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SCIENTIFIC BASIS FOR ASSESSING THE PSKEM RIVER FLOW BASED ON VARIOUS FORECASTING MODELS

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Hydrometeorology and environmental monitoring

Hydrometeorology and environmental monitoring

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ARTICLE ANNOTATION

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This article explores the possibilities of forecasting water discharge in the Piskom River basin based on meteorological data using Machine Learning (ML) models. The study establishes relationships between the Piskom River flow and meteorological factors using Random Forest, XGBoost, and LSTM models, with their accuracy compared through various evaluation metrics (MAE, RMSE, R², and NSE). The analysis demonstrates that the Random Forest model provides the highest accuracy in forecasting the water discharge of the Piskom River. The research results indicate that ML models can serve as an effective tool for preliminary assessment of river flow and water resource management.

AUTHORS

D.Turgunov

Научно-исследовательский гидрометеорологический институт,

U.Balkhiev

Научно-исследовательский гидрометеорологический институт,

K.Gofurjonov

Научно-исследовательский гидрометеорологический институт,

Tags

# расход воды# сув сарфи# Machine Learning# river basin# дарё ҳавзаси# дарё оқими# речной сток# river flow# meteorological factors# речной бассейн# метеорологик омиллар# метеорологические факторы# random forest# LSTM# XGBoost# прогнозлаш аниқлиги.# модели прогноза речного стока# точность прогноза.# water discharge forecasting# river flow forecasting models# forecasting accuracy.

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Article Identifiers

ROI:

https://eroi.uz/11.0191/A0126-0006

DOI:

Not Available

References

Шульц В.Л., Машрапов Р. Ўрта Осиё гидрографияси. – Тошкент: Ўқитувчи, 1969. – 327 б.

Nishonov B. E., Abdurakhmanov, M. M. Evaluation of ERA5 reanalysis data with observed data in the Akhangaran River Basin // Hydrometeorology and Environmental Monitoring, 2025. №1. – PP. 28-38.

Kratzert F., Klotz D., Brenner C., Schulz K., Herrnegger M. Rainfall–runoff modelling using long short-term memory (LSTM) networks // Hydrology and Earth System Sciences, 2023. №2. – PP. 19-48.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel V., Thirion B., Grisel O., Duchesnay É. Machine Learning in Python // Journal of Machine Learning Research, 2021. №5. – PP. 23-39.

Электрон ресурслар: UN “World Population Prospects 2022”. URL: https://www.un.org

Machine Learning Tutorial. URL: https://www.geeksforgeeks.org/machine-learning

XGBoost Tutorials. URL: https://xgboost.readthedocs.io/en/stable/tutorials/model.html

LSTM Tutorials. URL: https://scikit-learn.org/stable/modules/ensemble.html

Шульц В.Л., Машрапов Р. Ўрта Осиё гидрографияси. – Тошкент: Ўқитувчи, 1969. – 327 б.

Nishonov B. E., Abdurakhmanov, M. M. Evaluation of ERA5 reanalysis data with observed data in the Akhangaran River Basin // Hydrometeorology and Environmental Monitoring, 2025. №1. – PP. 28-38.

Kratzert F., Klotz D., Brenner C., Schulz K., Herrnegger M. Rainfall–runoff modelling using long short-term memory (LSTM) networks // Hydrology and Earth System Sciences, 2023. №2. – PP. 19-48.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel V., Thirion B., Grisel O., Duchesnay É. Machine Learning in Python // Journal of Machine Learning Research, 2021. №5. – PP. 23-39.

Электрон ресурслар: UN “World Population Prospects 2022”. URL: https://www.un.org

Machine Learning Tutorial. URL: https://www.geeksforgeeks.org/machine-learning

XGBoost Tutorials. URL: https://xgboost.readthedocs.io/en/stable/tutorials/model.html

LSTM Tutorials. URL: https://scikit-learn.org/stable/modules/ensemble.html