Current Situation and Prospect of Geospatial AI in Air Pollution Prediction
Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Fir...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/12/1411 |
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| author | Chunlai Wu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_facet | Chunlai Wu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_sort | Chunlai Wu |
| collection | DOAJ |
| description | Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research. |
| format | Article |
| id | doaj-art-da6693f77b454b009f6c1f4631ecf378 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-da6693f77b454b009f6c1f4631ecf3782025-08-20T02:53:18ZengMDPI AGAtmosphere2073-44332024-11-011512141110.3390/atmos15121411Current Situation and Prospect of Geospatial AI in Air Pollution PredictionChunlai Wu0Siyu Lu1Jiawei Tian2Lirong Yin3Lei Wang4Wenfeng Zheng5School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of KoreaDepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USASchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaFaced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.https://www.mdpi.com/2073-4433/15/12/1411web of sciencecitespacebibliometric analysisneural networkair quality predictionhaze |
| spellingShingle | Chunlai Wu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng Current Situation and Prospect of Geospatial AI in Air Pollution Prediction Atmosphere web of science citespace bibliometric analysis neural network air quality prediction haze |
| title | Current Situation and Prospect of Geospatial AI in Air Pollution Prediction |
| title_full | Current Situation and Prospect of Geospatial AI in Air Pollution Prediction |
| title_fullStr | Current Situation and Prospect of Geospatial AI in Air Pollution Prediction |
| title_full_unstemmed | Current Situation and Prospect of Geospatial AI in Air Pollution Prediction |
| title_short | Current Situation and Prospect of Geospatial AI in Air Pollution Prediction |
| title_sort | current situation and prospect of geospatial ai in air pollution prediction |
| topic | web of science citespace bibliometric analysis neural network air quality prediction haze |
| url | https://www.mdpi.com/2073-4433/15/12/1411 |
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