Streamflow forecasting using machine learning and remote sensing data in the Himalayan region

This study aimed to predict daily runoff using three machine learning models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost). Thirty-five years of river discharge data and remote sensing climate datasets were used for model development and validation. Based on six metrics, the ANN performed best, closely followed by XGBoost. In addition, the study identified 1-day lagged discharge (lag_1), average temperature, and maximum temperature as the three most important input variables. Scientifically, this study extends the applicability of satellite RS data in ML methods for predicting runoff, especially in data-poor regions such as Nepal. Practically, the developed ANN model demonstrates high accuracy in streamflow prediction, achieving an R2 of 0.928, a root mean square error (RMSE) of 435.43 m3/s, a Nash–Sutcliffe efficiency (NSE) of 0.93, and a Kling–Gupta efficiency (KGE) of 0.944 for the Narayani River Basin. When applied to the Trishuli River, a major tributary of the Narayani, the model demonstrated strong performance, with an R2 of 0.895, an RMSE of 425.92 m3/s, an NSE of 0.89, and a KGE of 0.908. Nonetheless, RF outperformed ANN in capturing low-flow conditions in the Narayani River. Overall, the models provide valuable tools for retrospective streamflow prediction, gap-filling in hydrological records, and supporting sustainable water management.