Open AccessDeep Learning Model for Air Quality Prediction Based on Big Data
Author(s) -
P. Parkavi,
Swaraj Rathi
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit217332
Subject(s) - computer science , air quality index , artificial intelligence , deep learning , machine learning , the internet , air pollution , big data , quality (philosophy) , predictive modelling , harm , data mining , internet of things , measure (data warehouse) , data science , computer security , world wide web , meteorology , philosophy , chemistry , physics , organic chemistry , epistemology , political science , law
Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.