Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana

Peter Ekow Baffoe; Alfred Allen Duker

doi:doi:10.11648/j.ajmcm.20190402.12

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Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana

Peter Ekow Baffoe, Alfred Allen Duker

Published in American Journal of Mathematical and Computer Modelling (Volume 4, Issue 2)

Received: 30 April 2019 Accepted: 4 June 2019 Published: 20 June 2019

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Abstract

Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.

Published in	American Journal of Mathematical and Computer Modelling (Volume 4, Issue 2)
DOI	10.11648/j.ajmcm.20190402.12
Page(s)	36-44
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 Models

References

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APA Style

Peter Ekow Baffoe, Alfred Allen Duker. (2019). Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. American Journal of Mathematical and Computer Modelling, 4(2), 36-44. https://doi.org/10.11648/j.ajmcm.20190402.12

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ACS Style

Peter Ekow Baffoe; Alfred Allen Duker. Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. Am. J. Math. Comput. Model. 2019, 4(2), 36-44. doi: 10.11648/j.ajmcm.20190402.12

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AMA Style

Peter Ekow Baffoe, Alfred Allen Duker. Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. Am J Math Comput Model. 2019;4(2):36-44. doi: 10.11648/j.ajmcm.20190402.12

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@article{10.11648/j.ajmcm.20190402.12,
  author = {Peter Ekow Baffoe and Alfred Allen Duker},
  title = {Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {4},
  number = {2},
  pages = {36-44},
  doi = {10.11648/j.ajmcm.20190402.12},
  url = {https://doi.org/10.11648/j.ajmcm.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20190402.12},
  abstract = {Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.},
 year = {2019}
}

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TY  - JOUR
T1  - Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana
AU  - Peter Ekow Baffoe
AU  - Alfred Allen Duker
Y1  - 2019/06/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmcm.20190402.12
DO  - 10.11648/j.ajmcm.20190402.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 36
EP  - 44
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20190402.12
AB  - Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.
VL  - 4
IS  - 2
ER  -

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Author Information

Peter Ekow Baffoe

Department of Geomatic Engineering, Faculty of Mineral Resources Technology, University of Mines and Technology, Tarkwa, Ghana
Alfred Allen Duker

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

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APA Style

Peter Ekow Baffoe, Alfred Allen Duker. (2019). Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. American Journal of Mathematical and Computer Modelling, 4(2), 36-44. https://doi.org/10.11648/j.ajmcm.20190402.12

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ACS Style

Peter Ekow Baffoe; Alfred Allen Duker. Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. Am. J. Math. Comput. Model. 2019, 4(2), 36-44. doi: 10.11648/j.ajmcm.20190402.12

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AMA Style

Peter Ekow Baffoe, Alfred Allen Duker. Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana. Am J Math Comput Model. 2019;4(2):36-44. doi: 10.11648/j.ajmcm.20190402.12

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@article{10.11648/j.ajmcm.20190402.12,
  author = {Peter Ekow Baffoe and Alfred Allen Duker},
  title = {Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {4},
  number = {2},
  pages = {36-44},
  doi = {10.11648/j.ajmcm.20190402.12},
  url = {https://doi.org/10.11648/j.ajmcm.20190402.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20190402.12},
  abstract = {Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana
AU  - Peter Ekow Baffoe
AU  - Alfred Allen Duker
Y1  - 2019/06/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ajmcm.20190402.12
DO  - 10.11648/j.ajmcm.20190402.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 36
EP  - 44
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20190402.12
AB  - Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.
VL  - 4
IS  - 2
ER  -

Copy | Download