Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation. (November 2015)

• Record Type: Journal Article
• Authors: Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.
• Is Part Of: Environmental modelling & software. Volume 73(2015:Nov.)
• Page Start: 175
• Page End: 188
• Publication Date: 2015-11
• Subjects: Extreme learning machines -- Support vector machine -- Artificial neural network -- Regression -- Environmental science -- Machine learning; 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.2015.08.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The extreme learning machine (ELM), a single-hidden layer feedforward neural network algorithm, was tested on nine environmental regression problems. The prediction accuracy and computational speed of the ensemble ELM were evaluated against multiple linear regression (MLR) and three nonlinear machine learning (ML) techniques – artificial neural network (ANN), support vector regression and random forest (RF). Simple automated algorithms were used to estimate the parameters (e.g. number of hidden neurons) needed for model training. Scaling the range of the random weights in ELM improved its performance. Excluding large datasets (with large number of cases and predictors), ELM tended to be the fastest among the nonlinear models. For large datasets, RF tended to be the fastest. ANN and ELM had similar skills, but ELM was much faster than ANN except for large datasets. Generally, the tested ML techniques outperformed MLR, but no single method was best for all the nine datasets. Highlights: We test extreme learning machine (ELM) for nonlinear regression on nine environmental datasets. We use automated algorithms to estimate the parameters of four nonlinear prediction methods. Scaling the range of the random weights improves the predictions of the ELM ensemble model. Excluding large datasets, ELM tends to be the fastest among the nonlinear models. No single method was best for all the nine datasets.