Bezdan, Jovana

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  • Bezdan, Jovana (2)
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Author's Bibliography

An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning

Bezdan, Atila; Bezdan, Jovana; Marković, Monika; Mirčetić, Dejan; Baumgertel, Aleksandar; Salvai, Andrea; Blagojević, Boško

(2025)

TY  - JOUR
AU  - Bezdan, Atila
AU  - Bezdan, Jovana
AU  - Marković, Monika
AU  - Mirčetić, Dejan
AU  - Baumgertel, Aleksandar
AU  - Salvai, Andrea
AU  - Blagojević, Boško
PY  - 2025
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1538
AB  - Waterlogging disasters are one of the most severe and widespread agricultural meteorological disasters. They affect about 15% of land surface globally, causing a significant reduction in crop growth and yields. This paper presents an objective methodology for assessing waterlogging risk, primarily in non-urban, predominantly agricultural areas. The waterlogging risk was assessed by evaluating vulnerability and hazard based on key environmental, anthropogenic, and climatic factors. The weights of factors affecting the waterlogging vulnerability were determined using the entropy weight method (EWM), assuring the objectivity of the overall evaluation results. The obtained waterlogging risk map was validated by comparing it with observed and detected waterlogged sites using Sentinel-2 imagery and Random Forest classification. The key novelties of this study are the use of the entropy weight method to objectively determine the relative importance of factors influencing waterlogging vulnerability, and a two-step validation process which includes field-based comparison and remote sensing validation. The presented methodology was demonstrated in the Vojvodina region, Serbia. The following waterlogging vulnerability factors were used: soil properties, geomorphology, surface depressions, average phreatic water table depth, and land cover. The EWM shows that surface depressions and soil properties have the most significant influence on waterlogging vulnerability. The highest waterlogging hazard classes occur in about 31% of the analyzed territory. The waterlogging hazard was estimated based on water balance for the non-vegetation season and maximum daily precipitation in spring, both modeled using the Generalize Extreme Value distribution function. The highest waterlogging hazard classes occur in about 31% of the analyzed territory. The final risk map shows that the high waterlogging risk occurs in about 11% of the territory. Those are mainly areas in the central, eastern, and southeastern parts of the Vojvodina region, usually along the main watercourses. High agreement between the detected waterlogged areas and the produced waterlogging risk map was achieved, validating the proposed methodology. The presented waterlogging risk assessment methodology is valuable for planning and policy-making for various water management and environmental activities. Although it is demonstrated in Vojvodina, by selecting the appropriate factors of vulnerability and hazard, it can be applied to any other region.
T2  - CATENA
T1  - An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning
SP  - 108618
VL  - 249
DO  - 10.1016/j.catena.2024.108618
ER  - 
@article{
author = "Bezdan, Atila and Bezdan, Jovana and Marković, Monika and Mirčetić, Dejan and Baumgertel, Aleksandar and Salvai, Andrea and Blagojević, Boško",
year = "2025",
abstract = "Waterlogging disasters are one of the most severe and widespread agricultural meteorological disasters. They affect about 15% of land surface globally, causing a significant reduction in crop growth and yields. This paper presents an objective methodology for assessing waterlogging risk, primarily in non-urban, predominantly agricultural areas. The waterlogging risk was assessed by evaluating vulnerability and hazard based on key environmental, anthropogenic, and climatic factors. The weights of factors affecting the waterlogging vulnerability were determined using the entropy weight method (EWM), assuring the objectivity of the overall evaluation results. The obtained waterlogging risk map was validated by comparing it with observed and detected waterlogged sites using Sentinel-2 imagery and Random Forest classification. The key novelties of this study are the use of the entropy weight method to objectively determine the relative importance of factors influencing waterlogging vulnerability, and a two-step validation process which includes field-based comparison and remote sensing validation. The presented methodology was demonstrated in the Vojvodina region, Serbia. The following waterlogging vulnerability factors were used: soil properties, geomorphology, surface depressions, average phreatic water table depth, and land cover. The EWM shows that surface depressions and soil properties have the most significant influence on waterlogging vulnerability. The highest waterlogging hazard classes occur in about 31% of the analyzed territory. The waterlogging hazard was estimated based on water balance for the non-vegetation season and maximum daily precipitation in spring, both modeled using the Generalize Extreme Value distribution function. The highest waterlogging hazard classes occur in about 31% of the analyzed territory. The final risk map shows that the high waterlogging risk occurs in about 11% of the territory. Those are mainly areas in the central, eastern, and southeastern parts of the Vojvodina region, usually along the main watercourses. High agreement between the detected waterlogged areas and the produced waterlogging risk map was achieved, validating the proposed methodology. The presented waterlogging risk assessment methodology is valuable for planning and policy-making for various water management and environmental activities. Although it is demonstrated in Vojvodina, by selecting the appropriate factors of vulnerability and hazard, it can be applied to any other region.",
journal = "CATENA",
title = "An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning",
pages = "108618",
volume = "249",
doi = "10.1016/j.catena.2024.108618"
}
Bezdan, A., Bezdan, J., Marković, M., Mirčetić, D., Baumgertel, A., Salvai, A.,& Blagojević, B.. (2025). An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning. in CATENA, 249, 108618.
https://doi.org/10.1016/j.catena.2024.108618
Bezdan A, Bezdan J, Marković M, Mirčetić D, Baumgertel A, Salvai A, Blagojević B. An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning. in CATENA. 2025;249:108618.
doi:10.1016/j.catena.2024.108618 .
Bezdan, Atila, Bezdan, Jovana, Marković, Monika, Mirčetić, Dejan, Baumgertel, Aleksandar, Salvai, Andrea, Blagojević, Boško, "An objective methodology for waterlogging risk assessment based on the entropy weighting method and machine learning" in CATENA, 249 (2025):108618,
https://doi.org/10.1016/j.catena.2024.108618 . .

Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia

Bezdan, Atila; Bezdan, Jovana; Blagojević, Boško; Baumgertel, Aleksandar; Lazić, Irida; Tošić, Milica; Đurđević, Vladimir

(2024)

TY  - JOUR
AU  - Bezdan, Atila
AU  - Bezdan, Jovana
AU  - Blagojević, Boško
AU  - Baumgertel, Aleksandar
AU  - Lazić, Irida
AU  - Tošić, Milica
AU  - Đurđević, Vladimir
PY  - 2024
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1458
AB  - One of the frequently used drought metrics in scientific research is the consecutive dry days (CDDs) because it effectively indicates short-term droughts important to ecosystems and agriculture. CDDs are expected to increase in many parts of the world in the future. In Serbia, both the frequency and severity of droughts have increased in recent decades, with most droughts being caused by a lack of precipitation during the warmer months of the year and an increase in evapotranspiration due to higher temperatures. In this study, the frequency and duration of extreme CDDs in the growing season in Serbia were analysed for the past (1950-2019) and the future (2020-2100) period. The Threshold Level Method over precipitation data series was used to analyse CDD events, where extreme CDDs are defined as at least 15 consecutive days without precipitation. In contrast to the original definition of the CDD as the maximum number of consecutive days with precipitation less than 1 mm, here we defined the threshold that is more suitable for agriculture because field crops can experience water stress after 15 days of no rainfall or irrigation. An approach for modelling the stochastic process of extreme CDDs based on the Zelenhasi & cacute;-Todorovi & cacute; (ZT) method was applied in this research. The ZT method was modified by selecting a different distribution function for modelling the durations of the longest CDD events, enabling a more reliable calculation of probabilities of occurrences. According to the results, future droughts in Serbia are likely to be more frequent and severe than those in the past. The duration of the longest CDDs in a growing season will be extended in the future, lasting up to 62 days with a 10-year return period and up to 94 days with a 100-year return period. Results indicate a worsening of drought conditions, especially in the eastern and northern parts of Serbia. The results can help decision-makers adapt agricultural strategies to climate change by providing information on the expected durations of extreme rainless periods in future growing seasons. Although the analysis was performed in Serbia, it can be applied to any other region.
T2  - International Journal of Climatology
T1  - Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia
EP  - 4141
IS  - 11
SP  - 4127
VL  - 44
DO  - 10.1002/joc.8573
UR  - conv_1804
ER  - 
@article{
author = "Bezdan, Atila and Bezdan, Jovana and Blagojević, Boško and Baumgertel, Aleksandar and Lazić, Irida and Tošić, Milica and Đurđević, Vladimir",
year = "2024",
abstract = "One of the frequently used drought metrics in scientific research is the consecutive dry days (CDDs) because it effectively indicates short-term droughts important to ecosystems and agriculture. CDDs are expected to increase in many parts of the world in the future. In Serbia, both the frequency and severity of droughts have increased in recent decades, with most droughts being caused by a lack of precipitation during the warmer months of the year and an increase in evapotranspiration due to higher temperatures. In this study, the frequency and duration of extreme CDDs in the growing season in Serbia were analysed for the past (1950-2019) and the future (2020-2100) period. The Threshold Level Method over precipitation data series was used to analyse CDD events, where extreme CDDs are defined as at least 15 consecutive days without precipitation. In contrast to the original definition of the CDD as the maximum number of consecutive days with precipitation less than 1 mm, here we defined the threshold that is more suitable for agriculture because field crops can experience water stress after 15 days of no rainfall or irrigation. An approach for modelling the stochastic process of extreme CDDs based on the Zelenhasi & cacute;-Todorovi & cacute; (ZT) method was applied in this research. The ZT method was modified by selecting a different distribution function for modelling the durations of the longest CDD events, enabling a more reliable calculation of probabilities of occurrences. According to the results, future droughts in Serbia are likely to be more frequent and severe than those in the past. The duration of the longest CDDs in a growing season will be extended in the future, lasting up to 62 days with a 10-year return period and up to 94 days with a 100-year return period. Results indicate a worsening of drought conditions, especially in the eastern and northern parts of Serbia. The results can help decision-makers adapt agricultural strategies to climate change by providing information on the expected durations of extreme rainless periods in future growing seasons. Although the analysis was performed in Serbia, it can be applied to any other region.",
journal = "International Journal of Climatology",
title = "Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia",
pages = "4141-4127",
number = "11",
volume = "44",
doi = "10.1002/joc.8573",
url = "conv_1804"
}
Bezdan, A., Bezdan, J., Blagojević, B., Baumgertel, A., Lazić, I., Tošić, M.,& Đurđević, V.. (2024). Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia. in International Journal of Climatology, 44(11), 4127-4141.
https://doi.org/10.1002/joc.8573
conv_1804
Bezdan A, Bezdan J, Blagojević B, Baumgertel A, Lazić I, Tošić M, Đurđević V. Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia. in International Journal of Climatology. 2024;44(11):4127-4141.
doi:10.1002/joc.8573
conv_1804 .
Bezdan, Atila, Bezdan, Jovana, Blagojević, Boško, Baumgertel, Aleksandar, Lazić, Irida, Tošić, Milica, Đurđević, Vladimir, "Observed characteristics and projected future changes of extreme consecutive dry days events of the growing season in Serbia" in International Journal of Climatology, 44, no. 11 (2024):4127-4141,
https://doi.org/10.1002/joc.8573 .,
conv_1804 .
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