Number: 10.1016/j.jhydrol.2016.07.045
- Journal Name: JOURNAL OF HYDROLOGY
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.18-34
- Keywords: Input variable selection, Information theory, Data-driven models, Extreme learning machines, Neural networks, INPUT VARIABLE SELECTION, ARTIFICIAL NEURAL-NETWORK, EXTREME LEARNING-MACHINE, WATER-RESOURCES, MUTUAL INFORMATION, RUNOFF, RELEVANCE, ENTROPY, OPTIMIZATION, UNCERTAINTY
- Middle East Technical University Affiliated: Yes
Abstract
This work investigates the uncertainty associated to the presence of multiple subsets of predictors yielding data-driven models with the same, or similar, predictive accuracy. To handle this uncertainty effectively, we introduce a novel input variable selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), specifically conceived to identify all alternate subsets of predictors in a given dataset. The search process is based on a four-objective optimization problem that minimizes the number of selected predictors, maximizes the predictive accuracy of a data-driven model and optimizes two information theoretic metrics of relevance and redundancy, which guarantee that the selected subsets are highly informative and with little intra-subset similarity. The algorithm is first tested on two synthetic test problems and then demonstrated on a real-world streamfiow prediction problem in the Yampa River catchment (US). Results show that complex hydro-meteorological datasets are characterized by a large number of alternate subsets of predictors, which provides useful insights on the underlying physical processes. Furthermore, the presence of multiple subsets of predictors and associated models helps find a better trade-off between different measures of predictive accuracy commonly adopted for hydrological modelling problems. (C) 2016 Elsevier B.V. All rights reserved.