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Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

Author

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  • Ahmed Abdulkareem Ahmed Adulaimi

    (Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Biswajeet Pradhan

    (Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
    Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia)

  • Subrata Chakraborty

    (Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Abdullah Alamri

    (Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

This study estimates the equivalent continuous sound pressure level (L eq ) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction L eq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (L eq ). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

Suggested Citation

  • Ahmed Abdulkareem Ahmed Adulaimi & Biswajeet Pradhan & Subrata Chakraborty & Abdullah Alamri, 2021. "Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS," Energies, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5095-:d:617143
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    References listed on IDEAS

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    1. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    2. Omer Saud Azeez & Biswajeet Pradhan & Helmi Z. M. Shafri, 2018. "Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    3. Luca Fredianelli & Marco Nastasi & Marco Bernardini & Francesco Fidecaro & Gaetano Licitra, 2020. "Pass-by Characterization of Noise Emitted by Different Categories of Seagoing Ships in Ports," Sustainability, MDPI, vol. 12(5), pages 1-12, February.
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