Prediction of Air Pollution Using Random Forest

EasyChair Preprint 5632

Abstract

The aim of this project is to use a heterogeneous ensemble of differential evolution with random forest method for air pollution prediction in New Delhi. This is different from existing work (independent classifier of Bayesian network and multi-label classifier used for the estimation of air pollutants) as a method is proposing to combine state-of-the-art differential evolution strategies with random forest method instead of focusing on existing single technique. When the existing approach i.e. independent and multi-label classifiers are compared with proposed approach, it shows proposed approach leads to the performance gains. Continuous ambient air quality data of two cities Delhi and Patna from Central Pollution Control Board were publicaly made available, from where seven pollutants (C6H6, NO2, O3, SO2, CO, PM2.5 and PM10) dataset are collected with daily average concentration. . Air pollution monitoring, is thus, becoming more and more significant. Real-time air quality information, such as concentration of PM2.5, PM10, and, NO2, is important aspect for pollution management and protecting human beings from the damages caused by air pollutants.

Keyphrases: Air quality prediction, Meteorology Data, Point of Interest, Random Forest, Traffic and road data

@booklet{EasyChair:5632,
  author    = {Sahil Singh and Ayush Yadav and Akhilesh Kumar},
  title     = {Prediction of Air Pollution Using Random Forest},
  howpublished = {EasyChair Preprint 5632},
  year      = {EasyChair, 2021}}