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

Subbarayan Saravanan, Nagireddy Masthan Reddy, Quoc Bao Pham, Abdullah Alodah (), Hazem Ghassan Abdo, Hussein Almohamad and Ahmed Abdullah Al Dughairi
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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

Sustainability, 2023, vol. 15, issue 16, 1-26

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.

Keywords: streamflow; CMIP6; machine learning; RF; SVR; MLP; water (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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