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Urban Air Pollution Monitoring System with Forecasting Model


Roshni A. Jeswani , Anjuman college of engineering and technology, Sadar, Nagpur; Prof. M.T. Hasan, Anjuman college of engineering and technology,Assisant Professor


Neural Network, Forecasting, Air Quality Index (AQI), Respirable Suspended Particulate Matter (RSPM), Machine Learning


In this paper a system for monitoring and forecasting urban air pollution is presented. The system uses low-cost air-quality monitoring motes. The model are prepared form receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information. The focus of this paper is on the monitoring system and its forecasting model. Machine learning algorithm are used to build accurate forecasting models for concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), Respirable Suspended Particulate Matter (RSPM), Air quality index (AQI). This paper focus on the prediction possibilities in urban air pollution monitoring system by using neural network using MATLAB program. A system for monitoring and forecasting urban air pollution was presented. The data was collected from government owned installations i.e. Maharashtra Pollution Control Board. This data was then subjected to intelligent processing and the ML algorithm was designed to present a model which is able to forecast. It is intended to develop a system which is able to forecast concentration values of sensors some hours ahead. The outcome of this paper can be useful for alarming applications in areas with high air pollution levels.

Other Details

Paper ID: IJSRDV6I40930
Published in: Volume : 6, Issue : 4
Publication Date: 01/07/2018
Page(s): 1213-1217

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