Toward creating simpler hydrological models: A LASSO subset selection approach. (October 2015)

• Record Type: Journal Article
• Authors: Bardsley, W.E.; Vetrova, V.; Liu, S.
• Is Part Of: Environmental modelling & software. Volume 72(2015:Oct.)
• Page Start: 33
• Page End: 43
• Publication Date: 2015-10
• Subjects: Model simplification -- Linear basis functions -- Linear LASSO -- Subset selection -- Finite mixture distributions -- Linear programming -- Runoff model -- Groundwater model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.06.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A formalised means of simplifying hydrological models concurrent with calibration is proposed for use when nonlinear models can be initially formulated as over-parameterised constrained absolute deviation regressions of nonlinear expressions. This provides a flexible modelling framework for approximation of nonlinear situations, while allowing the models to be amenable to algorithmic simplification. The degree of simplification is controlled by a user-specified forcing parameter λ. That is, an original over-parameterised linear model is reduced to a simpler working model which is no more complex than required for a given application. The degree of simplification is a compromise between two factors. With weak simplification most parameters will remain, risking calibration overfitting. On the other hand, a high degree of simplification generates inflexible models. The linear LASSO (Least Absolute Shrinkage and Selection Operator) is utilised for the simplification process because of its ability to deal with linear constraints in the over-parameterised initial model. Highlights: Models are first formulated as linearly-constrained linear functions. The linear functions can be linear combinations of pre-calculated nonlinear functions. The degree of simplification is controlled by a user-specified forcing parameter. Simplification and calibration are carried out by linear programming minimisation.