Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)

Beáta Novotná, Ľuboš Jurík, Ján Čimo, Jozef Palkovič, Branislav Chvíla and Vladimír Kišš
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Beáta Novotná: Institute of the Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture, 949 76 Nitra, Slovakia
Ľuboš Jurík: Institute of the Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture, 949 76 Nitra, Slovakia
Ján Čimo: Institute of the Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture, 949 76 Nitra, Slovakia
Jozef Palkovič: Institute of Statistics, Operation Research and Mathematics, Faculty of Economics and Management, Slovak University of Agriculture, 949 76 Nitra, Slovakia
Branislav Chvíla: Meteorological and Climatological Monitoring, Network of Ground Synoptic Stations, Slovak Hydrometeorological Institute, 833 15 Bratislava, Slovakia
Vladimír Kišš: AgroBioTech Research Centre, Slovak University of Agriculture, 949 76 Nitra, Slovakia

Sustainability, 2022, vol. 14, issue 6, 1-22

Abstract: Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.

Keywords: pan evaporation; agroclimatic zone; macro-region; climatic characteristic; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:6:p:3475-:d:772391

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