Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal

Abstract

Abstract Accurate simulation and stream flow prediction are essential for effective water resources planning and management. Hydrological models have long been used for rainfall-runoff modeling; in recent years, with the availability of large datasets, machine learning algorithms, and deep learning techniques have become powerful tools to explore. This study compares rainfall-runoff simulation between the Long and Short-Term Memory (LSTM) (a deep learning model) and the Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS) (a conceptual model) for a steep mountainous catchment in Nepal. During calibration (1989–2006) and validation (2007–2016), the HEC-HMS model demonstrated a root mean square error (RMSE) and Nash Sutcliffe Efficiency (NSE) of 18.3 and 0.73 and 22.93 and 0.66, respectively, for the calibration and validation periods. During the same periods, the LSTM model showed values of 7.25 and 0.87 and 19.59 and 0.64, respectively, for the calibration and validation periods. The performance indices show that the LSTM model has performed better than the HEC-HMS model. The outcomes of this study suggest that the LSTM deep learning model can simulate the discharge successfully across steep mountainous catchments characterized by monsoon and snowmelt runoff, even with a wide range of precipitation variation, making it an asset for hydrological modeling and water resources management in the data-scarce region.

Keywords

• LSTM
• HEC-HMS
• Machine learning
• Rainfall-runoff simulation
• Mountainous river basin