SUN'IY INTELLEKT MODELLARIDAN FOYDALANGAN HOLDA YER OSTI SUV HAVZALARI MA'LUMOTLARINI BASHORATLASH

Mualliflar

  • Dadajonova Zilola Botirjon qizi Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti assistenti, Ilmiy tadqiqotlar va texnologiyalar transferi, inkubatsiya va akseleratsiya bo'limi boshlig'i
  • Maxmudjanov Sarvar Ulugbekovich Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti dotsenti
  • Nurmurodov Javohir Nurmurod o'g'li Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti

DOI:

https://doi.org/10.69891/

Kalit so‘zlar:

yer osti suvlari sun'iy intellekt bashoratlash gidrogeologik ma'lumotlar mashinali oʻqitish monitoring maʼlumotlar tahlili

Abstrak

Mazkur maqolada yer osti suv havzalari maʼlumotlarini sun'iy intellekt modellari yordamida bashorat qilish masalalari atroflicha ko'rib chiqilgan. Tadqiqotda yer osti suvlarining gidrogeologik va gidrodinamik koʻrsatkichlari asosida maʼlumotlarni tahlil qilish hamda ularning kelajakdagi oʻzgarishlarini aniqlash usullari chuqur oʻrganilgan. Sun'iy intellekt modellaridan, xususan, Transformer, LSTM va GRU kabi ilg'or arxitekturalardan foydalanish murakkab gidrogeologik jarayonlar oʻrtasidagi bogʻliqliklarni aniqlash va bashorat natijalarining aniqligini sezilarli darajada oshirish imkonini beradi. Ushbu modellar uzoq muddatli va qisqa muddatli dinamik o'zgarishlarni, jumladan, mavsumiy sikllar, antropogen ta'sirlar va yog'ingarchilik-oqim-suv sathi-gidrokimyoviy tarkib zanjirlarini hisobga olishga qodir. Tadqiqot natijalari yer osti suv resurslarini monitoring qilish, ularni samarali boshqarish, ekologik muammolarni oldini olish hamda qaror qabul qilish tizimlarini takomillashtirishda muhim amaliy ahamiyatga ega bo'lishi mumkin. Shuningdek, kelajakda turli sun'iy intellekt modellarini birgalikda qo'llash va bir nechta manbalardan ma'lumotlarni integratsiya qilish bashorat natijalarining ishonchliligini oshirishga xizmat qiladi.

References

Haykin S. Neural Networks and Learning Machines. — 3rd ed. — New York: Pearson Education, 2009. — pp. 1–936.

Goodfellow I., Bengio Y., Courville A. Deep Learning. — Cambridge: MIT Press, 2016. — pp. 1–775.

Bishop C.M. Pattern Recognition and Machine Learning. — New York: Springer, 2006. — pp. 1–738.

Russell S., Norvig P. Artificial Intelligence: A Modern Approach. — 4th ed. — Pearson Education, 2021. — pp. 1–1168.

Géron A. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. — Sebastopol: O'Reilly Media, 2022. — pp. 1–851.

Chollet F. Deep Learning with Python. — New York: Manning Publications, 2021. — pp. 1–504.

Han J., Kamber M., Pei J. Data Mining: Concepts and Techniques. — Waltham: Morgan Kaufmann, 2011. — pp. 1–703.

Z.B. Dadajonova, B.D. Abdullaev, N. Zaynidinov, B.B. Akbaraliev, B.R. Nasibov. Intellectual processing of hydrogeological data (using the example of groundwater) // British Journal of Global Ecology and Sustainable Development. — 2024.— Vol. 32. — pp. 1–8.

Dadajonova Z.B., Abdullayev B.D., Nasibov B.R., Dadajonov B.B. Monitoring of changes in groundwater quality in irrigated lands of Kashkadarya region // Bulletin of the University of Geological Sciences. — 2024. — No. 2. — pp. 1–8.

Dadajonova Z.B., Nurmurodov J.N. Classification of deposits with underground resources using artificial intelligence technologies // Al-Fargʻoniy avlodlari electronic scientific journal. — 2025. — Vol. 1. — Issue 2.

Dadajonova Z.B. Predicting groundwater level changes using an artificial intelligence model // Scientific article. — 2026.

Zaynidinov H.N., Qobilov S.Sh. A two-dimensional hydrogeological data modeling program based on a spline model independent of nodes. — 2024.

Aggarwal C.C. Neural Networks and Deep Learning. — Cham: Springer, 2018. — pp. 1–497.

Murphy K.P. Machine Learning: A Probabilistic Perspective. — Cambridge: MIT Press, 2012. — pp. 1–1067.

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. — New York: Springer, 2009. — pp. 1–745.

Maier H.R., Jain A., Dandy G.C., Sudheer K.P. Methods used for the development of neural networks for the prediction of water resource variables // Environmental Modelling & Software. — 2010. — Vol. 25. — No. 8. — pp. 891–909.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial Neural Networks in Hydrology // Journal of Hydrologic Engineering. — 2000. — Vol. 5. — No. 2. — pp. 115–123.

Kratzert F., Klotz D., Brenner C., Schulz K., Herrnegger M. Rainfall-runoff modelling using Long Short-Term Memory networks // Hydrology and Earth System Sciences. — 2018. — Vol. 22. — pp. 6005–6022.

Shalev-Shwartz S., Ben-David S. Understanding Machine Learning: From Theory to Algorithms. — Cambridge: Cambridge University Press, 2014. — pp. 1–410.

Abbot M.B., Refsgaard J.C. Distributed Hydrological Modelling. — Dordrecht: Springer, 1996. — pp. 1–336.

Ian H.W., Eibe F., Hall M.A. Data Mining: Practical Machine Learning Tools and Techniques. — Burlington: Morgan Kaufmann, 2016. — pp. 1–654.

Kelleher J.D., Tierney B. Data Science. — Cambridge: MIT Press, 2018. — pp. 1–280.

LeCun Y., Bengio Y., Hinton G. Deep Learning // Nature. — 2015. — Vol. 521. — pp. 436–444.

Schmidhuber J. Deep Learning in Neural Networks: An Overview // Neural Networks. — 2015. — Vol. 61. — pp. 85–117.

Hochreiter S., Schmidhuber J. Long Short-Term Memory // Neural Computation. — 1997. — Vol. 9. — No. 8. — pp. 1735–1780.

Downloads

Nashr qilingan

2026-06-22

How to Cite

SUN’IY INTELLEKT MODELLARIDAN FOYDALANGAN HOLDA YER OSTI SUV HAVZALARI MA’LUMOTLARINI BASHORATLASH. (2026). Universal Xalqaro Ilmiy Jurnal, 3(6), 49-63. https://doi.org/10.69891/