5 citations
- University of OttawaCA3 papers
- Huawei Technologies (China)CN2 papers
- Biostatistique et processus spatiauxFR1 paper
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementFR1 paper
- LeroIE1 paper
- Monash UniversityAU1 paper
- National University of Ireland, MaynoothIE1 paper
- Santa Fe InstituteUS1 paper
- Science Foundation IrelandIE1 paper
- University of LuxembourgLU1 paper
- University of VermontUS1 paper
- Valeo (Ireland)IE1 paper
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stat.AP2026
A Scalable Bayesian Spatiotemporal Model for Water Level Predictions using a Nearest Neighbor Gaussian Process Approach
Victor Hugo Nagahama, James Sweeney, Niamh Cahill
Obtaining accurate water level predictions are essential for water resource management and implementing flood mitigation strategies. Several data-driven models can be found in the…
stat.AP2026
Enhancing the Accuracy of Spatio-Temporal Models for Wind Speed Prediction by Incorporating Bias-Corrected Crowdsourced Data
Eamonn Organ, Maeve Upton, Denis Allard +2
Accurate high-resolution spatial and temporal wind speed data is critical for estimating the wind energy potential of a location. For real-time wind speed prediction, statistical m…