Optimizing Urban Noise Control Using AI-Driven Acoustic Mapping
Authors:-Krishnaraj. S
Abstract-:Urban noise pollution has emerged as a pressing public health concern, affecting millions of city dwellers globally. Traditional methods of noise assessment and control have often fallen short due to limitations in spatial coverage, temporal resolution, and adaptability to dynamic urban environments. This paper presents a comprehensive exploration of AI-driven acoustic mapping as a transformative approach to optimize urban noise control. By integrating machine learning algorithms, real-time sensor data, and geospatial analytics, AI can generate high-resolution acoustic maps that capture the complex soundscape of urban environments. These maps not only provide detailed spatial distribution of noise levels but also reveal patterns, predict future noise trends, and guide mitigation strategies more efficiently than conventional models.
The objective of this study is to evaluate how artificial intelligence can revolutionize noise monitoring by enabling more accurate, scalable, and cost-effective solutions. Key components such as neural networks, edge computing, Internet of Things (IoT) sensor networks, and predictive analytics are examined for their role in data collection, processing, and interpretation. Case studies from leading smart cities illustrate successful implementations and potential pitfalls. In addition, we propose a conceptual framework for urban policymakers to adopt AI-driven acoustic mapping as part of sustainable urban planning. The paper concludes with a critical discussion of ethical, privacy, and technological challenges, alongside recommendations for future research and deployment strategies. Through this study, we aim to contribute to the growing discourse on leveraging AI for environmental sustainability and public health in urban ecosystems.
