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There are several regression models for the rainfall in the literature. Some of them are linear regression model, multiple regression model, piece wise regression model, Poisson regression model, negative binomial regression model, etc. Various piece wise regression models are linear piece wise regression, polynomial piece wise regression, cubic piece wise regression, quadratic piece wise regression, etc. This paper proposes three piece wise regression models using exponential smoothing models, i.e, simple exponential smoothing spline, holt’s exponential spline, and damped exponential spline for annual rainfall data from 1970 to 2017 with two knots at 1990 and 2005 with three subdivisions. Symmetric mean average percentage error is used as an accuracy measure for choosing the best model among the proposed three exponential spline models.

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.