Correction: Zhao et al. Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention. Water 2025, 17, 2043

Authors

Stefano Fazi (ORCID: https://orcid.org/0000-0001-7688-7179)
Liangjie Zhao
Fan Yu (ORCID: https://orcid.org/0000-0003-1562-250X)
Cheng Li (ORCID: https://orcid.org/0000-0002-6546-6413)
Yang Yang (ORCID: https://orcid.org/0000-0002-9008-5137)
Zhe Wang (ORCID: https://orcid.org/0000-0003-1964-2292)
Song Luan (ORCID: https://orcid.org/0000-0001-9201-6111)

Institutions

Chinese Academy of Geological Sciences (CN)
Water Research Institute (IT)
National Research Council (IT)

Publication Details

Journal: Water
Published: 2025-08-25
DOI: https://doi.org/10.3390/w17172526
Primary Topic: Landslides and related hazards
Type: article
Field-Weighted Citation Impact: 0.00

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article

Correction: Zhao et al. Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention. Water 2025, 17, 2043

Stefano Fazi, Liangjie Zhao, Fan Yu, Cheng Li et al.

Aug 25, 2025

Water

Landslides and related hazards

article

Correction: Zhao et al. Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention. Water 2025, 17, 2043

Stefano Fazi, Liangjie Zhao, Fan Yu, Cheng Li, Yang Yang, Zhe Wang, Song Luan

August 25, 2025article en

Abstract

No abstract available for this paper.

WaterVol. 17(17)

Chinese Academy of Geological Sciences (CN), Water Research Institute (IT), National Research Council (IT)

Openalex Percentile: Top 70%

Landslides and related hazards

DOI Open Access PDF OpenAlex

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