Streamflow Forecasting using Genetic Programming, LSTM, and Hybrid Meta-Learning GP-LSTM model in Monsoon-Dominated Basins

Accurate streamflow prediction remains challenging in monsoon-dominated basins characterized by extreme flow variability. This study evaluated three machine learning approaches for daily streamflow forecasting using 39 years of data (1980-2018) of the Basantpur station, Mahanadi basin, India from the CAMELS-India dataset which are (1) Optuna- optimized Genetic Programming (GP) for interpretable symbolic regression, (2) Optuna- optimized bidirectional LSTM networks, and (3) a novel GP-LSTM meta-learning framework that predicts optimal hyperparameters from time series statistical features.Analysis of highly skewed flow distributions (97.66% of values