## Urban Noise Suitability Assessment Method and Driving Factor Analysis Based on Machine Learning### AbstractThe evaluation of urban noise suitability is crucial for managing the urban environment. Methods for obtaining noise distribution data that are effective and economical are in high demand. This study uses various machine learning methods to investigate noise suitability in Nanchang City's central area and finds that the Random Forest algorithm performs best. The findings of the study are as follows:1. Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects noise levels in Nanchang City.2. The ranking of feature importance indicates that population distribution has the most significant impact on urban noise, followed by distance to bodies of water and road network density. These three features play a crucial role in noise assessment and should be prioritized in noise control measures.3. Areas with weak noise suitability in the central part of Nanchang are primarily concentrated on the east bank of the Ganjiang River, identifying this region as key for noise management. In general, Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Highly suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively.4. The SHAP model identifies the top three significant features and examines the varying effects of different values on noise suitability.This study employs innovative concepts and machine learning techniques from data mining to provide an objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the sustainable development of the urban environment.### First Subtopic: Machine Learning Algorithms for Urban Noise Evaluation**Introduction**With improved living standards, residents' tolerance for urban environmental issues has decreased, and noise has become a significant concern. In 2021, the "2020 National Public Service Quality Monitoring Report" released by the State Administration for Market Regulation ranked "noise control" last among 68 evaluation indicators. Furthermore, the 2023 "China Noise Pollution Prevention Report" indicated that noise-related complaints accounted for 59.9% of all environmental pollution complaints.**Research on Urban Acoustic Environments**Scholars have explored the acoustic environment in urban areas, optimizing noise conditions in major functional areas (residential, commercial, transit, industrial). They have also highlighted the role of green spaces in noise control and the positive impact of soundscape elements (water and bird songs).**Noise Mapping**Currently, noise maps are a prevalent tool for assessing and controlling noise pollution in urban environments. These maps provide a visual representation of noise distribution, allowing for precise mitigation measures. In 2002, the European Union issued the Environmental Noise Directive (2002/49/EC), requiring member states to create noise maps.### Second Subtopic: Methodology**Study Area**This study focuses on Nanchang, the capital of Jiangxi Province, representative of provincial capitals in China.**Data Sources**The study utilizes noise monitoring data from the Nanchang Environmental Protection Bureau and geospatial data from various sources. The feature matrix for urban noise suitability is constructed based on these datasets, encompassing natural environmental conditions and urban development conditions.**Machine Learning Modeling**Six classification algorithms are evaluated: Decision Trees, Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting. The Random Forest algorithm is selected for its high accuracy.### Third Subtopic: Results and Analysis**Model Reliability Evaluation**Confusion matrices, ROC curves, and learning curves are used to assess model performance. The Random Forest algorithm demonstrates high accuracy in noise suitability classification.**Urban Noise Suitability Analysis**The central urban area is divided into 58,508 grids for noise suitability prediction. The results are categorized into five classes: Height suitable, Relatively suitable, Moderately suitable, Slightly suitable, and Unsuitable. Areas with low noise suitability are concentrated in the city center, while the surrounding areas show higher noise suitability.**Analysis of Driving Factors**Population distribution is found to be the most critical factor, followed by distance to water bodies and road network density.**SHAP Model Interpretation**The SHAP model provides insights into the relationships between features and the model's output, revealing the varying contributions of distance to water bodies, population spatial distribution, and road network density to noise levels.### Fourth Subtopic: Discussion**Benefits of Machine Learning**Machine learning offers faster results and eliminates cost constraints compared to traditional methods. By utilizing existing data, this study provides an economical, efficient, and accurate approach to urban noise suitability evaluation.**Decision-Making Support**The study's findings offer scientific decision-making support for urban planning and environmental management. By identifying key noise drivers, policymakers can prioritize targeted noise reduction measures.### Fifth Subtopic: Conclusion**Main Findings:*** Machine learning algorithms can effectively evaluate urban noise suitability.* The Random Forest algorithm outperforms other methods in this study.* Population distribution, distance to water bodies, and road network density are significant factors affecting noise suitability.**Practical Implications:*** Provides a low-cost and accurate method for urban noise assessment.* Offers scientific support for noise control measures, improving the quality of urban life and achieving sustainable urban development.### References**Include 42 references utilized in the study.**