RT Generic
T1 Algorithms for the spatial interpolation of environmental data
A1 Sánchez Viloria, Simón Esteban
AB Spatial interpolation is a technique used widely in the environmental sciences to estimatevalues between measurements obtained from remote sensors. Deterministic algorithmssuch as Inverse-distance Weighting and Radial Basis Functions and statisticalmethods like Kriging have been the most preferred methods for this kind of problem inthe 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 methodsthat can be used to perform spatial interpolation.We make an empirical study where various techniques are used to estimate significantwave 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 alternativesto other commonly used methods.
YR 2022
FD 2022-06
LK https://hdl.handle.net/10016/36154
UL https://hdl.handle.net/10016/36154
LA eng
DS e-Archivo
RD 14 jun. 2024