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An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models
Date
2016-11-01
Author
TAORMİNA, RİCCARDO
GALELLİ, STEFANO
Karakaya, Gülşah
Ahipasaoglu, S. D.
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Water Science and Technology
URI
https://hdl.handle.net/11511/35873
Journal
JOURNAL OF HYDROLOGY
DOI
https://doi.org/10.1016/j.jhydrol.2016.07.045
Collections
Department of Business Administration, Article