Ambient Air Pollution Forecasting System using Deep Neural Networks
Geethika Jujjavarapu1, Siddhartha Duggirala2, Anulekha Kavutarapu3, Ravikishan Surapaneni4
1Geethika Jujjavarapu , Computer Science program at VR Siddhartha Engineering College. affiliated to JNTUK University, Kakinada.
2Siddhartha Duggirala, Bachelor of Technology in Computer Science and Engineering at VR Siddhartha Engineering College afflicted to JNTUK University, Kakinada.
3Anulekha Kavutarapu , B.Tech final year in Computer Science and Engineering at VR Siddhartha Engineering College affiliated to JNTUK University, Kakinada.
4Mr. Ravikishan Surapaneni , Associate Professor, CSE department at VR Siddhartha Engineering College.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4161-4165 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9425038620/2020©BEIESP | DOI: 10.35940/ijrte.F9425.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Air pollution is a major problem that has been recognized throughout the world. Harmful impacts of air contamination include hypersensitive reactions such as throat irritation, itchy eyes, nose, and some other serious problems. In recent years, the number of fatalities occurred due to air pollution has been increasing dramatically. In this paper, various air pollutants such as Carbon Monoxide, Methane or natural gas, LPG, and air quality at different places of city are measured using sensors. Further, the detected values are then used in the prediction of future values. The evolution of deep neural networks and Internet of Things made this possible to detect and forecast the concentration of pollutants underlying in the air. We use a special module called pyFirmata firmware which is used to connect the Arduino with python and upload the data into csv file on Jupyter Notebook. Here, the data collected is univariate i.e. it varies with only time. Though there are many statistical models to predict time series datasets such as ARIMA, their efficiency is low. Deep Neural Networks works well for predicting univariate as well as time series datasets. Hence, the Keras sequential model is employed to predict the hourly future values of air pollutants based on previous readings. The final results of prediction are compared with the actual values and error is calculated. As a result, the level of air pollutants at a particular hour can be predicted. The concentration of air pollutants in coming years, month or week helps us to reduce its concentration to lesser than the harmful or toxic range.
Keywords: Air Pollution, Deep Neural Networks, Keras Sequential Model, pyFirmata, Univariate Data.
Scope of the Article: Deep learning.