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The hydro-forecast skill provides a comprehensive framework for hydrological modeling in Julia, integrating traditional conceptual models like MarrMot and Xinanjiang (XAJ) with advanced deep learning architectures such as LSTM, TCN, and KAN. It automates the end-to-end workflow of rainfall-runoff simulation, including flood event identification, dataset partitioning into training and validation sets, and model calibration using metrics like NSE and KGE. Designed for water resource engineers and researchers, it generates detailed evaluation reports and visualizations to assess model accuracy on flood peaks and timing.
