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InterpolateR - A Comprehensive Toolkit for Fast and Efficient Spatial Interpolation
Spatial interpolation toolkit designed for environmental and geospatial applications. It includes a range of methods, from traditional techniques to advanced machine learning approaches, ensuring accurate and efficient estimation of values in unobserved locations.
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5.15 score 14 stars 2 scripts 421 downloadsRFplus - Machine Learning for Merging Satellite and Ground Precipitation Data
A machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.
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4.26 score 3 stars 12 scripts 268 downloads