Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai

© 2016 The Authors. Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them...

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Main Authors: Wongsathan R., Chankham S.
Format: Conference Proceeding
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999873181&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42322
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-423222017-09-28T04:26:25Z Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai Wongsathan R. Chankham S. © 2016 The Authors. Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later. 2017-09-28T04:26:25Z 2017-09-28T04:26:25Z 2016-01-01 Conference Proceeding 2-s2.0-84999873181 10.1016/j.procs.2016.05.062 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999873181&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42322
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description © 2016 The Authors. Since the air monitoring stations do not provide the relation between other toxic gas and meteorological parameters with the particulate matter up to 10 micrometer or PM-10. The influence of meteorological as well as correlation with other toxic gas is investigated and used them to forecast PM-10 in the case of Chiang Mai province of Thailand. In this paper an attempt to develop hybrid models of an Autoregressive Integrated Moving Average (ARIMA) model with other exogenous variables (ARIMAX) and Neural Networks (NNs), the two hybrid models, i.e. hybrid ARIMAX-NNs model and hybrid NNs-ARIMAX model were implemented to forecast PM-10 for highly season during January-April of Chiang Mai Province. Simulation results of hybrid model are compared with the results of ARIMA, ARIMAX and NNs model. The experimental results demonstrated that the hybrid NNs-ARIMAX model outperformed best over the hybrid ARIMAX-NNs model, ARIMAX model, NNs model, and ARIMA model respectively. In this case study and maybe other cases, it has proved that the NNs model should be priori captured and filtered the non-stationary non-linear component while the fully linearly stationary residuals were accurately predicted by ARIMAX model later.
format Conference Proceeding
author Wongsathan R.
Chankham S.
spellingShingle Wongsathan R.
Chankham S.
Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
author_facet Wongsathan R.
Chankham S.
author_sort Wongsathan R.
title Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
title_short Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
title_full Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
title_fullStr Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
title_full_unstemmed Improvement on PM-10 Forecast by Using Hybrid ARIMAX and Neural Networks Model for the Summer Season in Chiang Mai
title_sort improvement on pm-10 forecast by using hybrid arimax and neural networks model for the summer season in chiang mai
publishDate 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84999873181&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/42322
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