Air Pollution Prediction using Machine Learning

Simu, Shreyas; Turkar, Varsha; Martires, Rohit; Asolkar, Vranda; Monteiro, Swizel; Fernandes, Vaylon; Salgaoncary, Vassant

doi:10.1109/ibssc51096.2020.9332184

Cited by 14 publications

(11 citation statements)

References 7 publications

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“…These findings show that accuracy can be improved with adequate training and data management of class imbalance. The accuracy of the paper [4] [5] was 80 percent. A comparison study was carried out.…”

Section: Introductionmentioning

confidence: 95%

“…The authors proposed a deep machine learning algorithm for air pollution prediction in this paper. Based on the above analysis, [4] is the best study where the model obtained 80% accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The total number of articles published on this topic over the past five years is 25. The authors of [4] discovered that the one-hour dataset outperformed the five-minute dataset regardless of the algorithms used. In the study given in [5], the accuracy obtained was 70%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

show abstract

“…The total number of articles published on this topic over the past five years is 25. In [4], the authors found that the one-hour dataset had better results than the five-minute dataset, regardless of the algorithms used. In the study given in [5], the accuracy obtained is 70%.…”

Section: Introductionmentioning

confidence: 99%

“…These findings show that accuracy can be improved with practical training and data management of class imbalance. In the paper [4][6], accuracy has been obtained of 80%. Various machine algorithms were used to forecast emission rates based on this data, and a comparison study was performed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Air Pollution Level in Particular Region Area Using Logistic Regression and Naive Bayes

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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To predict the air pollution level in a particular region area using an Innovative Logistic Regression algorithm compared with the Naive Bayes algorithm. The Logistic Regression Algorithm and the Naive Bayes Algorithm are two sets of algorithms. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N=5 iterations for each method. The threshold value is 0.05%, and the Confidence Interval is 95%. The G-power test used is about 80%. The innovative Logistic Regression algorithm (98.26%) has better accuracy when compared with Naive Bayes(97.32%). Logistic Regression has the highest accuracy in comparison to the Naive Bayes algorithm. Significance value for accuracy is 0.056(p>0.05), Precision 0.02(p<0.05) and recall 0.01(p<0.05) based on 2-tail analysis. Logistic Regression outperforms the Naive Bayes Algorithm in the prediction of air pollution.

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Atmospheric Dispersion Prediction for Toxic Gas Clouds by Using Machine Learning Approaches

Rada,

Granados,

Quintero M

et al. 2023

Communications in Computer and Information Science

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Air Pollution Prediction using Machine Learning

Cited by 14 publications

References 7 publications

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Prediction of Air Pollution Level in Particular Region Area Using Logistic Regression and Naive Bayes

Atmospheric Dispersion Prediction for Toxic Gas Clouds by Using Machine Learning Approaches

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