13 citations as recorded by crossref.

1. Global Wildfire Outlook Forecast with Neural Networks Y. Song & Y. Wang https://doi.org/10.3390/rs12142246
2. Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections E. Strobach & G. Bel https://doi.org/10.1038/s41467-020-14342-9
3. On the prediction of persistent processes using the output of deterministic models H. Tyralis & D. Koutsoyiannis https://doi.org/10.1080/02626667.2017.1361535
4. Decadal Climate Predictions Using Sequential Learning Algorithms E. Strobach & G. Bel https://doi.org/10.1175/JCLI-D-15-0648.1
5. Regional Decadal Climate Predictions Using an Ensemble of WRF Parameterizations Driven by the MIROC5 GCM E. Strobach & G. Bel https://doi.org/10.1175/JAMC-D-18-0051.1
6. The contribution of internal and model variabilities to the uncertainty in CMIP5 decadal climate predictions E. Strobach & G. Bel https://doi.org/10.1007/s00382-016-3507-7
7. Quantifying the Uncertainties in an Ensemble of Decadal Climate Predictions E. Strobach & G. Bel https://doi.org/10.1002/2017JD027249
8. Parameter Sensitivity Analysis of the WRF-Hydro Modeling System for Streamflow Simulation: a Case Study in Semi-Humid and Semi-Arid Catchments of Northern China Y. Liu et al. https://doi.org/10.1007/s13143-020-00205-2
9. A new approach to extended‐range multimodel forecasting: Sequential learning algorithms P. Gonzalez et al. https://doi.org/10.1002/qj.4177
10. Research Challenges in Financial Data Modeling and Analysis L. Alexander et al. https://doi.org/10.1089/big.2016.0074
11. Multi-decadal drought variability in the Eastern Mediterranean and its connection to large-scale climate indices Y. Menachem & E. Strobach https://doi.org/10.1016/j.ejrh.2026.103249
12. Tackling Climate Change with Machine Learning D. Rolnick et al. https://doi.org/10.1145/3485128
13. Multi-output Deep Learning for Bus Arrival Time Predictions N. Petersen et al. https://doi.org/10.1016/j.trpro.2019.09.025