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, R 2 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, R 2 of 0.955 and R of 0.977. Plotted box-andwhisker 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.

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, R 2 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.