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Application of Developing Artificial Intelligence (AI) Techniques to Model Pan Evaporation Trends in Slovak River Sub-Basins

Author

Listed:
  • Beáta Novotná

    (Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovakia)

  • Vladimír Cviklovič

    (Institute of Electrical Engineering, Automation, Informatics and Physics, Faculty of Engineering, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovakia)

  • Branislav Chvíla

    (Division of Meteorological Service, Slovak Hydrometeorological Institute, 833 15 Bratislava, Slovakia)

  • Martin Minárik

    (Institute of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak University of Agriculture in Nitra, 949 76 Nitra, Slovakia)

Abstract

The modeling of pan evaporation ( Ep ) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity ( RH ) and minimum temperature ( t min ) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity.

Suggested Citation

  • Beáta Novotná & Vladimír Cviklovič & Branislav Chvíla & Martin Minárik, 2025. "Application of Developing Artificial Intelligence (AI) Techniques to Model Pan Evaporation Trends in Slovak River Sub-Basins," Sustainability, MDPI, vol. 17(2), pages 1-28, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:526-:d:1565007
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    1. Veronika Eyring & William D. Collins & Pierre Gentine & Elizabeth A. Barnes & Marcelo Barreiro & Tom Beucler & Marc Bocquet & Christopher S. Bretherton & Hannah M. Christensen & Katherine Dagon & Davi, 2024. "Pushing the frontiers in climate modelling and analysis with machine learning," Nature Climate Change, Nature, vol. 14(9), pages 916-928, September.
    2. Zhang, Lei & Traore, Seydou & Cui, Yuanlai & Luo, Yufeng & Zhu, Ge & Liu, Bo & Fipps, Guy & Karthikeyan, R. & Singh, Vijay, 2019. "Assessment of spatiotemporal variability of reference evapotranspiration and controlling climate factors over decades in China using geospatial techniques," Agricultural Water Management, Elsevier, vol. 213(C), pages 499-511.
    3. W. Brutsaert & M. B. Parlange, 1998. "Hydrologic cycle explains the evaporation paradox," Nature, Nature, vol. 396(6706), pages 30-30, November.
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