Current Condition of Water Resources and Their Saving

Authors

  • Tolaniddin R. Nurmukhammedov Department of Information Systems and Technologies in Transport, Tashkent State Transport University, Tashkent, Uzbekistan
  • Abdulkhay A. Azimov Department of Information Systems and Technologies in Transport, Tashkent State Transport University, Tashkent, Uzbekistan
  • Temur S. Tashmetov Department of Information Systems and Technologies in Transport, Tashkent State Transport University, Tashkent, Uzbekistan

DOI:

https://doi.org/10.51699/emjms.v26i2.1109

Keywords:

water, resource, ecosystem, agriculture, smart use, artificial intelligence

Abstract

The article presents analytical data on water resources in Uzbekistan and other countries. Based on the system of artificial intelligence, opinions were expressed about the preservation of water resources and the ecosystem in it. Considerations on the wise use of water resources using artificial intelligence, deterministic - stochastic models are presented.

References

“Uzbekistan will lack 7 billion cubic meters of water by 2030.” [Online]. Available: https://www.gazeta.uz/uz/2023/04/30/suv/

“II-i avtomaticheskoe upravlenie vodnymi resursami.” [Online]. Available: https://nauchniestati.ru/spravka/ii-i-avtomaticheskoe-upravlenie-vodnymi-resursami/

S. A. Kondratev and M. V. Shmakova, “Determinirovanno-stochasticheskoe modelirovanie kak instrument otsenki stoka i vynosa biogennyx veshchestv s vodosborov pri dekoti dannyx naturnyx nablyudeniy,” in Sovrem. problemy hydrokhimii i monitoringa kachestva poverkhn. vod. Mat-ly nauchn. conf. s mejdunar. I fly. Federal service of hydrometeorology and environmental monitoring. FGBU “Hydrochemical Institute,” 2015, pp. 367–371.

H. Tao, “Groundwater level prediction using machine learning models: A comprehensive review,” Neurocomputing, vol. 489. pp. 271–308, 2022. doi: 10.1016/j.neucom.2022.03.014.

H. Sanikhani, “Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios,” Theoretical and Applied Climatology, vol. 135, no. 1. pp. 449–462, 2019. doi: 10.1007/s00704-018-2390-z.

B. Zhu, “Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data,” Computers and Electronics in Agriculture, vol. 173. 2020. doi: 10.1016/j.compag.2020.105430.

A. Malik, “Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model,” Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1. pp. 323–338, 2020. doi: 10.1080/19942060.2020.1715845.

S. A. Kondratev, Formirovanie vneshney nagruzki na vodeemy: problemy modelirovaniya. SPb.: Nauka, 2007.

S. A. Kondratev and M. V. Shmakova, “Formirovanie vneshney nagruzki na vodeemy: problemy modelirovaniya,” Uchen Zap RGGMU, no. 42, pp. 24–32, 2016.

Tiyasha, “A survey on river water quality modelling using artificial intelligence models: 2000–2020,” Journal of Hydrology, vol. 585. 2020. doi: 10.1016/j.jhydrol.2020.124670.

S. Ghimire, “Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks,” Scientific Reports, vol. 11, no. 1. 2021. doi: 10.1038/s41598-021-96751-4.

W. j Niu, “Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management,” Sustainable Cities and Society, vol. 64. 2021. doi: 10.1016/j.scs.2020.102562.

S. A. Aani, “Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?,” Desalination, vol. 458. pp. 84–96, 2019. doi: 10.1016/j.desal.2019.02.005.

M. E. Mondejar, “Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet,” Science of the Total Environment, vol. 794. 2021. doi: 10.1016/j.scitotenv.2021.148539.

S. Maroufpoor, “Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm,” Journal of Hydrology, vol. 575. pp. 544–556, 2019. doi: 10.1016/j.jhydrol.2019.05.045.

S. Kouadri, “Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast),” Applied Water Science, vol. 11, no. 12. 2021. doi: 10.1007/s13201-021-01528-9.

Z. k Feng, “Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization,” Journal of Hydrology, vol. 583. 2020. doi: 10.1016/j.jhydrol.2020.124627.

S. A. Uvarov, “Principy ekologicheskogo obespecheniya logistiki,” in Materialy 10-y Mejdunarodnoy nauchno-prakticheskoy conference “Logistics - Eurasian bridge,” Krasnoyarsk: Krasnoyar. Mr. agrarian un-t, 2015, pp. 320–325.

M. V. Shmakova, Theory and practice of mathematical modeling of speech flow. SPb.: Lema, 2013.

M. V. Shmakova, “Stochastic model pogody v sisteme determinirovano-stochasticheskogo modelirovaniya characteristic stoka,” Dis. Cand. tech. science, SPb., 2000.

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Published

2024-02-12

How to Cite

Nurmukhammedov, T. R., Azimov, A. A., & Tashmetov, T. S. (2024). Current Condition of Water Resources and Their Saving. European Multidisciplinary Journal of Modern Science, 26(2), 1–5. https://doi.org/10.51699/emjms.v26i2.1109

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Articles