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Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Author

Listed:
  • Subbarayan Saravanan

    (Department of Civil Engineering, National Institute of Technology, Tiruchirappalli 620015, India)

  • Nagireddy Masthan Reddy

    (Department of Civil Engineering, National Institute of Technology, Tiruchirappalli 620015, India)

  • Quoc Bao Pham

    (Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland)

  • Abdullah Alodah

    (Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia)

  • Hazem Ghassan Abdo

    (Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria)

  • Hussein Almohamad

    (Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia)

  • Ahmed Abdullah Al Dughairi

    (Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia)

Abstract

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R 2 ). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R 2 , and RMSE values of 0.95, 0.979, 0.937, and 30.805 m 3 /s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m 3 /s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.

Suggested Citation

  • Subbarayan Saravanan & Nagireddy Masthan Reddy & Quoc Bao Pham & Abdullah Alodah & Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi, 2023. "Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset," Sustainability, MDPI, vol. 15(16), pages 1-26, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12295-:d:1215671
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    References listed on IDEAS

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    1. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
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    4. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.
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    Keywords

    streamflow; CMIP6; machine learning; RF; SVR; MLP; water;
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