Despite its importance to public health, environmental noise and soundscapes are a forgotten topic in urban design. This study crates a framework for a design tool for soundscape design. The aim of this framework is to bridge the knowledge gap of soundscapes for urban designers. This is done by looking at the relationship between design elements and the percevied pleasantness in the soundscape. This pleasantness will be predicted by machine learning methods.
Machine learning methodologies present a promising approach for predicting and evaluating the efficacy of potential solutions aimed at improving urban soundscapes. By analyzing datasets that include environmental factors, noise levels, architectural designs, and community preferences, machine learning algorithms can help identify optimal interventions. Predictive models can also streamline decision-making processes by forecasting the potential impact of proposed solutions on soundscape quality.
This paper investigates the complexities of urban soundscapes, examines the feasibility and limitations of using machine learning to predict viable solutions, and proposes a data-driven framework to guide decision-making in urban development. The goal is to enhance auditory environments in urban areas without the necessity of consulting soundscape experts.
The RF regressor that was used for the prediction model in this research had an R2 of 0.41, explaining 41% of the variance of the model. This framework is tried and tested on a new location to verify its use. The prediction maps and section are a helpful tool in communicating the value of the soundscape pleasantness and understanding the effect that the design elements have on its prediction.