Applications of artificial intelligence technology in the atmospheric environment field

LIANG Changde; YIN Min; HU Qing; YOU Yong; HUANG Qianhui; XU Shengbin

doi:10.13205/j.hjgc.202504010

Volume 43 Issue 4

Apr. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(4): 98-109

LIANG C D，YIN M，HU Q，et al.Applications of artificial intelligence technology in the atmospheric environment field［J］.Environmental Engineering，2025，43（4）：98-109. doi: 10.13205/j.hjgc.202504010

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Applications of artificial intelligence technology in the atmospheric environment field

doi: 10.13205/j.hjgc.202504010

1. Shenzhen Ecological Environment Intelligent Management and Control Center, Shenzhen 518000, China;
2. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China

Received Date: 2024-06-27
Accepted Date: 2024-10-22
Rev Recd Date: 2024-08-22
Publish Date: 2025-04-01

Abstract

Abstract

In recent years， the rapid development of artificial intelligence technology has attracted the attention of researchers in most fields， and fruitful achievements have been made in agriculture， climate， security， and the environment. Firstly， the review summarized the basic principles， technical characteristics， and application scope of artificial intelligence technologies such as fuzzy logic， genetic algorithms， artificial neural networks， XGBoost， LightGBM， etc. Moreover， it focused on the technical advancements and advantages of hybrid intelligent systems which combined artificial neural networks and different artificial intelligence technologies. Secondly， taking the field of atmospheric environment as an example， the application progress of artificial intelligence technology in environmental fields was investigated. Especially， by summarizing the common input parameter types and performance evaluation indexes for the prediction of different air pollutants， the prediction applications in the field of atmospheric environment of single artificial intelligence models and hybrid artificial intelligence models were illustrated with specific cases. Generally， artificial model prediction accuracy is affected by various factors， including the input parameters and model types. Compared to single neural network models， hybrid intelligent models have a relatively higher predictive performance. Finally， the challenges of artificial intelligence in the atmospheric environment field were analyzed from environmental element data sets， model training methods， and algorithm defects. In the future， the applications of artificial intelligence technology in the atmospheric environment field should be improved from the following aspects： hybrid intelligent system combinations， comprehensive analysis of complicated environmental elements， and cooperative working with environmental protection platforms.
- artificial intelligence,
- atmospheric environmental field,
- fuzzy logic,
- genetic algorithm,
- artificial neural network

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