Liangkun Deng
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View article: Challenges and opportunities of ML and explainable AI in large-sample hydrology
Challenges and opportunities of ML and explainable AI in large-sample hydrology Open
Machine learning (ML) is a powerful tool for hydrological modelling, prediction, dataset creation and the generation of insights into hydrological processes. As such, ML has become integral to the field of large-sample hydrology, where hun…
View article: Decoding Flow-Ecology Relationships: A Machine learning framework for flow regime Characterization and riparian vegetation prediction
Decoding Flow-Ecology Relationships: A Machine learning framework for flow regime Characterization and riparian vegetation prediction Open
View article: Towards learning human influences in a highly regulated basin using a hybrid DL-process based framework
Towards learning human influences in a highly regulated basin using a hybrid DL-process based framework Open
Hybrid models have shown impressive performance for streamflow simulation, offering better accuracy than process-based hydrological models (PBMs) and superior interpretability than deep learning models (DLMs). A recent paradigm for streamf…