Machine Learning Based Prediction of PM 2.5 Pollution Level in Delhi
| Autor: | R Jaya Krishna, Apurv Mehrotra, Devi Prasad Sharma |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pollution
business.industry media_common.quotation_subject Air pollution medicine.disease_cause Machine learning computer.software_genre Random forest Support vector machine Naive Bayes classifier Binary classification Kernel (statistics) Principal component analysis medicine Artificial intelligence business computer media_common Mathematics |
| Zdroj: | Advances in Computing and Intelligent Systems ISBN: 9789811502217 |
| DOI: | 10.1007/978-981-15-0222-4_9 |
| Popis: | Scrutinizing Air pollution stances challenges due to the huge quantity of alignments present in the Air. Predicting PM 2.5 levels allows for further analysis and prediction of quality of air. PM 2.5 forms a major component of air pollution. This work addresses various machine learning algorithms to predict levels of PM 2.5, which are abundant in the atmosphere. We transformed problem into a binary classification with two classes being moderate and polluted. Support vector machine, Naive Bayes, K-nearest neighbors, random forest algorithms, and Principal component analysis (PCA) were applied to obtain results. The prediction scores are favorable with support vector classification kernel giving the best result. Results from random forest and Naive Bayes are similar while Naive Bayes having a much lower predicting accuracy. PCA approach does not hold much significance as it gives a much lower prediction score. |
| Databáze: | OpenAIRE |
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