Optimizing Regression Models for Predicting Noise Pollution Caused by Road Traffic

The study focuses on addressing the growing concern of noise pollution resulting from increased transportation. Effective strategies are necessary to mitigate the impact of noise pollution. The study utilizes noise regression models to estimate road-traffic-induced noise pollution. However, the availability and reliability of such models can be limited. To enhance the accuracy of predictions, optimization techniques are employed. A dataset encompassing various landscape configurations is generated, and three regression models (regression tree, support vector machines, and Gaussian process regression) are constructed for noise-pollution prediction. Optimization is performed by fine-tuning hyperparameters for each model. Performance measures such as mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R-2) are utilized to determine the optimal hyperparameter values. The results demonstrate that the optimization process significantly improves the models' performance. The optimized Gaussian process regression model exhibits the highest prediction accuracy, with an MSE of 0.19, RMSE of 0.04, and R-2 reaching 1. However, this model is comparatively slower in terms of computation speed. The study provides valuable insights for developing effective solutions and action plans to mitigate the adverse effects of noise pollution.

Automated summary

The study addresses the concern of noise pollution from increased transportation and proposes effective strategies to mitigate its impact. Noise regression models are used to estimate road-traffic-induced noise pollution, and optimization techniques are employed to enhance prediction accuracy. The optimized Gaussian process regression model exhibits the highest prediction accuracy.