Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters

Journal article

Awomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H. 2023. Towards Adequate Policy Enhancement: An AI-Driven Decision Tree Model for Efficient Recognition and Classification of EPA Status via Multi-Emission Parameters. City and Environment Interactions. 20, pp. 1-12. https://doi.org/10.1016/j.cacint.2023.100127
AuthorsAwomuti, A., Alimo, P., Young, G., Agyeman, S., Akintunde, T., Agbeja, A., Oderinde, O., Samuel, O. and Otobrise, H.
Abstract

Accurate and timely evaluation and assessment of emission data and its impact on environmental status has been a key challenge due to the conventional manual approach utilized for independently computing most emission parameters. To resolve this long-standing issue, we proposed an Artificial Intelligence (AI)-driven Decision Tree model to adequately classify Environmental Protection Agency (EPA) status based on multiple Emission Parameters. The model's performance was systematically evaluated using multiple emission parameters obtained from a two-stroke motorcycle dataset collected in Nigeria across various metrics such as K-S Statistics, Confusion Matrix, Correlation Heat Map, Decision Tree, Validation Curve, and Threshold Plot. The K-S Statistics plot's experimental results showed a considerable correlation between HC, CO, and the target variable, with values ranging from 0.75-0.80. At the same time, CO2 and O2 do not correlate with the target variable with values between 0.00 and 0.09. The Confusion Matrix revealed that the proposed model has an overall accuracy of 99.9% with 481 true positive predictions and 75 true negative predictions, indicating the effectiveness of the proposed AI-driven model. In conclusion, our proposed AI-driven model can effectively classify EPA status based on multiple emission parameters with high accuracy, which may spur positive advancement in policy enhancement for proper environmental management.

KeywordsDecision Tree; Artificial Intelligence; EPA Status; Air Emission Parameters; Machine Learning; Emission Reduction
Year2023
JournalCity and Environment Interactions
Journal citation20, pp. 1-12
PublisherElsevier BV
ISSN2590-2520
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cacint.2023.100127
Web address (URL)https://www.sciencedirect.com/journal/city-and-environment-interactions
Accepted author manuscript
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Open
Output statusPublished
Publication dates
Online19 Nov 2023
Publication process dates
Accepted15 Nov 2023
Deposited05 Dec 2023
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