Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain

Abstract

Random Search optimizes hyperparameters of regional LSTM networks for accurate hourly predictions across 40 flashy catchments in Basque Country, Spain. The study achieves high NSE and KGE scores for two different targets, highlighting significant differences between two optimized network architectures and emphasizing the importance of tailored configurations for regional hydrological modeling. • A systematic method for optimizing regional hydrological LSTMs. • An efficient approach to identify optimal network configurations. • Distinct configurations show statistically significant different performance metrics. • Thoughtful configuration selection post-random search in regional hydrology. • Highly accurate hourly streamflow and water level predictions, up to 0.97 NSE/KGE. This paper introduces a novel approach for hyperparameter optimization of long short-term memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level predictions in the realm of regional rainfall-runoff modeling. Leveraging simultaneous systematic hyperparameter optimization of 10 distinct hyperparameters by Random Search, the study achieves high accuracy in terms of predictions across 40 humid flashy catchments in Basque Country, north of Spain. By carefully designing the search space and incorporating domain expertise, the approach quickly converges to optimal and highly accurate network configurations with both efficiency and efficacy. LSTMs ingested precipitation, temperature, and potential evapotranspiration as inputs to predict 2 targets of streamflow and water level, in an hourly timestep. On the test set, the optimized LSTM networks accurately predicted streamflow and water level with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) efficiencies as high as 0.97, in one of the catchments. Across all 40 studied catchments, the overall average NSE and KGE values for streamflow were 0.89 and 0.87, respectively; water level exhibited average NSE and KGE scores of 0.91 and 0.92. Moreover, statistical analysis reveals significant differences in the performance of the 2 distinct optimized network architectures in different hydrological catchments, underscoring the importance of deliberate network configuration selection post-random search. This selection process is vital for achieving higher performance in as many catchments as possible. The findings highlight opportunities for enhancing the “learning maturity” of regional hydrological deep learning LSTM networks. This research provides valuable insights for researchers and practitioners involved in optimizing regional hydrological deep learning models for a variety of applications and on new datasets.