Citation :

R. Sudha Abirami, M.S. Irfan Ahmed, "Maximum Normalized Orthogonal Projective Extreme Learning Machine for Green Energy Resource Data Analytics on Sustainable Environmental Air Pollution," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 118-127, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P108

Abstract

Air pollution is a global environmental issues with severe threats and damage to human health and ecosystems. Air pollutants like Particulate Matter (𝑃𝑀2.5), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3) has been linked to adverse effects on the environment, including climate change and greenhouse gas emissions. Many researchers carried out their research on machine learning and artificial intelligence techniques for predicting environmental air pollution and for increasing the air quality. But the researcher’s faces the problem of meeting the growing energy demand with minimum greenhouse gas emission and environmental vulnerability. Therefore, a large amount of air quality data is used for improving green energy efficiency. In this paper, the Maximum Normalized Orthogonal Lemma Projective Extreme Learning Machine Classification (MNOLPELMC) Method is introduced for sustainable environmental air pollution prediction with high accuracy and less time complexity. An experimental evaluation of the MNOLPELMC method is carried out with performance metrics such as prediction accuracy, precision, recall, root mean square error, and prediction time.

Keywords

Green energy resource data analytics, Sustainable environmental pollution, Extreme Machine Learning Model, Grubbs maximum normalized residual test, Random orthogonal lemma projection process.

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