Environmental Noise Monitoring and Management in the Context of Artificial Intelligence

With the acceleration of urbanization, the problem of environmental noise pollution has become increasingly serious, seriously affecting the quality of life and physical and mental health of residents. This paper systematically discusses the progress and challenges of the application of artificial intelligence technology in environmental noise monitoring and management. In the field of monitoring, traditional methods such as sound level meters, remote sensing technology and noise sensors are practical, but there are limitations such as low spatial and temporal resolution, and insufficient data processing efficiency, etc. AI technology realizes efficient classification, source identification and accurate prediction of noise data through machine learning and deep learning algorithms, and significantly improves the real-time and accuracy of monitoring. In terms of management, traditional technologies such as sound insulation, sound absorption and noise elimination combined with intelligent optimization algorithms (e.g. genetic algorithms, particle swarm optimization) and simulation models can dynamically optimize the noise reduction program and improve the management effect. The integration of AI and traditional technologies provides a new way of controlling the source and blocking the propagation path of noise pollution by means of data-driven analysis, model prediction and optimization of strategies. However, the problems of high data quality dependence, insufficient model interpretability and computational complexity still need further breakthroughs. Future research should focus on multi-technology synergistic innovation to promote the development of environmental noise monitoring and management in the direction of intelligence and efficiency, and provide technical support for building livable urban environment.