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Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach

Received: 9 May 2019     Accepted: 17 June 2019     Published: 26 July 2019
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Abstract

Noise prediction models are very useful for urban planning and environmental management. As a result researchers are always searching for methods that are practically applicable in predicting noise levels accurately. It therefore became paramount to implement special systems that could to predict noise levels accurately for an urban area. In this study two land-use regression methods, were used to formulate two noise level prediction models namely, multiple linear regression (MLR) and analytical hierarchy process (AHP)-multiple linear regression for the Tarkwa Mining Community (TMC). The performances of the two models were evaluated using statistical indicators. The MLR model performed better than that of a hybrid model of AHP-MLR with RMSE of 1.569, standard deviation of 1.585, R2 of 0.961 and R of 0.980. The performance of the hybrid AHP-MLR was also RMSE of 1.774, standard deviation of 1.758, R2 of 0.955 and R of 0.977. Plotted box-and-whisker and range plots further confirmed the performances of the two models. The resulting map from the noise prediction gave insight suggested that with the appropriate data and useful tools noise pollution levels of an urban area could be well predicted and mapped for urban planning and environmental management.

Published in Urban and Regional Planning (Volume 4, Issue 3)
DOI 10.11648/j.urp.20190403.12
Page(s) 91-99
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Noise Level, Noise Pollution, Noise Models, Land Use Regression Model

References
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[2] Satterfield K. (2001). Balance disorders and patients with NIHL in on ear. America Academy of Otolaryngology Head and Neck Surgery, Vol. 703, pp. 1-3.
[3] Goines, L. and Hagler, L. (2007), “Noise Pollution: A Modern Plaque”, Southern Medical Journal, Vol. 100, Pp. 287-294.
[4] Benfield, J. A., Nurse, G. A., Jakubowski, R., Gibson, A. W., Taff, B. D., Newman, P. and Bell, P. A. (2012), “Testing Noise in the Field: A Brief Measure of Individual Noise Sensitivity”, Environment and Behavior, Vol. 46, No. 3, pp. 353–372.
[5] Ighoroje, A. D. A, Marchie, C., and Nwobodo, E. D. (2004), Noise-Induced Hearing Impairment as an Occupational Risk Factor among Nigerian Traders, Nigerian Journal of Physiological Sciences 19 (1-2): 14-19, Physiological Society of Nigeria 2004.
[6] Passchier-Vermeer, W., and Passchier, W. F., (2000), Noise Exposure and Public Health, Environmental Health Perspective, Vol. 108, p. 123-131.
[7] Xie, D., Liu, Y., and Chen, J., (2011), “Mapping Urban Environmental Noise: A Land Use Regression Method”, Environmental, Science and Technology, Vol. 45, 7358–7364.
[8] Henderson, F., Cox, F., Ganesh, S., Jonker, W., Young, W. (2015), “Rumen Microbial Community Composition Varies with Diet and Host, but a Core Microbiome Is Found Across A Wide Geographical Range”, Journal of Expo Science Environment.
[9] Aguilera, I., Foraster, M., Basagaña, X., Corradi, E., Deltell5, A., Morelli, X., Phuleria, H. C., Ragettli, M. S., Rivera, M., Thomasson, A., Rémy Slama, R., and Künzli, N., (2015), “Application of Land-use Regression Modelling to Assess the Spatial Distribution of Road Traffic Noise in Three European Cities.”, Journal of Expo Science Environ Epidemiol. Vol. 25 (1): 97-105.
[10] Akinlalu, A., A., Adegbuyiro, A., Adiat, K., A., N., Akeredolu, B. E., and Leteef, B. E. (2017), “Application of multi-criteria decision analysis in prediction of groundwater resources potential: A case of Oke-Ana, Ilesa Area Southwestern, Nigeria.”, NRIAG, Journal of Astronomy and Geophysics, Vol. 6, Issue 1, June 2017, pp. 184-200.
[11] Xishang, D., Huaizhi, M., and Zhen, Y. (2014), “An Application of AHP and FAHP on the Model Prediction”, Applied Mechanic and Materials, Vols. 687-691, pp. 1641-1644.
[12] Mantey, S. and Tagoe, N. D. (2012) “Geo-Property Tax Information System - A Case Study of the Tarkwa Nsuaem Municipality, Ghana”, FIG Working Week 2012: Knowing to manage the territory, protect the environment, evaluate the cultural heritage”, Rome Italy, from 6th to 10th, May 2012.
[13] Kumi-Boateng, B. (2012), “A Spatio-Temporal Based Estimation of Land-Use Cover Change and Sequestered Carbon in the Tarkwa Mining Area of Ghana”, PhD Thesis, University of Mine and Technology, Tarkwa, 2012, pp. 163.
[14] Mehdi, M. R., Minho, K., Jeong, C. S. and Mudassar, H. A. (2010), “Spatio-Temporal Patterns of Road Traffic Noise Pollution in Karachi, Pakistan”, Environment International journal, Vol. 12, pp. 1-8.
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Cite This Article
  • APA Style

    Peter Ekow Baffoe, Alfred Allen Duker. (2019). Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach. Urban and Regional Planning, 4(3), 91-99. https://doi.org/10.11648/j.urp.20190403.12

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    ACS Style

    Peter Ekow Baffoe; Alfred Allen Duker. Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach. Urban Reg. Plan. 2019, 4(3), 91-99. doi: 10.11648/j.urp.20190403.12

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    AMA Style

    Peter Ekow Baffoe, Alfred Allen Duker. Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach. Urban Reg Plan. 2019;4(3):91-99. doi: 10.11648/j.urp.20190403.12

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  • @article{10.11648/j.urp.20190403.12,
      author = {Peter Ekow Baffoe and Alfred Allen Duker},
      title = {Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach},
      journal = {Urban and Regional Planning},
      volume = {4},
      number = {3},
      pages = {91-99},
      doi = {10.11648/j.urp.20190403.12},
      url = {https://doi.org/10.11648/j.urp.20190403.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.urp.20190403.12},
      abstract = {Noise prediction models are very useful for urban planning and environmental management. As a result researchers are always searching for methods that are practically applicable in predicting noise levels accurately. It therefore became paramount to implement special systems that could to predict noise levels accurately for an urban area. In this study two land-use regression methods, were used to formulate two noise level prediction models namely, multiple linear regression (MLR) and analytical hierarchy process (AHP)-multiple linear regression for the Tarkwa Mining Community (TMC). The performances of the two models were evaluated using statistical indicators. The MLR model performed better than that of a hybrid model of AHP-MLR with RMSE of 1.569, standard deviation of 1.585, R2 of 0.961 and R of 0.980. The performance of the hybrid AHP-MLR was also RMSE of 1.774, standard deviation of 1.758, R2 of 0.955 and R of 0.977. Plotted box-and-whisker and range plots further confirmed the performances of the two models. The resulting map from the noise prediction gave insight suggested that with the appropriate data and useful tools noise pollution levels of an urban area could be well predicted and mapped for urban planning and environmental management.},
     year = {2019}
    }
    

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    T1  - Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach
    AU  - Peter Ekow Baffoe
    AU  - Alfred Allen Duker
    Y1  - 2019/07/26
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    N1  - https://doi.org/10.11648/j.urp.20190403.12
    DO  - 10.11648/j.urp.20190403.12
    T2  - Urban and Regional Planning
    JF  - Urban and Regional Planning
    JO  - Urban and Regional Planning
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    UR  - https://doi.org/10.11648/j.urp.20190403.12
    AB  - Noise prediction models are very useful for urban planning and environmental management. As a result researchers are always searching for methods that are practically applicable in predicting noise levels accurately. It therefore became paramount to implement special systems that could to predict noise levels accurately for an urban area. In this study two land-use regression methods, were used to formulate two noise level prediction models namely, multiple linear regression (MLR) and analytical hierarchy process (AHP)-multiple linear regression for the Tarkwa Mining Community (TMC). The performances of the two models were evaluated using statistical indicators. The MLR model performed better than that of a hybrid model of AHP-MLR with RMSE of 1.569, standard deviation of 1.585, R2 of 0.961 and R of 0.980. The performance of the hybrid AHP-MLR was also RMSE of 1.774, standard deviation of 1.758, R2 of 0.955 and R of 0.977. Plotted box-and-whisker and range plots further confirmed the performances of the two models. The resulting map from the noise prediction gave insight suggested that with the appropriate data and useful tools noise pollution levels of an urban area could be well predicted and mapped for urban planning and environmental management.
    VL  - 4
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Author Information
  • Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Ghana

  • Department of Geomatic Engineering, School of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

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