An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models

Taormina, R., Galelli, S., Karakaya, G. and Ahipasaoglu, S. D. (2016) An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models. Journal of Hydrology, 542, 18-34. (doi:10.1016/j.jhydrol.2016.07.045).

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 streamflow 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.

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Contributors

Author: S. D. Ahipasaoglu

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