ConferencePoster
 

SELF-ORGANIZING ENSEMBLE OF LSTM TO ENHANCE THE AIR POLLUTION ESTIMATION IN SANTIAGO OF CHILE

Ubal, Cristian Rodrigo; Salas, Rodrigo; Torres, Romina; Nicolis, Orietta

Keywords: Machine Learning, LSTM Networks, SOM

Abstract

The objective of this research is to group the scenarios and estimate the highest PM2.5 pollution in the "La Florida" area. For this, machine learning techniques will be used, such as Self-Organizing Maps (SOM) and Long short- term memory networks (LSTM)

Más información

Año de Inicio/Término: Agosto 2019
Idioma: Ingles