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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Beáta Novotná & Ľuboš Jurík & Ján Čimo & Jozef Palkovič & Branislav Chvíla & Vladimír Kišš, 2022. "Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3475-:d:772391
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    1. Yeşim Ahi & Çiğdem Coşkun Dilcan & Daniyal Durmuş Köksal & Hüseyin Tevfik Gültaş, 2023. "Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2607-2624, May.
    2. Han Chen & Ziqi Zhou & Han Li & Yizhao Wei & Jinhui (Jeanne) Huang & Hong Liang & Weimin Wang, 2023. "Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas," Sustainability, MDPI, vol. 15(12), pages 1-18, June.

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