Document Type : ORIGINAL RESEARCH PAPER

Author

Department of System Analysis and Decision Making, Ural Federal University, Ekaterinburg, Russian Federation

Abstract

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and regression tree using M5 algorithm. The prediction of Sulphur dioxide was based on atmospheric pollutants and meteorological parameters. While, the model performance was assessed by using four evaluation measures namely Correlation coefficient, mean absolute error, root mean squared error and relative absolute error. The results obtained suggest that 1) homogenous ensemble classifier random forest performs better than single base statistical and machine learning algorithms; 2) employing single base classifiers within bagging as base classifier improves their prediction accuracy; and 3) heterogeneous ensemble algorithm voting have the capability to match or perform better than homogenous classifiers (random forest and bagging). In general, it demonstrates that the performance of ensemble classifiers random forest, bagging and voting can outperform single base traditional statistical and machine learning algorithms such as linear regression, support vector machine for regression and multilayer perceptron to model the atmospheric concentration of sulphur dioxide.

Graphical Abstract

Application of ensemble learning techniques to model the atmospheric concentration of SO2

Highlights

  • Random Forest algorithm performs better than traditional independent learners  MLP and SVM;
  • Adopting single base classifiers such as SVM, MLP, etc. within Bagging (homogenous ensemble learning classifier) enhances their prediction accuracy;
  • Voting – a heterogeneous ensemble learning classifier has the characteristics of predicting SO2 concentration with high accuracy and low error values.

Keywords

Main Subjects

Bedoui, S.; Gomri, S.; Samet, H.; Kachouri, A., (2016). A prediction distribution of atmospheric pollutants using support vector machines, discriminant analysis and mapping tools (Case study: Tunisia). Pollution, 2: 11-23 (13 pages).

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.

CAPTCHA Image