Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting

Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP...

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Main Authors: Cho, Kar Mun, Nur Haizum Abd Rahman, Iszuanie Syafidza Che Ilias
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/20469/
http://journalarticle.ukm.my/20469/1/23.pdf
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author Cho, Kar Mun
Nur Haizum Abd Rahman,
Iszuanie Syafidza Che Ilias,
author_facet Cho, Kar Mun
Nur Haizum Abd Rahman,
Iszuanie Syafidza Che Ilias,
author_sort Cho, Kar Mun
building UKM Institutional Repository
collection Online Access
description Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP with both Levenberg-Marquardt algorithm and conjugate gradient method. These methods were used to predict the Air Pollutant Index (API) in Malaysia’s central region where represent urban and residential areas. The performances were discussed and compared using the mean square error (MSE) and mean absolute percentage error (MAPE). The result shows that MLP models have outperformed ARIMA models where MLP with Levenberg-Marquardt algorithm outperformed the conjugate gradient method.
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spelling oai:generic.eprints.org:204692022-11-10T07:36:35Z http://journalarticle.ukm.my/20469/ Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting Cho, Kar Mun Nur Haizum Abd Rahman, Iszuanie Syafidza Che Ilias, Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP with both Levenberg-Marquardt algorithm and conjugate gradient method. These methods were used to predict the Air Pollutant Index (API) in Malaysia’s central region where represent urban and residential areas. The performances were discussed and compared using the mean square error (MSE) and mean absolute percentage error (MAPE). The result shows that MLP models have outperformed ARIMA models where MLP with Levenberg-Marquardt algorithm outperformed the conjugate gradient method. Penerbit Universiti Kebangsaan Malaysia 2022-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20469/1/23.pdf Cho, Kar Mun and Nur Haizum Abd Rahman, and Iszuanie Syafidza Che Ilias, (2022) Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting. Sains Malaysiana, 51 (8). pp. 2645-2654. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil8_2022/KandunganJilid51Bil8_2022.html
spellingShingle Cho, Kar Mun
Nur Haizum Abd Rahman,
Iszuanie Syafidza Che Ilias,
Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title_full Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title_fullStr Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title_full_unstemmed Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title_short Performance of Levenberg-Marquardt neural network algorithm in air quality forecasting
title_sort performance of levenberg-marquardt neural network algorithm in air quality forecasting
url http://journalarticle.ukm.my/20469/
http://journalarticle.ukm.my/20469/
http://journalarticle.ukm.my/20469/1/23.pdf