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Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau
Lei K.S.; Wan F.
Conference NameNinth International Symposium on Neural Networks (ISNN 2012)
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7367 LNCS
IssuePART 1
Conference DateJuly 11-14, 2012
Conference PlaceShenyang

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.

KeywordAnfis Api Ensemble Learning Rmse
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Document TypeConference paper
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Lei K.S.,Wan F.. Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau[C],2012:509-516.
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