A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model
نویسندگان
چکیده
The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a parameter training framework consisting transformer-based learning model (ParaFormer) and surrogate based LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms learn global mapping from data be calibrated, which captures spatial correlations. other takes calibrated as inputs simulates observable such soil moisture, overcoming challenges directly combining complex with deep (DL) platform hybrid scheme. Using variable infiltration capacity reference, test performance datasets different resolutions. results demonstrate that, predicting moisture transferring task evapotranspiration prediction, learns more robust patterns compared state-of-the-art DL-based methods.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2072-4292']
DOI: https://doi.org/10.3390/rs15143536