Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models. (March 2015)

• Record Type: Journal Article
• Authors: Li, Xuyuan; Maier, Holger R.; Zecchin, Aaron C.
• Is Part Of: Environmental modelling & software. Volume 65(2015:Mar.)
• Page Start: 15
• Page End: 29
• Publication Date: 2015-03
• Subjects: Artificial neural networks -- General regression neural networks -- Partial mutual information -- Kernel bandwidth -- Kernel density estimation -- Environment -- Hydrology and water resources -- Input variable selection; 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.2014.11.028
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Input variable selection (IVS) is one of the most important steps in the development of artificial neural network and other data driven environmental and water resources models. Partial mutual information (PMI) is one of the most promising approaches to IVS, but has the disadvantage of requiring kernel density estimates (KDEs) of the data to be obtained, which can become problematic when the data are non-normally distributed, as is often the case for environmental and water resources problems. In order to overcome this issue, preliminary guidelines for the selection of the most appropriate methods for obtaining the required KDEs are determined based on the results of 3780 trials using synthetic data with distributions of varying degrees of non-normality and six different KDE techniques. The validity of the guidelines is confirmed for two semi-real case studies developed based on the forecasting of river salinity and rainfall-runoff modelling problems. Highlights: We address the performance of the PMI IVS influenced by the normality of data. We improved the performance of the PMI IVS for non-normally distributed data. Conventional PMI IVS performs well only for data following Gaussian distribution. Bandwidth with reduced Gaussian assumption improves the accuracy of PMI IVS. Preliminary guidelines are developed for PMI IVS and successfully validated.