Streamflow forecasting using machine learning and remote sensing data in the Himalayan region
Naresh Suwal, Rajesh Khatakho, Aditya Nath Jha, Swagato Biswas Ankon, Manoj Lamichhane, Alban Kuriqi
Abstract
ABSTRACT This study aimed to predict daily runoff using three machine learning models: artificial neural networks (ANNs), random forests (RFs), and extreme gradient boosting (XGBoost). Thirty-five years of discharge records and remote-sensing climate data were used to develop and validate the models, demonstrating the potential of satellite data for machine-learning-based runoff prediction, especially in data-poor regions like Nepal. The study identified 1-day lagged discharge (lag_1), average temperature, and maximum temperature as the three most important input variables. 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 effective and sustainable water management.