Analysis and Prediction for Air Quality Using Various Machine Learning Models

• Autorzy: Dao, To-Hieu , Dieu, Vu Hoang , Nhat, Hoang Van , Thu, Nguyen Thi , Tran, Duc-Tan , Trung, Hoang Quang , Tran, Duc-Nghia
• Języki publikacji: EN

Abstrakty

EN

Air pollution has been a concern in recent years. Measuring the extent of pollution is important to know about the air quality. Previous research has used machine learning algorithms to forecast the Air Quality Index (AQI) in specific locations. Even though that research achieved quite reliable results, they still have some drawbacks that need to be taken into consideration, such as low accuracy or lack of data analysis.On a public dataset, we used Random Forest, XGBoost, and Neural Network to build a machine learning model for the purpose of making predictions about the air quality index (AQI) in a number of cities located in India. The performances of these models were evaluated by using their score errors, Root Mean Square Error (RMSE), and Coefficient Of Determination (R2). This paper demonstrates the analysis of air pollutants from the dataset, which is an effective way to enhance the model's performance.

Słowa kluczowe

EN

machine learning; AQI; data analysis

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 89--94
• Opis fizyczny: Bibliogr. 24 poz., wykr., tab.

Twórcy

autor

Dao, To-Hieu

• EEE, Phenikaa University,
• GU of Science & Technology Ha Noi, Viet Nam

autor

Dieu, Vu Hoang

• EEE, Phenikaa University,
• GU of Science & Technology Ha Noi, Viet Nam

autor

Nhat, Hoang Van

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam,

autor

Thu, Nguyen Thi

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam,

autor

Tran, Duc-Tan

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam,

autor

Trung, Hoang Quang

• Electrical & Electronic Engineering Phenikaa University Ha Noi, Viet Nam,

autor

Tran, Duc-Nghia

• Institute of Information Technology Viet Nam Academy of Science & Technology Ha Noi, Viet Nam,

Bibliografia

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Identyfikatory

DOI

10.15439/2022R03