An urban big data-based air quality index prediction: (Record no. 14699)
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| fixed length control field | 02970nab a2200205 4500 |
| 005 - DATE & TIME | |
| control field | 20230914161936.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230914b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Zhiqiang, Zou |
| 245 ## - TITLE STATEMENT | |
| Title | An urban big data-based air quality index prediction: |
| Sub Title | a case study of routes planning for outdoor activities in Beijing/ |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sage, |
| Date of publication, distribution, etc | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol. 47, Issue 6, 2020, ( 948–963 p.) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Urban big data include various types of datasets, such as air quality data, meteorological data, and weather forecast data. Air quality index is broadly used in many countries as an indicator to measure the air pollution status. This indicator has a great impact on outdoor activities of urban residents, such as long-distance cycling, running, jogging, and walking. However, for routes planning for outdoor activities, there is still a lack of comprehensive consideration of air quality. In this paper, an air quality index prediction model (namely airQP-DNN) and its application are proposed to address the issue. This paper primarily consists of two components. The first component is to predict the future air quality index based on a deep neural network, using historical air quality datasets, current meteorological datasets, and weather forecasting datasets. The second component refers to a case study of outdoor activities routes planning in Beijing, which can help plan the routes for outdoor activities based on the airQP-DNN model, and allow users to enter the origin and destination of the route for the optimized path with the minimum accumulated air quality index. The air quality monitoring datasets of Beijing and surrounding cities from April 2014 to April 2015 (over 758,000 records) are used to verify the proposed airQP-DNN model. The experimental results explicitly demonstrate that our proposed model outperforms other commonly used methods in terms of prediction accuracy, including autoregressive integrated moving average model, gradient boosted decision tree, and long short-term memory. Based on the airQP-DNN model, the case study of outdoor activities routes planning is implemented. When the origin and destination are specified, the optimized paths with the minimum accumulated air quality index would be provided, instead of the standard static Dijkstra shortest path. In addition, a Web-GIS-based prototype has also been successfully developed to support the implementation of our proposed model in this research. The success of our study not only demonstrates the value of the proposed airQP-DNN model, but also shows the potential of our model in other possible extended applications. |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Cai, Tao |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Cao, Kai |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 8876 |
| Host Itemnumber | 17104 |
| Place, publisher, and date of publication | London Pion Ltd. 2010 |
| Title | Environment and planning B: planning and design (Urban Analytics and City Science) |
| International Standard Serial Number | 1472-3417 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/2399808319862292 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | E-Journal |
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| 700 ## - Added Entry Personal Name | |
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| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
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