Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau

Abstract

Nowadays, the conception on environmental protection is increasingly rising up and one of the critical environmental issues is the air pollution due to the rapidly growth of economy and population. Hence, a significant forecasting for the air pollution index (API) becomes important as it can act as the alarm for alerting our awareness in the air pollution issue. In this research, an architecture for ensembles of ANFIS (Adaptive Neuro-Fuzzy Inference System) is proposed for forecasting the Macau API and the performance of the proposed method is compared with the conventional ANFIS and the results is verified by the performance indexes, Root Mean Square Error (RMSE) and Average Percentage Error (APE), showing that a promising result can be achieved. © 2012 Springer-Verlag.