## 1 Introduction  
The Internet of Things (IoT) is an idea that connects the physical objects to the Internet, which can play a remarkable role and improve the quality of our lives in many different domains [1,2]. There are many possibilities and uncertainties in the application scenarios of IoT [3]. The application of the IoT in the urban area is of particular interest, as it facilitates the appropriate use of the public resources, enhancing the quality of the services provided to the citizens, and minimizing the operational costs of the public administrations, thus realizing the Smart City concept [4]. The urban IoT may provide a distributed database collected by different sensors to have a complete characterization of the environmental conditions [2]. Specifically, urban IoT can provide noise monitoring services to measure the noise levels generated at a given time in the places where the service is adopted [5]. With the unprecedented rate of urbanization as a result of the rapid acceleration of economic and population growth, new problems arose, such as traffic congestion, waste management, pollution, and parking allocation [6]. Recently, noise pollution has become one of the core urban environmental pollutions and has received increasing attention. Urban noise pollution can cause various consequences,especially severe effects on human health such as hearing damage, affecting sleep and increasing psychological stress [7; 8]. To solve the problem of noise pollution, the Chinese government issued the Law on the Prevention and Control of Environmental Noise Pollution of the People's Republic of China [9] and adopted a series of policy measures on controlling urban noise, rural noise, industrial noise, construction noise, etc. However, due to the variety of noise sources [10] and the prevalence of noise pollution, the problem of environmental noise pollution in China has not been adequately solved and remained as one of the main urban ecological problems. The Chinese Environmental Noise Pollution Prevention Report showed that in 2017, environmental noise-related complaints received by environmental protection departments at all levels accounted for 42.9% of the total environmental complaints [11]. According to the latest release of the Beijing-Tianjin-Hebei research report of the environmental resource based on the big data analysis in June 2018, noise pollution cases accounted for 73% of the total number of cases in the Beijing-Tianjin-Hebei regional environmental pollution case [12]. Therefore, paying attention to urban environmental noise pollution and making an accurate and timely prediction of environmental noise will assist in comprehensively grasping and managing the urban acoustic environment, thus improving residents' life satisfaction. Noise prediction has significant theoretical research value and practical significance. In past studies, the authors proposed many methodologies to evaluate and predict the noise in the environment. These prediction methods are mainly classified into three groups: physical propagation models, traditional statistical methods, and machine learning methods. The first group of noise prediction methods is the physical propagation model, which predicts the ambient noise of another location from the spatial perspective, based on the distance from the sound source to the calculated noise location point and the physical properties of the sound source itself. Since the early 1960s, many developed countries conducted research on airport noise and introduced many airport noise management methods and models [13; 14], such as the Integrated Noise Model promoted by the Federal Aviation Authority, the Aircraft Noise Contour (ANCON) model of the United Kingdom and the Fluglaerm (FLULA) program developed in Switzerland [15; 16; 17]. Some studies focused on the spatial propagation of noise to predict environmental noise [18; 19]. For example, based on the propagation model of noise attenuation, the noise distribution can be drawn [20].