Machine learning-based urban noise appropriateness evaluation method and driving factor analysis

Jinlin Teng; Cheng Zhang; Huimin Gong; Chunqing Liu

doi:10.1371/journal.pone.0311571

Peer Review History

Original SubmissionJuly 14, 2024
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(Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Good study and i really appreciate the presentation of results in an interesting way. FOllowing points can be revised in the manuscript 1. "However, interpolated data sometimes fail to accurately reflect actual noise levels in certain local areas. This paper proposes a machine learning-based solution for urban " Here the flow is not there. Need to flow your ideas logically, why you need machine learning. 2. Any reason for the particular selection of the study area ? 3. If possible improve the clarity of Figure 1 4. Why did you choose these algorithms ? Please justify. FOr your reference, these classical machine learning methods have been widely used in engineering applications https://www.sciencedirect.com/science/article/pii/S2590123023005157 https://www.nature.com/articles/s41598-023-40513-x 5. "models such as deep neural networks and ensemble models, they are often considered "black boxes" due to the difficulty in explaining their internal decision-making processes" For your reference : https://www.sciencedirect.com/science/article/pii/S2590123024007588 6. You might need to revisit the dependency plots description. The right side y axis is showing only the next best variable which is mostly associated with the feature in x axis, It is an optional plot.. Main dependency plot is the x axis and y (left) axis. Colour bar comes with that second feature (mostly associated). The present version is bit confusing Reviewer #2: Though work have some novelty but many limitations which made me some reservations. Before conclusions authors have mentioned line 303 However, this study has several limitations and areas for improvement , how this justify novelty and readers may have to understand this. Authors should refer recent past related papers to improve the discussions Analysis of Data Splitting on Streamflow Prediction using Random Forest (aimspress.com) AIMS Environmental Science 2024, Volume 11, Issue 4: 593-609. doi: 10.3934/environsci.2024029 Previous ArticleNext Article Kumar, V.; Azamathulla, H.M.; Sharma, K.V.; Mehta, D.J.; Maharaj, K.T. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management. Sustainability 2023, 15, 10543. doi: 10.3390/su151310543 Mahtabi, G.; ; Pal, M. Classification of Hydraulic Jump in Rough Beds. Water 2020, 12, 2249. doi: 10.3390/w12082249 ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. 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Revision 1
15 Sep 2024 Author Response Dear Dr. Upaka Rathnayake and Dr. Paula Katrina A. Maderazo, I hope this email finds you well. We sincerely appreciate your comprehensive review of our manuscript. Based on your valuable feedback, particularly your request to provide a new link to the Chinese Standard Map Service System, we have made the following explanations and modifications: 1.Source of Maps: Since the Standard Map Service of China is the official website of the Ministry of Natural Resources of China, it may be subject to geographical restrictions and network policies. As a result, accessing it from abroad could lead to issues such as slow loading times, failure to load, or complete blockage. We have therefore revisited the images in the manuscript that may involve copyrighted material (Figures 1, 2, and 6). We have removed any satellite or map images that could raise copyright concerns and replaced them with open-access maps from sources compliant with the Plos One copyright guidelines. The details can be found in Table 2 of the "Revised Manuscript with Track Changes." A brief explanation is as follows: ①Figure 1 does not involve satellite maps. The vector map of roads, water bodies, green spaces, and other elements is sourced from the public dataset available at OpenStreetMap.（www.openstreetmap.org） ② Figure 2 is based on various data sources. We carefully reviewed the origin of each subfigure, which includes: Land Use Data, Population Data, Remote Sensing Data, Ecosystem Service Value Data, Meteorological Data, Basic Vector Data, Building Height Data, Residential POI Data, and Noise Monitoring Data. Accordingly, we have updated Table 2 to reflect these sources. Notably: �"Remote Sensing Data" has been changed from Geospatial Data Cloud to "USGS Remote Sensing Data". Using Landsat 8 OLI-TIRS data for calculating Normalized Difference Vegetation Index (NDVI)（https://earthexplorer.usgs.gov/） �"Basic Vector Data" is now sourced from the public dataset at OpenStreetMap.（www.openstreetmap.org） �"Building Height Data" is sourced from the public dataset available at the Zenodo Platform Database, accessible via this link：https://zenodo.org/records/7827315 �"Population Data" is sourced from the public WORLDPOP Dataset, available at www.worldpop.org. �"Residential POI Data" is obtained from the Baidu Map API Open Interface, available at this link：https://lbsyun.baidu.com/products/advantage?active=service. �Additionally, "Ecosystem Service Value Data" and "Meteorological Data" are sourced from the public Resource and Environmental Science Data Platform, accessible at http://www.resdc.cn/DOI. �"Noise Monitoring Data" is sourced from the Nanchang Environmental Protection Bureau Environmental Quality Monitoring Database, available at http://sthjj.nc.gov.cn/ncgbj/zsjcbg/nav_list.shtml. ③ Figure 6 is based on the data and content described earlier in the manuscript. The final prediction results were obtained using machine learning techniques and do not involve any third-party copyright concerns. This figure represents one of the key findings of our study. 2．Manuscript Formatting: I have carefully reviewed and ensured that the manuscript now fully complies with the formatting guidelines of PLOS ONE. 3．Data and Code Availability: We have uploaded the code and urban noise monitoring data used in the study to GitHub. This repository also includes the final prediction data, supporting the principles of reproducibility and open data. You can access the repository at the following link: https://github.com/tengjinlin/noise-prediction.git. 4．Image Conversion: Lastly, all images in the manuscript have been converted using the PACE conversion engine's digital diagnostic tool (available at https://pacev2.apexcovantage.com/) to ensure they meet the image quality requirements of PLOS. Thank you again for your time and effort in reviewing our work. We hope these revisions address your concerns, and we look forward to your feedback. Best regards, TENG Jinlin College of Forestry, Jiangxi Agricultural University, Nanchang 330045, Jiangxi Province，China E-mail:tengjinlin@stu.jxau.edu.cn Dear Reviewer 1, Thank you for your valuable feedback. We have carefully considered your comments and have made the following revisions to the manuscript: 1．Introduction logic of research methods: We have revised the manuscript to clarify that, although the Kriging interpolation method accounts for the spatial correlation of spatial data and can sometimes produce accurate predictions, it may be significantly impacted by uneven sample point distribution or outliers in certain areas. This study, therefore, proposes a machine learning approach, which relies on large datasets for training and learning, enabling it to automatically extract features and patterns from the data. This means that even if the interpolation method fails in some localized areas, the machine learning model can still predict the actual levels in those areas by learning from data in other regions. The revisions can be found in the “Revised Manuscript with Track Changes.” 2．Rationale for Selecting Nanchang City: We chose Nanchang City as our study area because it is the capital of Jiangxi Province and represents typical noise environments found in Chinese provincial capital cities. Like other large and medium-sized cities, Nanchang faces significant population pressures, leading to common issues such as traffic noise, industrial noise, construction noise, and noise from social activities. Therefore, Nanchang serves as a representative case for studying noise environments in similar urban settings. 3．Image Quality: We have improved the clarity of the figures in the manuscript as per your suggestion. The updated figures are attached separately in this email. 4．Explanation for Choosing the Six Algorithms: Based on the references you provided and additional literature retrieved from databases, we have elaborated on the details of the six algorithms selected for the study and explained the rationale behind their selection. Decision Trees (DT) are tree-based algorithms used for classification and regression tasks. They construct a tree structure by dividing the dataset into subsets, with each node representing a feature, each branch representing a feature value, and the final leaf nodes representing a class (or regression value). Decision trees are easy to understand and interpret, and they can handle both numerical and categorical data, but they are prone to overfitting. Random Forest (RF) is an ensemble learning algorithm that improves the accuracy of classification and regression tasks by training multiple decision trees. It enhances model performance by averaging or voting on the predictions of numerous decision trees, reducing overfitting. RF is suitable for large datasets, robust to outliers and missing data, and can estimate feature importance. Support Vector Machine (SVM) is a supervised learning algorithm used for binary classification and regression tasks. It finds the optimal hyperplane in the feature space to separate data points from different classes, maximizing the margin between them. SVM has highly flexible kernel functions, can handle high-dimensional datasets, and is robust to outliers, but it requires longer training times for large datasets. Logistic Regression is a linear model used for binary classification problems. It classifies based on a linear combination of features, using the logistic function to convert the linear output into probabilities, which are then used for classification based on a threshold. Logistic regression is simple and easy to implement, can output class probabilities, but has limited ability to model non-linear problems. K-Nearest Neighbors (KNN) is an instance-based learning algorithm used for classification and regression tasks. It classifies by considering the class of the nearest neighbors to the input instance, using voting to determine the class of a sample. KNN requires no model training, is easy to understand and implement, but has slower prediction speed for large datasets and is less suitable for high-dimensional data. Gradient Boosting is an ensemble learning algorithm that improves model performance by sequentially training multiple weak learners. It iteratively trains new models to correct the errors of the previous model, gradually enhancing model performance. Gradient boosting generally performs well in prediction tasks, but it is sensitive to hyperparameter selection and tuning, requiring some experience in hyperparameter optimization. 5．Model Interpretability: We have incorporated the Shapley Additive Explanations (SHAP) method to enhance the interpretability of the model, as per your suggestion. We specifically referenced the following paper: Kulasooriya WKVJ, Ranasinghe RSS, Perera US, Thisovithan P, Ekanayake IU, Meddage DPP. "Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface." Scientific Reports. 2023; 13(1):13138. Available from: https://go.exlibris.link/ckWy5VZt doi: 10.1038/s41598-023-40513-x. 6．Revised SHAP Section: I have restructured the section on SHAP explanations to improve its clarity and readability. Below is the revised text: Using SHAP dependence plots, SHAP can also reveal the interactions between individual features. Figure 8 illustrates the dependence plots for the distance to water bodies, population spatial distribution, and road density. These three features demonstrate higher importance compared to others. The dependence plots highlight the contribution of these features to the output and identify the features with which they primarily interact. The SHAP value for noise is highest when the distance to water bodies is within 500 meters and reaches its lowest point at approximately 800 meters. The color scale on the right indicates the variable with which the distance to water bodies mainly interacts. As shown in the figure, the random forest model suggests that the distance to water bodies is primarily associated with road network density, and the SHAP value increases significantly when the distance interacts with higher road network density (represented by red dots). Regarding population spatial distribution, the SHAP value for noise reaches its maximum when the population spatial distribution exceeds 150, while lower SHAP values are observed within the range of 100. The model indicates a strong correlation between population spatial distribution and distance from residential areas. Most points with a greater distance from residential areas (red dots) are concentrated within the population spatial distribution range of 100. A greater distance from residential areas combined with a smaller population distribution typically results in a lower urban noise environment. For road network density, the SHAP value for noise peaks around a density of 500 and reaches its lowest values between 750 and 1125. Similar to population spatial distribution, the model suggests a strong correlation between road network density and distance from residential areas. Thank you once again for your valuable feedback. We hope these revisions meet your expectations, and we look forward to any further suggestions you may have. Best regards, TENG Jinlin College of Forestry, Jiangxi Agricultural University, Nanchang 330045, Jiangxi Province，China E-mail:tengjinlin@stu.jxau.edu.cn Dear Reviewer 2, Dear Dr. Mohammad Azamathulla Hazi, Thank you very much for your valuable feedback. Your suggestions, particularly the three reference papers you provided, have significantly enhanced the originality of the manuscript and the writing of the discussion section. Below is a summary of our responses and the corresponding revisions made to the manuscript: The potential benefits of machine learning are increasingly recognized by the public. Compared to traditional methods, machine learning offers faster results[40], which is particularly important for the rapidly changing urban noise environment. In this study, we evaluated urban noise using the Random Forest algorithm, which was selected after comparing various algorithms based on relevant parameters. This approach eliminates the need for costly on-site measurements. The accuracy of the model depends not only on the choice of the correct algorithm but also on the availability of extensive data[41]. By aggregating multi-source geospatial data, we fully utilized existing resources, thereby overcoming cost and time constraints. The final results demonstrate high model accuracy and identify key features influencing noise distribution, particularly population spatial distribution, distance to water bodies, and road network density. This confirms the robust capability of machine learning models in handling complex and nonlinear data, especially in environmental science involving geospatial data. As a low-cost, efficient, and accurate approach for urban noise suitability evaluation, this study not only innovates noise assessment methods but also provides scientific decision-making support for urban planning and environmental management[42]. Effective noise control strategies are crucial for improving residents' quality of life and achieving sustainable urban development, especially in the context of rapid urbanization[43]. By identifying and prioritizing key geographical factors influencing noise distribution, policymakers and planners can implement targeted noise reduction measures. As the Nanchang municipal government continues to deploy automatic noise monitoring stations, the study's database can be expanded in the future to include 24-hour monitoring data from these fixed points. This will allow for more detailed and in-depth research by incorporating the temporal dimension of noise variations. Thank you once again for your valuable feedback. We hope these revisions meet your expectations, and we look forward to any further suggestions you may have. Best regards, TENG Jinlin College of Forestry, Jiangxi Agricultural University, Nanchang 330045, Jiangxi Province，China E-mail:tengjinlin@stu.jxau.edu.cn Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0311571.r002
23 Sep 2024 Decision Letter - Upaka Rathnayake, Editor Machine Learning-based Urban Noise Appropriateness Evaluation Method and Driving Factor Analysis PONE-D-24-29106R1 Dear Dr. Chunqing, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: THe authors have successfully addressed the comments. The manuscript is acceptable. Reviewer #2: Manuscript Number PONE-D-24-29106R1 Machine Learning-based Urban Noise Appropriateness Evaluation Method and Driving Factor Analysis Manuscript Number PONE-D-24-29106R1 Machine Learning-based Urban Noise Appropriateness Evaluation Method and Driving Factor Analysis Dear authors thanks for applying all corrections ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes: Mohammad Azamathulla Hazi ******** https://doi.org/10.1371/journal.pone.0311571.r003
Formally Accepted
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