Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Pant Alka; Chandra Joshi Ramesh; SHARMA SANJAY; Pant Kamal

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: AVICENNA JOURNAL OF ENVIRONMENTAL HEALTH ENGINEERING Year:2023 | Volume:10 | Issue:1 Page(s): 38-43

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis

Author(s)

Pant Alka | Chandra Joshi Ramesh | SHARMA SANJAY | Pant Kamal | Issue Writer Certificate

Keywords

Air quality index

Time series analysis

Auto-correlation

Seasonal ARMA

Forecasting

Abstract

Air pollution is a widespread problem in India. The study focuses on forecasting the air quality index (AQI) using time series modeling techniques for the most polluted area of Dehradun City in Uttarakhand state, India. The train test approach of machine learning and Akaike information criterion (AIC) have been used on the monthly data of five years to select the best auto-regressive model. Using the auto-correlation functions (ACF and PACF) and the seasonality component in the time-series dataset, a seasonal autoregressive moving average (ARMA) model with its minimum AIC has been chosen to forecast the AQI. This model is also validated by comparing its predicted values with the actual values of AQI. The results showed that the seasonal ARMA model of (1, 0, 0)(1, 0, 0)12 could forecast AQI based on a stationary dataset. The research also indicates that the asthma patients of the Himalayan Drugs-ISBT region may experience more health effects, especially in winter, due to poor air quality. The model can be helpful for a scientist and the government to take precautionary measures in advance.

Multimedia

No record.

Cites

No record.

References

Cite

APA: Copy

Pant, Alka, Chandra Joshi, Ramesh, SHARMA, SANJAY, & Pant, Kamal. (2023). Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis. AVICENNA JOURNAL OF ENVIRONMENTAL HEALTH ENGINEERING, 10(1), 38-43. SID. https://sid.ir/paper/1080080/en

Vancouver: Copy

Pant Alka, Chandra Joshi Ramesh, SHARMA SANJAY, Pant Kamal. Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis. AVICENNA JOURNAL OF ENVIRONMENTAL HEALTH ENGINEERING[Internet]. 2023;10(1):38-43. Available from: https://sid.ir/paper/1080080/en

IEEE: Copy

Alka Pant, Ramesh Chandra Joshi, SANJAY SHARMA, and Kamal Pant, “Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis,” AVICENNA JOURNAL OF ENVIRONMENTAL HEALTH ENGINEERING, vol. 10, no. 1, pp. 38–43, 2023, [Online]. Available: https://sid.ir/paper/1080080/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops