Enhancing soil moisture prediction via a physics-informed xLSTM with multi-variate temporal dynamics

Accurate characterization of soil moisture (SM) is paramount for elucidating complex hydrological processes. To mitigate predictive uncertainties arising from the non-linearity of soil water movement and stochastic boundary conditions, this study presents xLSTM-MTV, a recurrent modeling framework in...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής

Κύριοι συγγραφείς:	Lihong He, Chen Zhang, Pengcheng Tang, Huiying Xie, Chibiao Liu, Jiancheng Zheng
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	Taylor & Francis Group 2026-12-01
Σειρά:	Engineering Applications of Computational Fluid Mechanics
Θέματα:	Soil moisture prediction deep learning xLSTM-MTV
Διαθέσιμο Online:	https://www.tandfonline.com/doi/10.1080/19942060.2026.2678125
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