Open AccessAir Quality Index Prediction in Realtime Using SVM based model in Machine Learning
Author(s) -
Rajarshi SinhaRoy,
Swarnendu Sarkhel
Publication year - 2021
Publication title -
international journal of innovative research in physics
Language(s) - English
Resource type - Journals
eISSN - 2689-484X
pISSN - 2687-7902
DOI - 10.15864/ijiip.3106
Subject(s) - air quality index , index (typography) , support vector machine , air pollution index , quality (philosophy) , computer science , data mining , air pollution , machine learning , artificial intelligence , meteorology , geography , philosophy , epistemology , world wide web , chemistry , organic chemistry
The air quality index is an index to decide the situation of the air quality. The air quality index is a measure of how air pollutants impact a persons' fitness within a time period. It is a standardized degree this is used to suggest the pollutant (so2, no2, pm 2.5, pm 10, etc.) levels. We designed a model that could estimate the air quality index based totally on ancient records of a few preceding years. The performance of this model is progressed through making use of numerous Estimation-Problem logics. Our model could be able to correctly predict the air quality index of a complete county or any nation or any bounded area supplied with the ancient records of pollutant concentration. In our model by implementing a support-vector machine, we achieved better performance than other models and for that our model gets an accuracy of 96%. With the help of support-vector machine, our model estimates the air quality to predict the air quality index of a given location primarily based totally on its ancient records of the pollution of a few preceding years. Our purpose is to increase a non-linear updatable version for real-time air quality index forecasting, to doubtlessly update the models presently being used.