Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework

• Published in: Atmosphere
• Main Authors: Achite, Mohammed, Mohammadi, Babak, Jehanzaib, Muhammad, Elshaboury, Nehal, Pham, Quoc Bao, Duan, Zheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: Water Engineering; Physical Geography; conceptual hydrological model; data-driven model; deep learning model; runoff simulation; Kalixälven; Northern Sweden
• Online Access: https://lup.lub.lu.se/record/db814ea2-2ac4-4cf7-8618-07f0857f6979; https://doi.org/10.3390/atmos13101688

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterp...