Publication: Algorithms for the spatial interpolation of environmental data
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Publication date
2022-06
Defense date
2022-07-12
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Abstract
Spatial interpolation is a technique used widely in the environmental sciences to estimate
values between measurements obtained from remote sensors. Deterministic algorithms
such as Inverse-distance Weighting and Radial Basis Functions and statistical
methods like Kriging have been the most preferred methods for this kind of problem in
the past. More recently, machine learning algorithms have begun to adapt to this problem.
This works attempts to make a survey of the various commonly used and novel methods
that can be used to perform spatial interpolation.
We make an empirical study where various techniques are used to estimate significant
wave height measurements using data obtained from the National Data Buoy Center
(NDBC) of the United States’ Oceanographic and Atmospheric Administration (NOAA).
We show that Machine Learning methods can be reliable and more accurate alternatives
to other commonly used methods.
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Spatial interpolation, Machine learning, Algorithms, Statistical methods, Kriging