Risk assessment for extreme air pollution events using vine copula

Ismail, Mohd Sabri; Masseran, Nurulkamal

doi:10.1007/s00477-024-02682-7

Stoch Environ Res Risk Assess

2024

DOI: 10.1007/s00477-024-02682-7

|View full text |Cite

Risk assessment for extreme air pollution events using vine copula

Mohd Sabri Ismail,

Nurulkamal Masseran

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Logistic regression approach on classifying air-pollution events: a parsimony technique

Masseran

2024

Environ. Res. Commun.

View full text Add to dashboard Cite

This study deals with the problem of classifying extreme and nonextreme air pollution events using the logistic regression technique, which is a model specifically developed for binary classification. Aiming at the features engineering of duration, intensity, and severity size of air pollution events, this study presents logistic regression as a parsimonious yet effective model. A case study was performed in Klang, Malaysia. Inductive learning with a basis of data mining framework was employed to train and test the accuracy of logistic regression. The results revealed a high precision and low generalization error for both extreme and nonextreme air pollution events. In conclusion, logistic regression is a suitable and efficient machine learning model for the classification of air pollution events. Further investigation is warranted to incorporate topological characteristics of air pollution events as additional features in training logistic regression models, which can potentially offer valuable insights into the behavior of air pollution events and improve the classification process.

show abstract

Logistic regression approach on classifying air-pollution events: a parsimony technique

Masseran

2024

Environ. Res. Commun.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Risk assessment for extreme air pollution events using vine copula

Cited by 1 publication

References 74 publications

Logistic regression approach on classifying air-pollution events: a parsimony technique

Logistic regression approach on classifying air-pollution events: a parsimony technique

Contact Info

Product

Resources

About