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Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model

Air quality conditions currently demands particular consideration, notably in Jakarta. As per the Air Quality Index (AQI) website, Jakarta ranks second globally for the poorest air quality, registering an AQI value of 170 (categorized as unhealthy). To address this challenge, forecasting emerges as...

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Main Author: Zidan Aqila Kamil, Mochamad
Format: Final Year Project
Language:English
Published: 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://utpedia.utp.edu.my/id/eprint/27001/1/21002801.pdf
http://utpedia.utp.edu.my/id/eprint/27001/
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spelling oai:utpedia.utp.edu.my:270012024-05-29T07:28:45Z http://utpedia.utp.edu.my/id/eprint/27001/ Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model Zidan Aqila Kamil, Mochamad QA75 Electronic computers. Computer science Air quality conditions currently demands particular consideration, notably in Jakarta. As per the Air Quality Index (AQI) website, Jakarta ranks second globally for the poorest air quality, registering an AQI value of 170 (categorized as unhealthy). To address this challenge, forecasting emerges as a potential solution. Forecasts serve as essential tools for policymakers and the public, enabling proactive measures to regulate and prevent future air quality issues. Among the methodologies available for forecasting, the Linear Regression model stands as one viable approach. The testing process was carried out using daily Index of Air Quality Standard (ISPU) DKI Jakarta data (1 March 2021 to 31 December 2021) obtained from the Satu Data Jakarta website, with 80% of the data as training data and 20% as test data. The parameters predicted by the Linear Regression model are the concentration values of the pollutants Particulate Matter 25 (PM25), Particulate Matter 10 (PM10), Sulphur Dioxide (SO2), Carbon Monoxide (CO), Ozone (O3), and Nitrogen Dioxide (NO2), with evaluation using the Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE) metrics. Overall, the results of forecasting pollutant parameters using the Linear Regression model obtained good accuracy. Very accurate results (MAPE < 10%) were obtained by the SO2 parameter. Then accurate results (MAPE 11% - 20%) were obtained by the O3 parameter. The rest got fairly accurate results (MAPE 21% - 50%) obtained by the parameters PM2.5, PM10, CO and NO2. Apart from that, visualisation of forecasting results is presented in the form of a website, along with the Air Quality Index (AQI) value, parameter value, and AQI category. 2024-01 Final Year Project NonPeerReviewed text en http://utpedia.utp.edu.my/id/eprint/27001/1/21002801.pdf Zidan Aqila Kamil, Mochamad (2024) Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model. [Final Year Project] (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zidan Aqila Kamil, Mochamad
Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
description Air quality conditions currently demands particular consideration, notably in Jakarta. As per the Air Quality Index (AQI) website, Jakarta ranks second globally for the poorest air quality, registering an AQI value of 170 (categorized as unhealthy). To address this challenge, forecasting emerges as a potential solution. Forecasts serve as essential tools for policymakers and the public, enabling proactive measures to regulate and prevent future air quality issues. Among the methodologies available for forecasting, the Linear Regression model stands as one viable approach. The testing process was carried out using daily Index of Air Quality Standard (ISPU) DKI Jakarta data (1 March 2021 to 31 December 2021) obtained from the Satu Data Jakarta website, with 80% of the data as training data and 20% as test data. The parameters predicted by the Linear Regression model are the concentration values of the pollutants Particulate Matter 25 (PM25), Particulate Matter 10 (PM10), Sulphur Dioxide (SO2), Carbon Monoxide (CO), Ozone (O3), and Nitrogen Dioxide (NO2), with evaluation using the Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE) metrics. Overall, the results of forecasting pollutant parameters using the Linear Regression model obtained good accuracy. Very accurate results (MAPE < 10%) were obtained by the SO2 parameter. Then accurate results (MAPE 11% - 20%) were obtained by the O3 parameter. The rest got fairly accurate results (MAPE 21% - 50%) obtained by the parameters PM2.5, PM10, CO and NO2. Apart from that, visualisation of forecasting results is presented in the form of a website, along with the Air Quality Index (AQI) value, parameter value, and AQI category.
format Final Year Project
author Zidan Aqila Kamil, Mochamad
author_facet Zidan Aqila Kamil, Mochamad
author_sort Zidan Aqila Kamil, Mochamad
title Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
title_short Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
title_full Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
title_fullStr Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
title_full_unstemmed Forecasting the Air Quality Index (AQI) in Jakarta, Indonesia by Using a Linear Regression Model
title_sort forecasting the air quality index (aqi) in jakarta, indonesia by using a linear regression model
publishDate 2024
url http://utpedia.utp.edu.my/id/eprint/27001/1/21002801.pdf
http://utpedia.utp.edu.my/id/eprint/27001/
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score 13.211869

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