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Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models

Author

Listed:
  • Basir Ullah

    (University of Engineering and Technology Peshawar)

  • Muhammad Fawad

    (University of Engineering and Technology Peshawar)

  • Afed Ullah Khan

    (University of Engineering and Technology Peshawar)

  • Sikander Khan Mohamand

    (University of Engineering and Technology Peshawar)

  • Mehran Khan

    (University of Engineering and Technology Peshawar
    University of Engineering and Technology)

  • Muhammad Junaid Iqbal

    (University of Engineering and Technology Peshawar)

  • Jehanzeb Khan

    (Higher Education Department KPK)

Abstract

Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This paper aims to evaluate four machine learning (ML) algorithms, namely, Long Short-Term Memory (LSTM), Regression Tree, AdaBoost, and Gradient Boosting algorithms, to predict the futuristic streamflow of the Swat River basin. Ten General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) 245 and 585 were used for futuristic streamflow assessment. The ML models were developed using maximum temperature, minimum temperature, and precipitation as the input variables while streamflow as the target variable. The performance of ML models was assessed via statistical performance indicators, namely the coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). The AdaBoost exhibits exceptional performance (R2: 0.99 during training, 0.86 during testing). The futuristic streamflow projection shows an increase in mean annual streamflow between 2050 and 2080 s from 3.26 to 7.52% for SSP245 and 3.77–13.55% for SSP585. ML models, notably adaboost, provide a reliable method for projecting streamflow, will assist in hazard and water management in the area.

Suggested Citation

  • Basir Ullah & Muhammad Fawad & Afed Ullah Khan & Sikander Khan Mohamand & Mehran Khan & Muhammad Junaid Iqbal & Jehanzeb Khan, 2023. "Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 6089-6106, December.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:15:d:10.1007_s11269-023-03645-3
    DOI: 10.1007/s11269-023-03645-3
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    References listed on IDEAS

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    1. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
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    Cited by:

    1. Malihe Danesh & Amin Gharehbaghi & Saeid Mehdizadeh & Amirhossein Danesh, 2025. "A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1911-1930, March.
    2. Divya Chandran & N. R. Chithra, 2025. "Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1235-1259, February.

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    Keywords

    Streamflow; Prediction; Machine Learning; CMIP6;
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