Use of machine learning techniques in predicting inflow in Tarbela reservoir of Upper Indus Basin

Authors

  • SHABIR JAN Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • UZAIR KHAN Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • ALAMGIR KHALIL Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • AMJAD ALI KHAN Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • HABIB AHMAD JAN Department of Civil Engineering, University of Engineering and Technology, Peshawar, Pakistan
  • IBAD ULLAH Institute of Chemical Sciences, University of Peshawar 25120, Pakistan

DOI:

https://doi.org/10.54386/jam.v26i4.2676

Keywords:

XGBoost, Random Forest, Machine learning, Model Performance, Inflow prediction

References

Event Analysis (2023). Floods in Swat Valley, Pakistan - Pakistan | ReliefWeb. (n.d.). Retrieved 25 May 2024, from https://reliefweb.int/report/pakistan/event-analysis-2022-floods-swat-valley-pakistan

Gupta, A., and Kumar, A. (2022). Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models. J. Hydro-Environ. Res., 45, 39–52. https://doi.org/10.1016/J.JHER.2022.10.002

Khan, F. (2022). Water availability and response of Tarbela Reservoir under the changing climate in the Upper Indus Basin, Pakistan. Sci. Reps., 12(1), 15865. https://doi.org/10.1038/s41598-022-20159-x

Kumar, V., Kedam, N., Sharma, K. V., Mehta, D. J., and Caloiero, T. (2023). Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models. Water (Switzerland), 15(14). https://doi.org/10.3390/w15142572

Rajesh, M., Indranil, P., & Rehana, S. (2022). Reservoir Inflow Forecasting Based On Gradient Boosting Regressor Model — A Case Study Of Bhadra Reservoir, India. 18th Annu. Meet. Asia Oceania Geosci. Soc, 64–66. https://doi.org/10.1142/9789811260100_0022

Sakthipriya, D., & Thangavel, C. (2024). Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed. J. Agrometeorol., 26(2): 209–214. https://doi.org/10.54386/jam.v26i2.2553

Shrivastav, L. K., and Kumar, R. (2021). An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction. J. Inf. Technol. Res, 15(1), 1–19. https://doi.org/10.4018/JITR.2022010102

Sridhara, S., Soumya B. R., & Kashyap, G. R. (2024). Multistage sugarcane yield prediction using machine learning algorithms. J. Agrometeorol., 26(1): 37–44. https://doi.org/10.54386/jam.v26i1.2411

Tarbela Dam Project (2023). Tarbela Dam Project, Haripur District, Pakistan. (n.d.). Retrieved 25 May 2024, from https://www.water-technology.net/projects/tarbela-dam-project/

Wang, M., Feng, D., Li, D., & Wang, J. (2022). Reservoir Parameter Prediction Based on the Neural Random Forest Model. Front Earth Sci, 10. https://doi.org/10.3389/feart.2022.888933

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Published

01-12-2024

How to Cite

JAN, S., KHAN, U., KHALIL, A., KHAN, A. A., JAN, H. A., & ULLAH, I. (2024). Use of machine learning techniques in predicting inflow in Tarbela reservoir of Upper Indus Basin. Journal of Agrometeorology, 26(4), 501–504. https://doi.org/10.54386/jam.v26i4.2676

Issue

Vol. 26 No. 4 (2024): December

Section

Short Communication

Categories

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Copyright (c) 2024 SHABIR JAN, UZAIR KHAN, ALAMGIR KHALIL, AMJAD ALI KHAN, HABIB AHMAD JAN, IBAD ULLAH

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