期刊论文

Zhang, CX

英文

Journal of Hydrology

0022-1694

2024

637

131434

10.1016/j.jhydrol.2024.131434

CRediT authorship contribution statement Heng Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, acquisition, Formal analysis, Data curation, Conceptualization. Chunxiao Zhang: Writing – review & editing, Supervision, Resources, Methodology, acquisition, Conceptualization. Wenhao Chu: Writing – original draft, Methodology, Data curation. Dingtao Shen: Writing – review & editing, Resources, acquisition, Conceptualization. Rongrong Li:

本校为其他机构

Although deep learning (DL) models, especially long-short-term memory (LSTM), demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the predictions from process-based models are more physical than DL models. The main reason is that DL models have almost no process understanding capabilities like process-based models beyond their fitting capability. In this study, we developed a process-driven DL model under a unified DL architecture to improve the process awareness of DL models. To implement the model, a conceptual hydrological model (EXP-HYDRO) is implanted int...