Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3485
Title: An IoT Based Air Quality Monitoring with Deep Learning Model System
Authors: Srivastava, Harshit
Bansal, Kailash
Das, Santos Kumar
Sarkar, Santanu
Keywords: Internet of Things (IoT)
Raspberry Pi 3
Recurrent Neural Networks (RNN)
Long Short Term Memory (LSTM)
Air Quality Index (AQI)
Issue Date: Dec-2020
Citation: International Conference on Devices, Intelligent Systems & Communications (DISC), JBIET, Hyderabad, 09-10 January 2020
Abstract: Air pollution occurs when the environmental gases such as CO2, NH3 etc. concentration levels go above the optimum level. As the AQI is being calculated and as per the Central Pollution Control Board (CPCB), there is standard level of ranges for pollution level. This paper presents about monitoring the pollution level using Raspberry Pi 3 based on IoT technology. Here, the temperature, humidity, due point and wind speed parameters are also monitored and use of these parameters as data sets for prediction of pollution forecasting. Then, the target of this project is applying the deep learning concept for the prediction and analysis of gas sensors pollution level so that we can analyze the pollution level due to the pollutant gases based on pre-diction analysis. Various experiments being performed validation of the development of the system for real-time monitoring. Here, We are dis-cussing about the different methods used in deep learning i.e. Artificial Neural Networks (ANN), Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN) using LSTM model to analyze and predict the multivariate time series forecasting.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/3485
Appears in Collections:Conference Papers

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