Abstract
AbstractGeospatial atmospheric data is the input variable of a wide range of hydrological and ecological spatial models, many of which are oriented towards improving the socioeconomic and environmental sustainability. Here, we provide an evaluation of machine learning (ML) methods for the spatial interpolation of annual precipitation, minimum and maximum temperatures for a mountain range, in this case, the Pyrenees. To this end, this work compares the performance and accuracy of multiple linear regressions (MLR) and generalized additive models (GAM) against five ML methods (K-Nearest Neighbors, Supported Vector Machines, Neural Networks, Stochastic Gradient Boosting and Random Forest). The ML algorithms outperformed the MLR and GAM independently of the predictor variables used, the geographical sector analyzed or the elevation range. Overall, the differences between ML algorithms are negligible. Random Forest shows a slightly higher than average accuracy for the spatial interpolation of precipitation (R2 = 0.93; MAE = 70.44 mm), whereas Stochastic Gradient Boosting is the best ML method for the spatial interpolation of the mean maximum annual temperature (R2 = 0.96, MAE = 0.43 ºC). Stochastic Gradient Boosting, Neural Networks and Random Forest have similar performances for the spatial interpolation of the mean minimum annual temperature (R2 = 0.98, MAE = 0.19 ºC). Results presented here can be valuable for the past and future climate spatial analysis, environmental niche modelling, hydrological projections, and water management.
Funder
Agència de Gestió d’Ajuts Universitaris i de Recerca
Ministerio de Ciencia, Innovación y Universidades
Universitat de Barcelona
Publisher
Springer Science and Business Media LLC
Reference72 articles.
1. Agnew MD, Palutikof JP (2000) GIS-based construction of baseline climatologies for the Mediterranean using terrain variables. Climate Res 14:115–127
2. Alonso-González E, López-Moreno JI, Navarro-Serrano FM, Revuelto J (2020a) Impact of North Atlantic Oscillation on the Snowpack in Iberian Peninsula Mountains. Water 12:105. https://doi.org/10.3390/w12010105
3. Alonso-González E, López-Moreno JI, Navarro-Serrano F, Sanmiguel-Vallelado A, Aznárez-Balta M, Revuelto J, Ceballos A (2020b) Snowpack Sensitivity to Temperature Precipitation and Solar Radiation Variability over an Elevational Gradient in the Iberian Mountains. Atmos Res 243:104973. https://doi.org/10.1016/j.atmosres.2020.104973
4. Appelhans T, Mwangomo E, Hardy DR, Hemp A, Nauss T (2015) Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro Tanzania, Spatial Statistics 14:91–113. https://doi.org/10.1016/j.spasta.2015.05.008
5. Batalla M, Ninyerola M, Trapero L, Esteban P (2016) ACDA: Andorran Climate Digital Atlas (period 1981–2010) Map server. Institut d'Estudis Andorrans (IEA) Universitat Autonoma de Barcelona (UAB). https://www.iea.ad/90-sigma/cartografia/cartografia-climatica. Accessed 22 Jan 2022
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