Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation.

ABSTRACT: Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin

SUBMITTER: Li W 

PROVIDER: S-EPMC11636842 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature