scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

Air Quality Prediction Model using Supervised Machine Learning Algorithms

2020 ◽  
pp. 190-201
Author(s):  
Suprateek Halsana
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Learning Algorithms ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Data Set ◽  
Air Quality Prediction ◽  
The Government ◽  
Living Organisms

<p>Air pollution is the “world’s largest environmental health threat”[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index ) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10,S02, N02, C0 as well as weather conditions like temperature, pressure and humidity[2].Hence the data set is combined from various web sources like cpcb.nic.in and uci repository in order to bring accuracy in the prediction and to justify whether the Quality of air is suitable or not. This prediction will be brought about with the help of some supervised machine learning algorithms and the observation and the result will state which algorithm is giving better accuracy in prediction of AQI and which one is giving less error.</p>

scholarly journals Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2401 ◽  
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Learning Technologies ◽  
Sensor Data ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Air Pollution Prediction ◽  
Pollution Prediction

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

scholarly journals A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

2017 ◽  
Author(s):  
Dixian Zhu ◽  
Changjie Cai ◽  
Tianbao Yang ◽  
Xun Zhou
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Small Scale ◽  
Quality Prediction ◽  
Pollution Concentration ◽  
Task Learning ◽  
Air Quality Prediction ◽  
Standard Regression

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

scholarly journals Prediction and Analysis of Pollutant using Supervised Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 50-54
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Prediction Model ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Air Quality Prediction

Air is the most essential natural resource for the survival of humans, animals, and plants on the planet. Air is polluted due to the burning of fuels, exhaust gases from factories and industries, and mining operations. Now, air pollution becomes the most dangerous pollution that humanity ever faced. This causes many health effects on humans like respiratory, lung, and skin diseases, which also causes effects on plants, and animals to survive. Hence, air quality prediction and evaluation as becoming an important research area. In this paper, a machine learning-based prediction model is constructed for air quality forecasting. This model will help us to find the major pollutant present in the location along with the causes and sources of that particular pollutant. Air Quality Index value for India is used to predict air quality. The data is collected from various places throughout India so that the collected data is preprocessed to recover from null values, missing values, and duplicate values. The dataset is trained and tested with various machine learning algorithms like Logistic Regression, Naïve Bayes Classification, Random Forest, Support Vector Machine, K Nearest Neighbor, and Decision Tree algorithm in order to find the performance measurement of the above-mentioned algorithms. From this, the prediction model is constructed using the Decision Tree algorithm to predict the air quality, because it provides the best and highest accuracy of 100%. The machine learning-based air quality prediction model helps India meteorological department in predicting the future of air quality, and its status and depends on that they can take action.

scholarly journals A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

2018 ◽  
Vol 2 (1) ◽  
pp. 5 ◽  
Author(s):  
Dixian Zhu ◽  
Changjie Cai ◽  
Tianbao Yang ◽  
Xun Zhou
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Prediction Models ◽  
Frobenius Norm ◽  
Quality Prediction ◽  
Pollution Concentration ◽  
Air Quality Prediction ◽  
Standard Regression

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and ℓ 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.

scholarly journals Air Quality Prediction based on Supervised Machine Learning Methods

2019 ◽  
Vol 8 (9S4) ◽  
pp. 206-212
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Quality Prediction ◽  
Validation Data ◽  
Air Quality Prediction ◽  
Learning Techniques

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 compared with respect to Precision, Recall and F1 Score. It is found that Decision Tree algorithm works well for predicting air quality. This application can help the meteorological Department in predicting air quality. In future, this work can be optimized by applying Artificial Intelligence techniques.

scholarly journals Air Quality Index Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 7489-7492
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Learning Algorithms ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Support Vector

— The global environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.

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