Development of Machine Learning models for air pollutants using weka

Abstract

All over the world desecration is a major factor for livelihood and plantation. There are various types of desecrations and major are air desecration ( or desecration), water desecration or water desecration, Industrial desecration, noise desecration, urban desecration etc. In India Act 1981 prescribes various functions for the Central Desecration Control Board (CPCB) at the open level and state desecration control board at the state level about air desecration. Act 1974 gives about water prevention and control desecration. Act 1986 section 2(a) explains noise desecration under rules, 2000. In this paper we are collected data of air pollutants such as particulate matter of 2.5 microns or less. particulate matter of 10 microns or less, Nitrogen Monoxide, Nitrogen dioxide, chemical compound of oxygen and nitrogen, Azane or Ammonia, Carbon Monoxide, Sulfur dioxide Ozone,Benzene, Toluene, Xylene, AQI, AQI_Bucket. For these variables we are fitted Machine Learning models such as Naive Bayes Classifier, CV Parameter Selection, K-nearest neighbours, J48 and Hyper Pipes using WEKA software. Detailed accuracy for models like TP ratio, FP ratio, precision, F-measure, ROC area are estimated. Using Root average square error, Average absolute error, we examine which is the best model.