Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Raniyah Wazirali,Rami Ahmad

doi:10.32604/cmc.2022.021477

Abstract

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Highlights

The World Health Organization reported that’s even million people are affected by air pollution
The experimental analysis of the proposed smart city air pollution prediction environment has been tested using the data from the KDD cup 2018, which contains the name of 35 stations with air pollutant concentrations, such as PM2.5, PM10, NO2, carbon monoxide (CO), O, and SO2
A smart city air pollution prediction system using ML and DL methods is proposed in this study

Summary

Introduction

The World Health Organization reported that’s even million people are affected by air pollution. Increasing population, transportation, and reliance can raise energy, which provides additional industries and vehicles to the cities This phenomenon can increase the sources of pollution emissions, which must be addressed by local and national authorities. The experimental result with comparative analysis proves that the proposed deep evolutionary algorithm obtains high accuracy on predicting air pollutants with minimal error rate. This approach can reduce the sensor cost placed on unnecessary areas. The proposed approach will efficiently and effectively predict air pollutants and sensor placements and produce a smart city with an efficient air quality monitoring system.

Related Work

Proposed Gaussian SVM Methodology

Classification of Air-Polluted Area Using Gaussian SVM

Results and Discussions

Conclusion