2026: Proceedings of MMIT'26 International Conference
487-493
2026-06-06
Open Access

EFFECTIVE ARTIFICIAL INTELLIGENCE ALGORITHMS IN ENVIRONMENTAL MONITORING

Negmatov Ulug‘bek, Isomaddinov Usmonali, Murodjon Xasanov, Tursunov Ibrohimjon
https://doi.org/10.61587/

Received: 2026-05-30

Published: 2026-06-06

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Abstract

This article analyzes the theoretical and practical aspects of using artificial intelligence algorithms in environmental monitoring. Under digitalization, the high cost, long duration, and relatively low accuracy of traditional observation methods increase the need for rapid and reliable processing of environmental data. The authors show that artificial intelligence can be used to monitor water, air, soil, and natural hazards, as well as to determine pollutant concentrations. The study compares SVM, SVR, decision trees, random forests, RNN, CNN, and hybrid models and evaluates their strengths and weaknesses. It also identifies challenges related to data quality, computing resources, model interpretability, security, generalization capability, and energy consumption. The article concludes that effective artificial intelligence solutions in environmental monitoring should be regarded not only as a technical achievement but also as a comprehensive management tool that contributes to environmental sustainability.

Keywords

List of references

  1. The Fourth Industrial Revolution. What It Means and How to Respond. By Klaus Schwab. December 12, 2015. URL: https://www.foreignaffairs.com/world/fourth-industrial-revolution (дата звернення: 14.02.2025).

  2. Hameed, M., Sharqi, S.S., Yaseen, Z.M., Afan, H.A., Hussain, A., Elshafie, A. Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Comput. Appl. 2017. № 28. S. 893–905. DOI: https://doi.org/10.1007/s00521-016-2404-7.

  3. Li, Y., Feng, Z., Chen, S., Zhao, Z., Wang, F. Application of the artificial neural network and support vector machines in forest fire prediction in the Guangxi autonomous region, China. Discrete Dyn. Nat. Soc. 2020. № 1. S. 1–14. DOI: https://doi.org/10.1155/2020/5612650.

  4. Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., Kycko, M. Comparison of random forest, support vector machines, and neural networks for post-disaster forest species mapping of the krkonoše/karkonosze transboundary biosphere reserve. Remote Sens. 2021. № 13(13), 2581. DOI: https://doi.org/10.3390/rs13132581.

  5. Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., Wang, J. Tackling environmental challenges in pollution controls using artificial intelligence: A review. Sci. Total Environ. 2020. № 699:134279. DOI: https://doi.org/10.1016/j.scitotenv.2019.134279.

  6. El-Magd, S.A.A., Soliman, G., Morsy, M., Kharbish, S. Environmental hazard assessment and monitoring for air pollution using machine learning and remote sensing. Int. J. Environ. Sci. Technol. 2022. № 20 (6), S. 6103–6116. DOI: https://doi.org/10.1007/s13762-022-04367-6.

  7. Zhang, H., Zhang, L., Wang, S., Zhang, L. Online water quality monitoring based on UV–Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon. Environ. Monit. Assess. 2022. № 194:630. DOI: https://doi.org/10.1007/s10661-022-10118-4.

  8. Ghorbani, Z., Behzadan, A.H. Monitoring offshore oil pollution using multi-class convolutional neural networks. Environ. Poll. 2021. № 289:117884. DOI: https://doi.org/10.1016/j.envpol.2021.117884.

  9. Almalawi, A., Alsolami, F., Khan, A.I., Alkhathlan, A., Fahad, A., Irshad, K., Qaiyum, S., Alfakeeh, A.S. An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique. Environ. 2022. № 206:112576. DOI: https://doi.org/10.1016/j.envres.2021.112576.

  10. Rowley, A., Karakus¸, O. Predicting air quality via multimodal AI and satellite imagery. Remote Sens. Environ. 2023. № 293:113609. DOI: https://doi.org/10.1016/j.rse.2023.113609.

  11. Baniecki H., Biecek, P. Adversarial attacks and defenses in explainable artificial intelligence: a survey. Information Fusion. 2024. № 107(5):102303. DOI: https://doi.org/10.1016/j.inffus.2024.102303.

  12. Sarker, I.H. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science. 2020. № 3:158. DOI: https://doi.org/10.1007/s42979-022-01043-x.

  13. Kernbach, J.M., Staartjes, V.E. (2022). Foundations of machine learning-based clinical prediction modeling: part II–Generalization and overfitting. In: Staartjes, V.E., Regli, L., Serra, C. (eds). Machine Learning in Clinical Neuroscience: Acta Neurochirurgica Supplement. 2022. № 134. S. 15–21. DOI: https://doi.org/10.1007/978-3-030-85292-4_3.

  14. Ryo, M., Angelov, B., Mammola, S., Kass, J.M., Benito, B.M., Hartig, F. Explainable artificial intelligence enhances the ecological interpretability of blackbox species distribution models. Ecography. 2020. № 44 (2). S. 199–205. DOI: https://doi.org/10.1111/ecog.05360.

  15. Bloomfield, P., Clutton-Brock, P., Pencheon, E., Magnusson, J., Karpathakis, K. Artificial Intelligence in the NHS: Climate and Emissions. J. Clim. Change Health. 2021. № 4:100056. DOI: https://doi.org/10.1016/j.joclim.2021.100056.

About the Authors

Negmatov Ulug‘bek, Isomaddinov Usmonali, Murodjon Xasanov, Tursunov Ibrohimjon
murodjon19955@gmail.com
Namangan State Technical University

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How to Cite

EFFECTIVE ARTIFICIAL INTELLIGENCE ALGORITHMS IN ENVIRONMENTAL MONITORING. (2026). MMIT Proceedings, 487-493. https://doi.org/10.61587/

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