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

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 in the Pranhita subbasin (Godavari basin), India, from 1993 2014. Input parameters were selected using correlation pairwise attribution evaluation methods, incorporating a two-day lag of streamflow, maximum minimum temperatures, various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) utilized process. performance was evaluated Pearson (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient determination (R2). IMD outperformed all CMIP6 modeling, while RF demonstrated best among developed both datasets. During training phase, exhibited NSE, R, R2, RMSE values 0.95, 0.979, 0.937, 30.805 m3/s, respectively, gridded as input. In testing corresponding 0.681, 0.91, 0.828, 41.237 m3/s. The results highlight significance advanced applications, providing valuable insights management decision making.