Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model

Angga Vidianto; Achmad Sindunata; Novanto Yudistira

doi:10.1145/3479645.3479662

Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model

Angga Vidianto
, Achmad Sindunata
, Novanto Yudistira

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Air quality significantly affects human health and climate, good air quality and accurate air quality predictions have become one of the most widespread concerns. In previous studies, the datasets were used only in one of the major cities in the world. The dataset used in this study came from 7 different countries and used LSTM and VAR to predict. From several tests, LSTM can provide the best performance for predicting in the long term. The best RMSE value from LSTM is 22,227 by providing 0.0001 for learning rate, the number of neurons and epochs of 50 each, and tested on testing data within four months. Meanwhile, VAR gave the best results with an RMSE of 16,472 on the first month of testing data by giving a P-value is 24. The results show that the RSME value of the VAR is 36,802, the RSME value of the LSTM is 27,902.

Original language	English
Title of host publication	Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021
Publisher	Association for Computing Machinery
Pages	139-145
Number of pages	7
ISBN (Electronic)	9781450384070
DOIs	https://doi.org/10.1145/3479645.3479662
Publication status	Published - 13 Sept 2021
Event	6th International Conference on Sustainable Information Engineering and Technology, SIET 2021 - Virtual, Online, Indonesia Duration: 13 Sept 2021 → 14 Sept 2021

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Sustainable Information Engineering and Technology, SIET 2021
Country/Territory	Indonesia
City	Virtual, Online
Period	13/09/21 → 14/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

air pollution
lstm
pm2.5
var

Access to Document

10.1145/3479645.3479662

Cite this

Vidianto, A., Sindunata, A., & Yudistira, N. (2021). Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model. In Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021 (pp. 139-145). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3479645.3479662

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abstract = "Air quality significantly affects human health and climate, good air quality and accurate air quality predictions have become one of the most widespread concerns. In previous studies, the datasets were used only in one of the major cities in the world. The dataset used in this study came from 7 different countries and used LSTM and VAR to predict. From several tests, LSTM can provide the best performance for predicting in the long term. The best RMSE value from LSTM is 22,227 by providing 0.0001 for learning rate, the number of neurons and epochs of 50 each, and tested on testing data within four months. Meanwhile, VAR gave the best results with an RMSE of 16,472 on the first month of testing data by giving a P-value is 24. The results show that the RSME value of the VAR is 36,802, the RSME value of the LSTM is 27,902.",

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Vidianto, A, Sindunata, A & Yudistira, N 2021, Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model. in Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 139-145, 6th International Conference on Sustainable Information Engineering and Technology, SIET 2021, Virtual, Online, Indonesia, 13/09/21. https://doi.org/10.1145/3479645.3479662

Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model. / Vidianto, Angga; Sindunata, Achmad; Yudistira, Novanto.
Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. Association for Computing Machinery, 2021. p. 139-145 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sindunata, Achmad

AU - Yudistira, Novanto

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Y1 - 2021/9/13

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Vidianto A, Sindunata A, Yudistira N. Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model. In Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. Association for Computing Machinery. 2021. p. 139-145. (ACM International Conference Proceeding Series). doi: 10.1145/3479645.3479662

Air Pollution Particulate Matter (PM2.5) Forecasting using Long Short Term Memory Model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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