Advancing Air Quality Prediction in Specific Cities Using Machine Learning

Authors

  • A. Deepak Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu
  • Amrapali S. Chavan Assistant Professor, Department of Computer Engineering, AISSMS Institute of Information Technology, Pune
  • Aniruddha Bodhankar Department of Decision Sciences, Dr. Ambedkar Institute of Management Studies and Research, Nagpur, Maharashtra
  • L. Sherly Puspha Annabel Professor, Department of Information Technology, St. Joseph’s College of Engineering, Chennai, Tamilnadu, India
  • Nimmalaharathi Assistant Professor, EIE, School of Engineering, Sree Vidyanikethan Engineering College, AP
  • A. Vanathi Associate Professor, Department of CSE, Aditya Engineering College(A), Surampalem, Kakinada, Andra Pradesh

Keywords:

Accuracy, Air pollution, Detection, Machine learning, Prediction, Recommendation

Abstract

The project aims to ensure optimal air quality in targeted urban areas by employing a sophisticated air quality monitoring system that collects data on contaminants from various locations. The release of hazardous gasses from industrial activities and the increasing emissions from vehicles have made air pollution a critical environmental and public health concern. Pollutants like particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and others, accumulate in the atmosphere, causing a deterioration in air quality and posing serious risks to both human health and the environment. The impact of air pollution is especially pronounced in major cities worldwide, where the concentration of industries and transportation systems worsens the problem. These urban areas often experience pollution levels that exceed the air quality standards set by governments, exposing residents to a harmful mixture of pollutants. By leveraging pre-collected data and employing the XG Boost algorithm, the ML technology calculates the Air Quality Index, thereby contributing to improved air quality management and its impact on public health.

Downloads

Download data is not yet available.

References

Mrs. A. GnanaSoundariMtech, (Phd) ,Mrs. J. GnanaJeslin M.E, (Phd), Akshaya A.C. “Indian Air Quality Prediction And Analysis Using Machine Learning”. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 11, 2019 (Special Issue)

Suhasini V. Kottur , Dr. S. S. Mantha. “An Integrated Model Using Artificial Neural Network(Ann) And Kriging For Forecasting Air Pollutants Using Meteorological Data”. International Journal of Advanced Research in Computer and Communication Engineering ISSN (Online) : 2278-1021 ISSN (Print) : 2319-5940 Vol. 4, Issue 1, January 2015

RuchiRaturi, Dr. J.R. Prasad .“Recognition Of Future Air Quality Index Using Artificial Neural Network”.International Research Journal of Engineering and Technology (IRJET) .e-ISSN: 2395-0056 p-ISSN: 2395-0072 Volume: 05 Issue: 03 Mar-2018

Aditya C R, Chandana R Deshmukh, Nayana D K, Praveen Gandhi Vidyavastu .” Detection and Prediction of Air Pollution using Machine Learning Models”. International Journal of Engineering Trends and Technology (IJETT) – volume 59 Issue 4 – May 2018

Gaganjot Kaur Kang, Jerry ZeyuGao, Sen Chiao, Shengqiang Lu, and Gang Xie.” Air Quality Prediction: Big Data and Machine Learning Approaches”. International Journal of Environmental Science and Development, Vol. 9, No. 1, January 2018

Alade IO, Rahman MAA, Saleh TA (2019a) Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Sol Energy 183:74–82.

Alade IO, Rahman MAA, Saleh TA (2019b) Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model. Nano-Struct Nano-Objects 17:103–111.

Al-Jamimi HA, Saleh TA (2019) Transparent predictive modelling of catalytic hydrodesulfurization using an interval type-2 fuzzy logic. J Clean Prod 231:1079–1088.

Al-Jamimi HA, Al-Azani S, Saleh TA (2018) Supervised machine learning techniques in the desulfurization of oil products for environmental protection: a review. Process Saf Environ Prot 120:57–71.

Al-Jamimi HA, Bagudu A, Saleh TA (2019) An intelligent approach for the modeling and experimental optimization of molecular hydrodesulfurization over AlMoCoBi catalyst. J Mol Liq 278:376–384.

Ayturan YA, Ayturan ZC, Altun HO, Kongoli C, Tuncez FD, Dursun S, Ozturk A (2020) Short-term prediction of PM2.5 pollution with deep learning methods. Global NEST J 22(1):126–131

Bellinger C, Jabbar MSM, Zaïane O, Osornio-Vargas A (2017) A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health.

Bhalgat P, Bhoite S, Pitare S (2019) Air Quality Prediction using Machine Learning Algorithms. Int J Comput Appl Technol Res 8(9):367–370.

K Murali Krishna, Amit Jain, Hardeep Singh Kang, Mithra Venkatesan, Anurag Shrivastava, Sitesh Kumar Singh, Muhammad Arif, Development of the Broadband Multilayer Absorption Materials with Genetic Algorithm up to 8 GHz Frequency, Security and Communication Networks

P Gite, A Shrivastava, KM Krishna, GH Kusumadev, Under water motion tracking and monitoring using wireless sensor network and Machine learning, Materials Today: Proceedings, Volume 80, Part 3, 2023, Pages 3511-3516

Anurag Shrivastava, Midhun Chakkaravathy, Mohd Asif Shah, A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches’, Cybernetics and Systems, Taylor & Francis

Mukesh Patidar, Anurag Shrivastava, Shahajan Miah, Yogendra Kumar, Arun Kumar Sivaraman, An energy efficient high-speed quantum-dot based full adder design and parity gate for nano application, Materials Today: Proceedings, Volume 62, Part 7, 2022, Pages 4880-4890

Castelli M, Clemente FM, Popoviˇc A, Silva S, Vanneschi L (2020) A machine learning approach to predict air quality in California. Complexity 2020(8049504):1–23.

Doreswamy HKS, Yogesh KM, Gad I (2020) Forecasting Air pollution particulate matter (PM2.5) using machine learning regression models. Procedia Comput Sci 171:2057–2066.

Fahad S, Sönmez O, Saud S, Wang D, Wu C, Adnan M, Turan, V (2021a) Plant growth regulators for climate-smart agriculture (1st ed.). CRC Press.

Fahad, S, Sönmez O, Saud S, Wang D, Wu C, Adnan M, Turan V (2021b) Sustainable soil and land management and climate change (1st ed.). CRC Press.

Liang Y, Maimury Y, Chen AH, Josue RCJ (2020) Machine learning-based prediction of air quality. Appl Sci 10(9151):1–17.

Madan T, Sagar S, Virmani D (2020) Air quality prediction using machine learning algorithms–a review. In: 2nd international conference on advances in computing, communication control and networking (ICACCCN) pp 140–145.

Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal & Abhay Chaturvedi (2023): IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area, Cybernetics and Systems, DOI: 10.1080/01969722.2023.2166243

P. William, A. Shrivastava, H. Chauhan, P. Nagpal, V. K. T. N and P. Singh, "Framework for Intelligent Smart City Deployment via Artificial Intelligence Software Networking," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 455-460, doi: 10.1109/ICIEM54221.2022.9853119.

Madhuri VM, Samyama GGH, Kamalapurkar S (2020) Air pollution prediction using machine learning supervised learning approach. Int J Sci Technol Res 9(4):118–123

Velarde-Molina, J. F. ., Ancco, J. A. ., Rosado, M. B. ., Huamaní, E. L. ., Paico Campos, M. M. ., Meneses-Claudio, B. ., & Benancio, L. O. . (2023). Development of Mobile Application to Report Criminal Acts in Young Areas of Peru. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 215–218. https://doi.org/10.17762/ijritcc.v11i4.6403

Jacobs, M., Georgiev, I., Đorđević, S., Oliveira, F., & Sánchez, F. Efficient Clustering Algorithms for Big Data Analytics. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/138

Anupong, W., Azhagumurugan, R., Sahay, K. B., Dhabliya, D., Kumar, R., & Vijendra Babu, D. (2022). Towards a high precision in AMI-based smart meters and new technologies in the smart grid. Sustainable Computing: Informatics and Systems, 35 doi:10.1016/j.suscom.2022.100690

Downloads

Published

30.08.2023

How to Cite

Deepak, A. ., Chavan, A. S. ., Bodhankar, A. ., Annabel, L. S. P. ., Nimmalaharathi, & Vanathi, A. . (2023). Advancing Air Quality Prediction in Specific Cities Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 309–317. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3474

Issue

Section

Research Article

Most read articles by the same author(s)

<< < 1 2 3 4 5 > >>