Skip to content
DraconiticResearch
Search Sources API
178,033 studies31 sourcesUpdated 16 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Hybrid machine learning framework for drought forecasting: integration of LSBoost, RQNN, CNN-RNN, CNN-LSTM and advanced data decomposition

  • Hatice Çıtakoğlu
  • Pınar Spor
  • Volkan Gündüz
  • Berna Aksoy
Open at sourcePDFDOICite

Related

  1. Advanced machine learning techniques for daily streamflow forecasting: a case study of the Brahmaputra River
    Kazy Noor-e-Alam Siddiquee, Md Tanjil Sarker, Marran Al Qwaid +5
    Scientific ReportsPeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  2. FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting
    Shijie Xiao, Jonathan Lin, Thomas Ehrmann +1
    arXivPreprintarXiv licenceFull textAtmospheric science & meteorologyarXiv
  3. Station-specific thunderstorm forecasting using climate predictors with seasonal decomposition analysis in Bangladesh’s most vulnerable regions
    Mohammad Mahboob Hussain Khan, Amrin Binte Ahmed, Adisha Dulmini +6
    PLOS OnePeer-reviewedCC BY 4.0Full textAtmospheric science & meteorologyGeophysics & seismologyPLOS
  4. Circular Economy Approaches for Sustainable Waste Management: A Review on Integration of AI, Advanced Technologies and Policy Recommendations
    Abhishek N. Srivastava, Arun Krishna Vuppaladadiyam, Rakhi Punnadan Koroth +8
    RecyclingPeer-reviewedCC BY13 citedEnvironmental science & pollutionOpenAlex
  5. Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification
    Rohit Shukla, Cayelan C. Carey, Adrienne Breef-Pilz +1
    ACS ES&T waterPeer-reviewedCC BYFull textEnvironmental science & pollutionEurope PMC

Cite

BibTeXRIS
BibTeX
@article{cıtakoglu2026hybrid,
  title = {{Hybrid machine learning framework for drought forecasting: integration of LSBoost, RQNN, CNN-RNN, CNN-LSTM and advanced data decomposition}},
  author = {Hatice Çıtakoğlu and Pınar Spor and Volkan Gündüz and Berna Aksoy},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-73680-8},
  url = {https://www.nature.com/articles/s41598-026-73680-8},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Hybrid machine learning framework for drought forecasting: integration of LSBoost, RQNN, CNN-RNN, CNN-LSTM and advanced data decomposition
AU  - Hatice Çıtakoğlu
AU  - Pınar Spor
AU  - Volkan Gündüz
AU  - Berna Aksoy
PY  - 2026
DA  - 2026/10/03
JO  - Scientific Reports
DO  - 10.1038/s41598-026-73680-8
UR  - https://www.nature.com/articles/s41598-026-73680-8
SN  - 2045-2322
ER  -