Support Vector Machine can also be used as a regression method, maintaining all the main features that characterize the algorithm (maximal margin). The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. First of all, because output is a real number it becomes very difficult to predict the information at hand, which has infinite possibilities.
Published in Chapter:
Analyzing AQI before Covid '19: Experimental Study of 3 Years for Intelligent Environment Conducted at North Indian Zone to Extract Knowledge
Copyright: © 2024
|Pages: 15
DOI: 10.4018/979-8-3693-2109-6.ch017
Abstract
In the populated and developing countries, governments consider the regulation and protection of environment as a major task and should take into consideration the concept of smart environment monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, data sets, and other devices connected across the globe through a network. This system can help in monitoring air quality. Also, these factors contribute a lot to air pollution. So, forecasting air quality index using an intelligent environment system includes a machine learning model to predict air quality index for NCR (National Capital Region). The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3. The authors have implemented different machine learning algorithms of classification and regression techniques. To make their prediction more accurate, mean square error, mean absolute error, and R square errors have been considered. The chapter helps to frame a structured view of air quality prediction methods in the reader's mind and also gives suggestions for other prediction methods as well. The real challenge is to decide which method will be applied in predicting air quality. Hence, it is important to test and use all these methods.