An Interpretable Machine Learning Framework for Urban Traffic Noise Prediction in Kuwait: A Data-Driven Approach to Environmental Management

Jamal Almatawah, Mubarak Alrumaidhi (), Hamad Matar, Abdulsalam Altemeemi and Jamal Alhubail
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Jamal Almatawah: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait
Mubarak Alrumaidhi: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait
Hamad Matar: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait
Abdulsalam Altemeemi: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait
Jamal Alhubail: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait

Sustainability, 2025, vol. 17, issue 19, 1-18

Abstract: Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R 2 = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation.

Keywords: traffic noise exposure; urban environment; noise pollution; noise management; machine learning; shapley additive explanations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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