Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

Javier Linkolk López-Gonzales, Ana María Gómez Lamus, Romina Torres, Paulo Canas Rodrigues, Rodrigo Salas

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM (Formula presented.) pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM (Formula presented.) for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM (Formula presented.).

Original languageEnglish
Pages (from-to)1241-1259
Number of pages19
JournalStats
Volume6
Issue number4
DOIs
StatePublished - Dec 2023

Fingerprint

Dive into the research topics of 'Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values'. Together they form a unique fingerprint.

Cite this