Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

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