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 |
Noise Level, Noise Pollution, Noise Models, Land Use Regression Model
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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
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
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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Download@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}
}
TY - JOUR 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 PY - 2019 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 SP - 91 EP - 99 PB - Science Publishing Group SN - 2575-1697 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 IS - 3 ER -
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