Performance Evaluation of Extreme Learning Machine Techniques For Prediction of Noise Pollution

Abstract

Urban planning, epidemiology research, and environmental management have significant challenges when predicting intraurban noise levels in communities, particularly in developing nations. In order to accurately predict changes in noise levels during intraurban development and the resulting noise pollution, majority of existing noise-predicting models are limited. In this study, two noise prediction models (C-ELM and B-ELM) were developed for Tarkwa Nsuaem Municipality and their performances evaluated using statistical indicators. Using statistical measures to compare the models' performances, the B-ELM outperformed the C-ELM. The indications show the difference, with the RMSE of B-ELM being 0.8736 dB and that of C-ELM being 3.675145 dB. Additionally, the B-Standard ELM's Deviation and Mean Square Error were 0.804479 dB and 0.1399 dB, respectively, while for the C-ELM, they were 3.73656 dB and 0.0619 dB. The findings of the B-ELM were used to create a map that depicts the distribution of the expected noise levels. It was discovered that there is a hazard, meaning persons who live in that region are at a high risk of experiencing negative health impacts from noise levels above 65 dB, when comparing the expected noise levels to the EPA limits.