Traffic Noise Modeling in Sambalpur City Using Machine Learning Technique

Noise is defined as sound that is considered unpleasant, loud, or disruptive to hearing. Similar to air pollution, noise pollution is a significant issue in urban areas that demands attention. Noise pollution occurs when the noise level surpasses a specific threshold and negatively impacts human health and well-being. In the field of urban planning, it becomes crucial to utilize methods and tools that can aid designers in developing, planning, and implementing appropriate measures to mitigate and control traffic noise. Consequently, it is essential to predict traffic noise levels in order to prevent excessive noise and explore potential alternative suggestions for existing road infrastructure. The objective of this study is to develop a predictive model for traffic noise level using linear regression analysis in the field of machine learning. A suitable model has been formulated using linear regression to accurately predict the traffic noise level. Subsequently, the model was tested and validated in the city of Sambalpur.