Vehicle Emission Detection in Data-Driven Methods
Joint Authors
He, Zheng
Ye, Gang
Jiang, Hui
Fu, Youming
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-14
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring.
Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods.
In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants.
To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time.
This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.
American Psychological Association (APA)
He, Zheng& Ye, Gang& Jiang, Hui& Fu, Youming. 2020. Vehicle Emission Detection in Data-Driven Methods. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195493
Modern Language Association (MLA)
He, Zheng…[et al.]. Vehicle Emission Detection in Data-Driven Methods. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1195493
American Medical Association (AMA)
He, Zheng& Ye, Gang& Jiang, Hui& Fu, Youming. Vehicle Emission Detection in Data-Driven Methods. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1195493
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195493