Geostatistical Estimation Methods: Kriging

Z., Y.

doi:10.1007/978-3-030-17860-4_16

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Cooperative Spectrum Sensing and Hidden Nodes1

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2023

2024

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Cited by 2 publications

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“…Although for other problems related to geo-tagged variables, interpolation methods like Delaunay and Kriging might provide a good approximation between the correlation of the geospatial data and the target objective [33], it is not the case of spectrum data. By one hand spectrum data is not only correlated to the spatial geolocation but to the frequency domain (i.e, self-correlation across the frequency domain).…”

Section: Cooperative Spectrum Sensing and Hidden Nodesmentioning

confidence: 99%

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

et al. 2023

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Section: Cooperative Spectrum Sensing and Hidden Nodesmentioning

confidence: 99%

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

et al. 2023

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SWAT + input data preparation in a scripted workflow: SWATprepR

Plunge,

Szabó,

Strauch

et al. 2024

Environ Sci Eur

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Input data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT + ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT + model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT + modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT + model for a small catchment in central Poland.

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Geostatistical Estimation Methods: Kriging

Cited by 2 publications

References 16 publications

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

SWAT + input data preparation in a scripted workflow: SWATprepR

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