22 citations as recorded by crossref.

1. SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates M. Mälicke et al. https://doi.org/10.1016/j.spasta.2023.100737
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5. Estimating Variogram Uncertainty B. Marchant & R. Lark https://doi.org/10.1023/B:MATG.0000048797.08986.a7
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7. Current Awareness https://doi.org/10.1002/hyp.538
8. The usability of 250 m resolution data from the UK Meteorological Office Unified Model as input data for a hydrological model P. Younger et al. https://doi.org/10.1002/met.46
9. Robust parameter design for nonlinear signal–response systems using kriging models X. Xiong et al. https://doi.org/10.1080/0305215X.2019.1650924
10. Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model S. Yao et al. https://doi.org/10.1007/s11431-012-5038-8
11. Variation of groundwater level due to land use, precipitation, and earthquake in Yogyakarta City from 2005 to 2020 N. Rahmawati et al. https://doi.org/10.1016/j.gsd.2024.101195
12. Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps C. Frei & F. Isotta https://doi.org/10.1029/2018JD030004
13. Probabilistic Quantitative Precipitation Estimation in Complex Terrain M. Clark & A. Slater https://doi.org/10.1175/JHM474.1
14. Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method S. Li et al. https://doi.org/10.1002/app.48659
15. On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy A. Lombardi et al. https://doi.org/10.5194/hess-28-3777-2024
16. Optimal Spatial Prediction Using Ensemble Machine Learning M. Davies & M. van der Laan https://doi.org/10.1515/ijb-2014-0060
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18. Research studies on improvement in real-time estimation of radar-based precipitation in Poland A. Jurczyk et al. https://doi.org/10.1007/s00703-007-0266-3
19. On the drawback of local detrending in universal kriging in conditions of heterogeneously spaced regional TEC data, low-order trends and outlier occurrences W. Jarmołowski et al. https://doi.org/10.1007/s00190-020-01447-8
20. A robust maximum likelihood estimation approach for Ordinary Kriging with outlier-contaminated spatial data V. Yildirim et al. https://doi.org/10.1007/s10651-026-00706-9
21. Optimized Sample Schemes for Geostatistical Surveys B. Marchant & R. Lark https://doi.org/10.1007/s11004-006-9069-1
22. Data Stream Driven Dynamic Multiobjective Optimization Using Surrogate Transfer Z. Liu et al. https://doi.org/10.1109/TETCI.2025.3526505