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Analyzing AQI before Covid '19: Experimental Study of 3 Years for Intelligent Environment Conducted at North Indian Zone to Extract Knowledge
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Author(s): Rohit Rastogi (Department of CSE, ABES Engineering College, Ghaziabad, India), Sheelu Sagar (Amity University, Noida, India)and Neeti Tandon (Vikram University, Ujjain, India)
Copyright: 2024
Pages: 15
Source title:
Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT)
Source Author(s)/Editor(s): Ahdi Hassan (Global Institute for Research Education and Scholarship, The Netherlands), Pronaya Bhattacharya (Amity University, Kolkata, India), Subrata Tikadar (Amity University, Kolkata, India), Pushan Kumar Dutta (Amity University, Kolkata, India)and Martin Sagayam (Karunya Institute of Technology and Sciences, India)
DOI: 10.4018/979-8-3693-2109-6.ch017
PurchaseView on the publisher's website for pricing and purchasing information.
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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.
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