Volume 4, Issue 3, September 2019, Page: 91-99
Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach
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
Received: May 9, 2019;       Accepted: Jun. 17, 2019;       Published: Jul. 26, 2019
DOI: 10.11648/j.urp.20190403.12      View  612      Downloads  111
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.
Noise Level, Noise Pollution, Noise Models, Land Use Regression Model
To cite this article
Peter Ekow Baffoe, Alfred Allen Duker, Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach, Urban and Regional Planning. Vol. 4, No. 3, 2019, pp. 91-99. doi: 10.11648/j.urp.20190403.12
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