Streamflow prediction is essential for water resources management, flood forecasting, and climate resilience. Long short-term memory (LSTM) networks have advanced large-sample hydrology through cross-basin learning, but their recurrent architectures have limited ability to capture long-range temporal dependencies, part...
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, tracking soil moisture and soil–atmosphere interactions, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error...
Qi Cheng, Licheng Liu, Yi-Xuan Chen et al.· Proceedings of the 32nd ACM...· 3 citations
Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, tracking soil moisture and soil–atmosphere interactions, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error...
Qi Cheng, Licheng Liu, Yixuan Chen et al.· Proceedings of the 32nd ACM...· 0 citations
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