SCIESCOPUSQ2
Quaternary Science Advances
ELSEVIER, United Kingdom
Quaternary Science Advances is an academic journal published by ELSEVIER (United Kingdom). Identifiers: ISSN 2666-0334, eISSN 2666-0334. Indexed in SCIE, SCOPUS. Metrics: JIF 2.2, CiteScore 3.8, SJR 0.654, SNIP 1.11. Subject areas: GEOSCIENCES, MULTIDISCIPLINARY. tlooto lists 301 papers from this journal.
CiteScore
3.80
Scopus citation metric
SJR
0.654
SCImago rank
SNIP
1.11
Source normalized impact
Percentage rank
-
JIF percentile rank
Journal profile
- ISSN
- 2666-0334
- eISSN
- 2666-0334
- Abbreviation
- QUAT SCI ADV
- Publisher
- ELSEVIER
- Country
- United Kingdom
Web of Science categories
No Web of Science category data available.
Scopus ASJC categories
1901 Earth and Planetary Sciences (miscellaneous)1904 Earth-Surface Processes1907 Geology
Papers in this journal
Recent papers
- Alpine speleothem records millennial-scale climate variability during Marine Isotope Stage 10
2026
- Morphostructural controls on the orientation of lajedos (rock platforms/rock outcrop) in Quixadá and Quixeramobim - CE
2026
- Effects of wood degradation on tree-ring stable isotopes used in climate reconstructions
2026
- The Skateholm networks and hunting grounds: Late Mesolithic cultural and territorial legacy investigated through strontium isotope analyses and tooth-bead use-wear
2026
- East Eurasian Dispersals to the Americas: A Spatiotemporal Perspective from Paleogenomics
2026
Most cited papers
- Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio method in Sikkim Himalaya
2022 · 67 citations
- Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India)
2023 · 54 citations
- Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India
2024 · 36 citations
- Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling
2023 · 36 citations
- Application of GIS-Based data-driven bivariate statistical models for Landslide prediction: A case study of highly affected landslide prone areas of Teesta River basin
2023 · 31 citations