A Comparative Study on Air Quality Analysis and Prediction Using Machine Learning Techniques (original) (raw)

Review on air pollution of Delhi zone using machine learning algorithm

Journal of Air Pollution and Health, 2021

The issue of pollution in urban cities is a major problem these days especially in cities like the New Delhi is detected with more number of toxic gases in air, which has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2 , SO2 and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.

Air Quality Index Prediction using Machine Learning Techniques

Pollution is the most vital and disturbing issues faced in today's world. Above 2000 people die due to diseases whose root cause is pollution. Pollution can be of various forms and each of these types can have different effects on different people. Increased pollution levels are capable of causing mass destruction to the earth as well as to the species residing in it. One of the prevalent environmental challenges right now is air pollution. Air pollution has been noticeable as one of the most important problems of metropolitan regions around the globe, exclusively in Delhi, Beijing and Tehran where its inhabitants and administrators have long been struggling with air pollution impairment such as the health issues of its citizens. With the rapid development in the availability of data and computational technologies, various machine learning techniques have been proposed for predicting air pollution. Air Quality Index can be predicted using both Classification and Regression models. Decision tree, Linear Regression, Support Vector Regression and Random Forest Techniques are implemented in this proposed research work. From the above models, it is evident that, Random Forest Regression based model out performs better in the air quality index prediction with an accuracy of 98%.

Air Quality Prediction Based on Decision Tree Using Machine Learning

2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES), 2023

Air pollution has become a severe problem due to urbanization, industrialization, and the burning of fossil fuels, among other factors. This paper focuses on the use of data mining techniques for predicting air quality using machine learning. The paper highlights the impact of pollutants such as PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), CO (carbon monoxide), NOx (oxides of nitrogen), SO2 (Sulphur dioxide), and O3 (ozone) on human health, which include respiratory and cardiovascular diseases, asthma attacks, strokes, and even death. We propose using data mining and artificial intelligence techniques to solve the problem. Decision trees are used for classification and regression tasks and work by building a tree-like structure of decisions and their possible outcomes. The tree is constructed by recursively splitting the dataset based on the feature that provides the highest information gain or reduction in impurity until a stopping criterion is met. Decision trees are easy to understand and can handle both continuous and categorical features, making them a popular algorithm in machine learning. The paper also discusses the importance of data mining in machine learning and its ability to identify patterns and relationships that would have otherwise gone unnoticed. This paper offers a practical solution to predict air quality of Bengaluru for the next coming month by analyzing the data from the previous 1 year. This provides insights into the use of decision trees and data mining for solving complex problems.

Comparative Analysis of Machine Learning Regression Algorithms on Air Pollution Dataset

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. The main objective of this paper to build a model for predicting Air Quality Index(AQI) of the specific cities using various types of machine learning algorithms namely Multiple Linear Regression, K Nearest Neighbours(KNN), Support Vector Machine(SVM) and Decision Tree. And also evaluate and compare the performance of every algorithm based on their accuracy score and errors. Air ...

Prediction of Daily Air Pollutants Concentration and Air Pollutant Index Using Machine Learning Approach

Pertanika Journal of Science and Technology

The major air pollutants in Malaysia that contribute to air pollution are carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter. Predicting the air pollutants concentration can help the government to monitor air quality and provide awareness to the public. Therefore, this study aims to overcome the problem by predicting the air pollutants concentration for the next day. This study focuses on an industrial, the Petaling Jaya monitoring station in Selangor. The data is obtained from the Department of Environment, which contains the dataset from 2004 to 2018. Subsequently, this study is conducted to construct predictive modeling that can predict the air pollutants concentrations for the next day using a tree-based approach. From the comparison of the three models, a random forest is a best-proposed model. The results of PM10 concentration prediction for the random forest is the best performance which is shown by RMSE (15.7611–19.0153), NAE (0.6508–0.8216), an...

Prediction of Air Quality Index Using Supervised Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

The proposed system depicts various strategies utilized for forecast of Air Quality Index (AQI) utilizing supervised machine learning procedures. The system examines machine learning algorithm for air quality index by computing algorithm accuracy which will bring about the best precision. Moreover, the system exhibits different machine learning accuracy figures from the given dataset with assessment order report which recognizes the perplexity lattice. The outcome shows the adequacy of machine learning suggested calculation method that can be contrasted and best exactness with accuracy, Recall and F1 Score. The air pollution database contains data for each state of India. Four supervised machine learning algorithms, decision tree, random forest tree, Naïve Bayes theorem and K-nearest neighbor are compared and evaluated. ORGANIZATION OF THE THESIS In this thesis, Introduction and architecture of our system, the objective and the problem statement will be discussed in Chapter 1. The Review of Literature will be discussed in Chapter 2. Chapter 3 will consist of Work Done on various modules of the project. The System Design is explained in the Chapter 4. The Results obtained by usingvarious algorithms are mentioned in the Chapter 5. The Chapter 6 will consist of Conclusion. At last, we have mentioned the Appendix and the References. I.

Air Quality Prediction based on Supervised Machine Learning Methods

International Journal of Innovative Technology and Exploring Engineering, 2019

Generally, Air pollution alludes to the issue of toxins into the air that are harmful to human well being and the entire planet. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Subsequently, air quality assessment and prediction has turned into a significant research zone. The aim is to investigate machine learning based techniques for air quality prediction. The air quality dataset is preprocessed with respect to univariate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning/preparing. Then, air quality is predicted using supervised machine learning techniques like Logistic Regression, Random Forest, K-Nearest Neighbors, Decision Tree and Support Vector Machines. The performance of various machine learning algorithms is ...

Machine Learning based Prediction System for Detecting Air Pollution

2019

In today’s world, one of the most common problems we are facing worldwide is Air Pollution. Air of most cities is polluted these days and newer pollutants have also been added in the air rendering it more poisonous. Both human and natural activities can produce Air Pollution. Pollutants like Sulphur Oxides, Carbon Dioxide (CO2), Nitrogen Oxides, Carbon Monoxide (CO), Chlorofluorocarbon (CFC), Lead, Mercury etc. are being added in the air due to human activities. In this paper, data has been collected from the two sources in the Bengaluru region: government website and static sensors built using Arduino. The level of CO is measured using three machine algorithms namely Random Forest Regression (RFR), Decision Tree Regression (DTR) and Linear Regression (LR). The results show that RFR gives least error of the three and hence more accuracy. Command line interface has also been created to see the CO level prediction. Keywords— Air Pollution, Machine Learning, Carbon Monoxide, Random For...

Predictive Analysis of Air Pollution Using Machine Learning Techniques

Air pollution is a major source of worry for all living things. India has one of the world's highest levels of air pollution. Rising population, unplanned growth, increased automotive traffic, stubble burning, industrial waste, fossil fuel combustion, powerplant emissions and a variety of other causes all contribute considerably to air pollution in developing countries. Particulate matter (PM) 2.5 is the most concerning of all air pollutants since it causes major health problems in individuals. Prediction and management of air quality have therefore become critical. Several machine learning algorithms were used in this work to examine dataset results. The results of our work suggest that for future predictions, logistic regression and autoregression can be efficaciously utilised for the analysis and forecasting of levels of PM2.5 in the future. Countries can lower the prevalence of strokes, and chronic and acute respiratory illnesses such as asthma, and lung cancer by reducing air pollution levels.

Air Pollution Prediction Using Machine Learning Supervised Learning Approach

International Journal of Scientific & Technology Research, 2020

Due to human activities, industrialization and urbanization air is getting polluted. The major air pollutants are CO, NO, C6H6,etc. The concentration of air pollutants in ambient air is governed by the meteorological parameters such as atmospheric wind speed, wind direction, relative humidity, and temperature. Earlier techniques such as Probability, Statistics etc. were used to predict the quality of air, but those methods are very complex to predict, the Machine Learning (ML) is the better approach to predict the air quality. With the need to predict air relative humidity by considering various parameters such as CO, Tin oxide, nonmetallic hydrocarbons, Benzene, Titanium, NO, Tungsten, Indium oxide, Temperature etc, approach uses Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest Method (RF) to predict the Relative humidity of air and uses Root Mean Square Error to predict the accuracy.