Asha B. Chelani

  • Air Pollution Control Board, National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur – 440020, India

Received: September 29, 2014
Revised: January 8, 2015
Accepted: January 14, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2014.09.0229  

  • Download: PDF


Cite this article:
Chelani, A.B (2015). Nearest Neighbour Based Forecast Model for PM10 Forecasting: Individual and Combination Forecasting. Aerosol Air Qual. Res. 15: 1130-1136. https://doi.org/10.4209/aaqr.2014.09.0229


HIGHLIGHTS

  • Comparison of various functions of nearest neighbours for PM10 forecasting.
  • Kernel regression of nearest neighbours outperforms the other individual models.
  • Due to linear and nonlinear patterns in data, combination forecasting is suggested.
  • Outperformance of combination forecasting over individual forecast models.
  • Study is useful when the data on predictor variables is not available.

 

ABSTRACT


Air quality forecasting using nearest neighbour technique provides an alternative to statistical and neural network models, which needs the information on predictor variables and understanding of underlying patterns in the data. k-nearest neighbour method of forecasting that does not assume any linear or nonlinear form of the data is used in this study to obtain the next step forecast of PM10 concentrations. Various function approximation techniques such as mean, median, linear combination and kernel regression of nearest neighbours are evaluated. It is observed that kernel regression of nearest neighbours outperforms the other individual models including bench mark persistence model for obtaining the next step forecasts. As the data may involve both linear and nonlinear patterns and any individual model cannot capture both types of patterns, combination forecasting is suggested as an alternative. The forecast error showed the outperformance of combination forecasting over individual forecast, which is quite obvious as it assigns more weightage to the model with minimum error. The study is useful when the data on predictor variables that influence the air pollutant concentrations is not available. The assumption on the underlying distribution of the data is also not required for the approach.


Keywords: Time series forecasting; Nearest neighbours; PM10 concentration; Combination forecasting


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

The Future Environment and Role of Multiple Air Pollutants

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.