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
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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.


  • 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.



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

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