Machine learning procedures for daily interpolation of rainfall in Navarre (Spain)
Consultable a partir de
2024-06-28
Fecha
2023Versión
Acceso embargado / Sarbidea bahitua dago
Tipo
Capítulo de libro / Liburuen kapitulua
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
Impacto
|
10.1007/978-3-031-04137-2_34
Resumen
Kriging is by far the most well known and widely used statistical method
for interpolating data in spatial random fields. The main reason is that it provides
the best linear unbiased predictor and it is an exact interpolator when normality is
assumed. The robustness of this method allows small departures from normality, however, many meteorological, pollutant and environmental variables have e ...
[++]
Kriging is by far the most well known and widely used statistical method
for interpolating data in spatial random fields. The main reason is that it provides
the best linear unbiased predictor and it is an exact interpolator when normality is
assumed. The robustness of this method allows small departures from normality, however, many meteorological, pollutant and environmental variables have extremely
asymmetrical distributions and Kriging cannot be used. Machine learning techniques
such as neural networks, random forest, and k-nearest neighbor can be used instead,
because they do not require specific distributional assumptions. The drawback is that
they do not take account of the spatial dependence, and for an optimal performance
in spatial random fields more complex machine learning techniques could be considered. These techniques also require a relatively large amount of training data and
they are computationally challenging to implement. For a reduced number of observations, we illustrate the performance of the aforementioned procedures using daily
rainfall data of manual meteorological gauge stations in Navarre, where the only
auxiliary variables available are the spatial coordinates and the altitude. The quality
of the predictions is carefully checked through three versions of the relative root
mean squared error (RRMSE). The conclusion is that when we cannot use Kriging,
random forest and neural networks outperform k-nearest neighbor technique, and
provide reliable predictions of rainfall daily data with scarce auxiliary information. [--]
Materias
Kriging,
Machine learning techniques,
Spatial random fields,
Rainfall data
Editor
Springer
Publicado en
Balakrishnan, N.; Gil, M. Á.; Martín, N.; Morales, D.; Pardo, M. C. (Eds.). Trends in mathematical, information and data sciences: a tribute to Leandro Pardo. Springer; 2023. p. 399-413 978-3-031-04137-2
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute for Advanced Materials and Mathematics - INAMAT2
Versión del editor
Entidades Financiadoras
This research was supported by the Spanish Research Agency (PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033 project). It has also received funding from la Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.