Deep learning for spatiotemporal forecasting in Earth system science: a review

Manzhu Yu, Qunying Huang, Zhenlong Li
ISSN / E-ISSN
— / —
DOI
10.1080/17538947.2024.2391952 ↗
Volume · Issue · Pages
17 · 1 · —
Publisher
Taylor and Francis Ltd.
Article URL
https://doi.org/10.1080/17538947.2024.2391952 ↗
Article details
Unverified
Publication date (each date has its type and source)
19 Aug 2024
OpenAlex publication date · source: OpenAlex
Dates on record for this article
TypeDateSource
OpenAlex publication date19 Aug 2024 OpenAlex

Keywords (as given): spatiotemporal prediction; deep learning; Earth system science; climate modeling; weather forecasting; disaster management; air quality modeling; hydrological modeling; renewable energy forecasting; oceanography; environmental monitoring; model interpretability; uncertainty quantification; domain adaptation; transfer learning; missing data handling

HEC Status at Publication Not the journal's current status

HEC Status at PublicationConclusive
HEC status at publication Recognized W
Based on the recorded HEC dataset applicable to the article's publication date. Listed as W in the HEC list for 2023–24.
Journal
International Journal of Digital Earth
Publication date used
19 Aug 2024 (OpenAlex publication date)
Applicable HEC period
2023–24 (version 1) · 01 Jul 2023 – 30 Sep 2024
Category
W
Recognition status
Recognized (OK_RECORD_FOUND)
Source
HJRS 2023-24 page 962, row 6698
Verification status
Verified against source · last verified 23 Sep 2026
This is an automatically generated summary based on the information available on the publication date. Terms & conditions apply.