Accuracy Assessment of Dimensionality Reduction Techniques in Novel Approach of Precise Noise Levels Prediction and Mapping

The increasing effects of noise pollution have necessitated the prediction of noise levels. In this regard, it has become very prudent to find models which are practically applicable and have the capability to predict noise levels with accuracy. In this project, two dimensionality reduction techniques namely the Principal Component Analysis (PCA) and Partial Least Squares (PLS) were used in truncating the dimensions of observed noise levels data collected in the Tarkwa Mining Community (TMC) for which the data with reduced dimensions served as input data for a Back Propagation Neural Network noise prediction model. The accuracies of the techniques were determined using statistical indicators. The Partial Least Squares technique had a better accuracy with RMSE of 1.135 when hybridized with the Back Propagation Neural Network. The performance of the Principal Component Analysis was also with RMSE of 1.373 and that of the observed noise data produced an RMSE of 1.433. Graphical representations also showed the precision of individual predicted noise levels compared to the observed noise levels. The importance of the techniques used in predicting noise levels cannot be overemphasized based on the results obtained.