Genetic algorithm based technique for defining threshold regression models

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

Pires, JCM (Author) Other; Martins, FG (Author) FEUP; Alvim Ferraz, MCM (Author) Other; Pereira, MC (Author) FEUP

Conference proceedings International

Title: Proc. iEMSs 4th Biennial Meeting - Int. Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008

Pages: 303-310

4th Biennial Meeting of International Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making, iEMSs 2008

Barcelona, Catalonia, 7 July 2008 through 10 July 2008

Indexing

ISI Web of Knowledge

Scopus - 5 Citations

Other information

Authenticus ID: P-007-XZJ

Abstract (EN): This study proposes a new technique based on genetic algorithms to define threshold regression models (TR-GA). The threshold regression assumes that the behaviour of the dependent variable changes when it enters in a different regime. The change from one regime to another depends of a specific value (threshold value) of an explanatory variable (threshold variable). In this study, the threshold regression models were composed by two linear equations. The application of genetic algorithms allows evaluating, at the same time: (i) the threshold variable; (ii) the threshold value; and (iii) the statistically significant regression parameters in each regime. The aim of this study was to evaluate the performance of TR-GA models in the prediction of next day hourly average ozone (O 3|d+1) concentrations. The considered predictors were hourly average concentrations of carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO 2) and ozone (O 3) and meteorological data (temperature - T, solar radiation - SR, relative humidity - RH and wind speed - WS). The studies were performed in the period from May 2004 to July 2004. Different TR-GA models were obtained, corresponding to different threshold variables and threshold values. Considering both training and test periods and comparing with multiple linear regression (MLR) approach, better performance indexes were achieved in four of these models. The model that presented the best results showed that the O 3|d+1 change its behaviour at the temperature of 23 °C. For temperatures below 23 °C, O 3|d+1 depended on CO, NO, NO 2, SR, WS and O 3, for higher temperatures, it depended on CO, NO, T, WS and O 3, while for MLR, the most important variables were NO, NO 2, SR, RH, WS and O 3.

Language: English

Type (Professor's evaluation): Scientific