EPS eLibrary: ASSESSING PAN EVAPORATION TRENDS IN THE VÁH RIVER BASIN, SLOVAK REPUBLIC, USING ARTIFICIAL INTELLIGENCE TECHNIQUES

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Title: ASSESSING PAN EVAPORATION TRENDS IN THE VÁH RIVER BASIN, SLOVAK REPUBLIC, USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Year

2025

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274

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Title

ASSESSING PAN EVAPORATION TRENDS IN THE VÁH RIVER BASIN, SLOVAK REPUBLIC, USING ARTIFICIAL INTELLIGENCE TECHNIQUES

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Authors

Beáta Novotná; Vladimír Cviklovic; Lucia Tátošová

DOI

10.5593/sgem2025/3.1/s11.07

DOI Issue

10.5593/sgem2025/3.1

ISSN

1314-2704

ISBN

978-619-7603-81-1

Language

English

Volume

Issue

3.1

Editor

• Prof. Dr. hab. oec. Baiba Rivza, LATVIA• Prof. DSc. Ildiko Tulbure, GERMANY• Prof. DSc. Oleksandr Trofymchuk, UKRAINE

Scientific Area

Hydrology, Water Resources, and Climate Resilience

Abstract/ Foreword

The combination of Long Short-Term Memory (LSTM) neural networks, comprehensive trend analysis, and standardized pan evaporation measurements creates the capability for understanding and predicting regional evaporation dynamics in the context of climate change in this study. Based on the thorough examination of this study in the Slovak Republic's Váh river basin the K-means clustering analysis revealed three different patterns of evaporation behaviour (1.5-3.2 mm/day); the seasonal analysis revealed that peak evaporation in July exceeded 4 mm and decreased to less than 2 mm in September; and the machine learning validation achieved remarkable results with an RMSE of 0.4. The three main analytical approaches employed in the study are succinctly described in the methodology section: Neural network training with convergence monitoring for model validation, seasonal analysis from May to September for temporal characterisation, and K-means clustering for spatial pattern detection.

References (click to open)

[1] Slovak Hydrometeorological Institute (2021). Climate Atlas of Slovakia. Bratislava: Slovak Hydrometeorological Institute. https://www.shmu.sk/sk/?page=2169
[2] Ministry of Environment of the Slovak Republic (2011). Water Plan of the Slovak Republic. Bratislava: Ministry of Environment of the Slovak Republic. https://faolex.fao.org/docs/pdf/slo218847.pdf
[3] Xu, J., Wang, Z., Li, X., Li, Z., & Li, Z. (2024). Prediction of daily climate using long short-term memory (LSTM) model. International Journal of Innovative Science and Research Technology, 9(7). https://doi.org/10.38124/ijisrt/IJISRT24JUL073
[4] MathWorks. Statistics and Machine Learning Toolbox Analyze and Model Data Using Statistics and Machine Learning, 2024. Available online: https://www.mathworks.com/products/statistics.html (accessed on 30 November 2024)
[5] Abed, M., Imteaz, M. A., Ahmed, A. N., & Huang, Y. F. (2021). Application of long short-term memory neural network technique for predicting monthly pan evaporation. Scientific Reports, 11, 20742. https://doi.org/10.1038/s41598-021-99999-y
[6] Zerouali, B., Bailek, N., Bouchouich, K., Mawloud, G., Kuriqi, A., Khafaga, D. S., Alharbi, A. H., & El-kenawy, E.-S. M. (2025). Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction. AQUA - Water Infrastructure, Ecosystems and Society, 74(1), 18-35. https://doi.org/10.2166/aqua.2025.105852
[7] Sabová, Z., Németová, Z., & Kohnová, S. (2023). Comparison of spatial and temporal future changes of hydrological regime in selected river basins of Slovakia. EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6181. https://doi.org/10.5194/egusphere-egu23-6181
[8] Novotná, B., Cviklovic, V., Chvíla, B., & Minárik, M. (2025). Application of Developing Artificial Intelligence (AI) Techniques to Model Pan Evaporation Trends in Slovak River Sub-Basins. Sustainability, 17(2), 526. https://doi.org/10.3390/su17020526
[9] Vulevic, A., Berisha, E., Cotella, G., & Solly, A. (2021). Cross-border cooperation and adaptation to climate change in Western Balkans Danube area. In E. Berisha, G. Cotella, & A. Solly (Eds.), Governing territorial development in the Western Balkans (Advances in Spatial Science, pp. 251–268). Springer. https://doi.org/10.1007/978-3-030-72124-4_14
[10] Miklos, L. (2002) Landscape Atlas of the Slovak Republic. DVD [GIS version on the DVD].
[11] Doan, Q.-V., Amagasa, T., Pham, T.-H., Sato, T., Chen, F., & Kusaka, H. (2023). Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data. Geoscientific Model Development, 16(6), 2215–2234. https://doi.org/10.5194/gmd-16-2215-2023
[12] Pei, X., Lei, X., Zhang, Y., Wang, M., Han, I., & Chen, Q. (2018). Cluster analysis of drought variation and its mutation characteristics in Xinjiang province, during 1961–2015. Hydrology Research, 49(4), 1016–1027. https://doi.org/10.2166/nh.2018.105
[13] Paule-Mercado, M. C., Rabaneda-Bueno, R., Porcal, P., Kopácek, J., Hejzlar, J., & Vystavna, Y. (2024). Climate and land use shape the water balance and water quality in selected European lakes. Scientific Reports, 14, 8049. https://doi.org/10.1038/s41598-024-58401-3
[14] Rouse, R. E., Khamis, D., Hosking, S., McRobie, A., & Shuckburgh, E. (2025). Streamflow prediction using artificial neural networks and soil moisture proxies. Environmental Data Science, 4, e5. https://doi.org/10.1017/eds.2024.48
[15] Huynh, N. N. T., Garambois, P.-A., Renard, B., & Monnier, J. (2025). A general framework for integrating neural networks into numerical resolution methods for spatially distributed hydrological models. In EGU General Assembly 2025, Vienna, Austria. https://doi.org/10.5194/egusphere-egu25-2740

Acknowledgements (click to open)

This study was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences (VEGA) under the contract No. VEGA 1/0559/23: “Assessment of the Production and Regulatory Function of Agricultural Ecosystems Affected by Climate Change.”

Type

conference

Url

https://doi.org/10.5593/sgem2025/3.1/s11.07

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ASSESSING PAN EVAPORATION TRENDS IN THE VÁH RIVER BASIN, SLOVAK REPUBLIC, USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Proceedings Тitle

Proceedings of 25th International Multidisciplinary Scientific GeoConference SGEM 2025, Volume 25, Issue 3.1

Note

25th International Multidisciplinary Scientific GeoConference SGEM 2025, Volume 25, Issue 3.1, 29 June - 6 July, 2025

Type

Proceedings Paper

Publisher

STEF92 Technology

Series

International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM

Organization

SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Russian Acad Arts; Turkish Acad Sci.

Pages

49-58

Date

29 June - 6 July, 2025

Online Source Type

website

Digital Source Type

10354

Keywords

Váh river basin, pan evaporation, trends, artificial intelligence, Long Short-Term Memory (LSTM) models