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ShareScore release 0.9.0
Dataset results
8 results for “kriging”
High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling - Datasets
<p>Datasets and notebooks used in the publication High-Resolution Vector-borne Disease Infection Risk Mapping with Area-to-Point Kriging and Species Distribution Modeling</p>
OMS project for Kriging interpolation of precipitation and temperature in Isarco River Valley
<p>The OMS project contains the simulations, jar files of the components, the inputs and the ouputs used in the Chapter 6 of the thesis " A flexible approach to the estimation of water budgets and its connection to the travel time theory ", Bancheri (2017) and in the article "The design and implementatation of Kriging models in the Object Modelling System v.3.", Bancheri et al. 2018. The project allows the Kriging interpolation of the precipitation and temperature, using data from Isarco River Valley. </p>
Borehole Lithological Facies Dataset for Interval Kriging
<p>Four sets of well log data and predefined grids for performing the interval kriging estimation. </p>
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Spherical variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of <strong>Spherical</strong> variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Northern Cameroon daily interpolated rainfall, 1948-2022, combined dataset obtained via ordinary kriging
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> This dataset was created by combining maps obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models and ordinary kriging with fitting of Spherical variogram models. The maps produced by the Spherical variogram models were taken as base of the combined dataset. Dates that could not be produced by the Spherical variogram models, but were produced by the Circular variogram models, were added to the combined dataset. Equally, dates produced by the Spherical variogram models containing negative values, but containing positive values when produced by the Circular variogram models, were replaced. The Mean Error calculated for both variogram models showed a similar bias and a paired t-test showed no significant difference. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Ordinary Kriging Precipitation Time Series for surrounding CAPLTER areas, 1999 to 2011
Annual rainfall data within the CAP/LTER boundary. Consists of 2 Layer Packages and tabular data. The table contains rainfall data from 2000 to 2011 based on ALERT data collection sites via The Flood Control District of Maricopa County. Included are 12 classified raster datasets displaying annual rainfall data for each year as well as point data of each ALERT collection site. Also included are city boundaries, the CAP/LTER boundary, Native American Reservation boundaries, and the location of the capital as either point or polygon datasets for a location reference.
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Circular variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
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