A Smart City Air Pollution Prediction System Using Machine Learning-Reference-Cited by-同舟云学术

A Smart City Air Pollution Prediction System Using Machine Learning

Published:2023-03-29 Issue: Volume: Page:
ISSN:
Container-title:
language:
Short-container-title:

Author:

Mathur Mitali¹,Tawar Aman¹,Verma Indu¹

Affiliation:

1. SRM Institute of Science and Technology Delhi-NCR

Abstract

Abstract Air pollution is one of the main cause of infections to human health. According to World Health Organization, 15 million people are at health risk due to air pollution every year and numbers are increasing every day because of excess use of coal and petroleum which has increased the interest in air pollution and its impacts among the scientific community. We are going to create and a model that might help us in predicting air quality. There we have a large database provided for the analysis and modelling. On the basis of this we will study all the important features like which concentration of benzene, NO2, CO2 etc. in the air and using all these categories we will determine the air quality. The tools used in this are Machine Learning Algorithms, Python, Feature Engineering, Pandas, NumPy, Seaborn, Flask, HTML, CSS etc. This model can be used by several government organizations and can help them in making the right decisions related to approval or rejection of any industrial project to control pollution level of our country by using Machine Learning algorithms.

Publisher

Research Square Platform LLC

Reference22 articles.

1. Verma I, Ahuja R. Hardik Meisheri, and LipikaDey. ”Air pollutant severity rediction using Bi-directional LSTM Network.” In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 651–654. IEEE, 2018.

2. Ayele. TemeseganWalelign, and RutvikMehta.”Air pollution monitoring and prediction using IoT.” In 2018 Second International Conference on Inventive Communication 6 Fig.

3. RH w.r.t Temperature Fig. 13. RH w.r.t CO and Computational Technologies (ICICCT), pp.1741–1745.IEEE,2018.

4. Comparative analysis of machine learning techniques for predicting air quality in smart cities;Ameer S;IEEE Access,2019

5. Djebbri N, MouniraRouainia. ”Artificial neural networksbased air pollution monitoring inindustrial sites.” In 2017 International Conference on Engineering and Technology (ICET), pp. 1–5. IEEE,2017.