A Binary Classification Model for PM&lt;inf&gt;10&lt;/inf&gt; Levels

Kaya, Kıymet; Öğüdücü, Şule Gündüz

doi:10.1109/ubmk.2018.8566285

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…proposed a solution to an unbalanced problem in order to address the prediction problem of the regression model due to the variation in the occurrence rate by particulate matter concentration. They confirmed the accuracy of approximately 80% in the entire data set by making the number of samples of the class the same for unbalanced data through the proposed upper sampling and down sampling [12].…”

Section: Introductionmentioning

confidence: 60%

Comparative analysis of multiple classification models to improve PM10 prediction performance

Jung

Cho

Lee

et al. 2021

IJECE

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With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model.

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Section: Introductionmentioning

confidence: 60%

Comparative analysis of multiple classification models to improve PM10 prediction performance

Jung

Cho

Lee

et al. 2021

IJECE

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“…During hyper-parameter tuning for the model of single layer LSTM with 72 memory units model, we used the meteorological data that performed best in our previous study 52 and forecasted PM 10 density at time t by using data we have at (t-4), (t-12), and (t-24). We evaluated model performance that depends on parameter changes according to MAE (10) and RMSE (11).…”

Section: Model Implementation and Experimental Resultsmentioning

confidence: 99%

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Kaya

Öğüdücü

2020

Sci Rep

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Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM 10 as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.

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“…Randomized tree classification is also defended by the researcher. In [16,17] a classification algorithm is used where a way to forecast air pollution levels has been given. SVM, Logistic Regression, and Support Vector machines are some of the algorithms utilized by an author to solve a problem.…”

Section: Literature Surveymentioning

confidence: 99%

“…In [18] Vehicle emissions forecasts were made in Spain using an SVM-based logistic regression that included the most important inductive reasoning in order to provide a good prediction of the main pollutants. The major findings of the existing methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] are that many different forms of pollution that are caused due to human activities are continuously monitored using several techniques, but as per the technological aspect, it is not possible to stop the spread of polluting contents. However, the presence of several chemicals in the atmosphere can be reduced by preventing the burning ratio of fossil fuels and other residues that introduces pollution to the surroundings.…”

Section: Literature Surveymentioning

confidence: 99%

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Electronics

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Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE).

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A Binary Classification Model for PM<inf>10</inf> Levels

Cited by 6 publications

References 17 publications

Comparative analysis of multiple classification models to improve PM10 prediction performance

Comparative analysis of multiple classification models to improve PM10 prediction performance

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

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