Application and development of artificial intelligence in environmental health

Research Article
Open access

Application and development of artificial intelligence in environmental health

Tong Wu 1*
  • 1 Guangzhou experiment high school, Guangzhou, 510055, China    
  • *corresponding author tonywutong0715@163.com
Published on 21 July 2023 | https://doi.org/10.54254/2755-2721/7/20230335
ACE Vol.7
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-61-4
ISBN (Online): 978-1-915371-62-1

Abstract

In recent years, major industries have been hit hard by the COVID-19 pandemic, and people have become more aware of issues such as environmental health. The development of computer programs, artificial intelligence (AI), has gradually entered the vision of researchers. The product of this technology can help people better manage and improve the current environmental conditions. This paper, through a method of literature review, aims to explain the development history of AI and its application in different fields such as the environment. Through the research, the paper concludes that artificial intelligence can observe in real time for long periods of time without getting tired like humans do. Based on the application of big data analysis and intelligent programs, artificial intelligence can make reasonable plans in a much shorter time than human beings. However, the solution is only based on the operation of the system. In the actual situation, there will be many obstacles. Therefore, it is still necessary for humans to make judgments based on the results of an operation rather than relying entirely on artificial intelligence. Complicated weather conditions can also make the results of the calculation inaccurate. So there will still be a need to evolve computer programs or for artificial intelligence to be able to calibrate the data it is monitoring from the past.

Keywords:

artificial intelligence, environmental science, wireless sensor network, water quality analysis

Wu,T. (2023). Application and development of artificial intelligence in environmental health. Applied and Computational Engineering,7,35-40.
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References

[1]. Andrianto, H., & Faizal, A. (2020, October). Development of smart greenhouse system for hydroponic agriculture. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI) (pp. 335-340). IEEE

[2]. .Dwivedi, A. K. (2017). Researches in water pollution: A review. International Research Journal of Natural and Applied Sciences, 4(1), 118-142.

[3]. van Vliet, M. T., Jones, E. R., Flörke, M., Franssen, W. H., Hanasaki, N., Wada, Y., & Yearsley, J. R. (2021). Global water scarcity including surface water quality and expansions of clean water technologies. Environmental Research Letters, 16(2), 024020.

[4]. Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The lancet, 360(9341), 1233-1242.

[5]. Xiang, X.J., Li, Q., Khan, S., & Khalaf, O.I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86, 106515.

[6]. Liu Zhiyong, Yang Chen, Wu Yang & Guo Shuxin.(2022). Water environment analysis platform based on artificial intelligence -- Zhongtai Zhizhou. Internet of Things Technology (07),3-4

[7]. Zhang C, Tang W & Xiao J (2020). Development and application of air quality prediction system based on artificial intelligence. Chinese Research Academy of Environmental Sciences (43), 189-191

[8]. Sun, Y., Xu, X., Yu, H., & Wang, H. (2022). Impact of value co‐creation in the artificial intelligence innovation ecosystem on competitive advantage and innovation intelligibility. Systems Research and Behavioral Science, 39(3), 474-488.

[9]. Jennings, N. R. (2000). On agent-based software engineering. Artificial intelligence, 117(2), 277-296

Cite this article

Wu,T. (2023). Application and development of artificial intelligence in environmental health. Applied and Computational Engineering,7,35-40.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2023), Part II

ISBN：978-1-915371-61-4(Print) / 978-1-915371-62-1(Online)
Editor：Ioannis Spanopoulos, Niaz Ahmed, Sajjad Seifi Mofarah
Conference website: https://www.confmcee.org/
Conference date: 18 March 2023
Series: Applied and Computational Engineering
Volume number: Vol.7
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Andrianto, H., & Faizal, A. (2020, October). Development of smart greenhouse system for hydroponic agriculture. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI) (pp. 335-340). IEEE

[2]. .Dwivedi, A. K. (2017). Researches in water pollution: A review. International Research Journal of Natural and Applied Sciences, 4(1), 118-142.

[3]. van Vliet, M. T., Jones, E. R., Flörke, M., Franssen, W. H., Hanasaki, N., Wada, Y., & Yearsley, J. R. (2021). Global water scarcity including surface water quality and expansions of clean water technologies. Environmental Research Letters, 16(2), 024020.

[4]. Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The lancet, 360(9341), 1233-1242.

[5]. Xiang, X.J., Li, Q., Khan, S., & Khalaf, O.I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86, 106515.

[6]. Liu Zhiyong, Yang Chen, Wu Yang & Guo Shuxin.(2022). Water environment analysis platform based on artificial intelligence -- Zhongtai Zhizhou. Internet of Things Technology (07),3-4

[7]. Zhang C, Tang W & Xiao J (2020). Development and application of air quality prediction system based on artificial intelligence. Chinese Research Academy of Environmental Sciences (43), 189-191

[8]. Sun, Y., Xu, X., Yu, H., & Wang, H. (2022). Impact of value co‐creation in the artificial intelligence innovation ecosystem on competitive advantage and innovation intelligibility. Systems Research and Behavioral Science, 39(3), 474-488.

[9]. Jennings, N. R. (2000). On agent-based software engineering. Artificial intelligence, 117(2), 277-296

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