TY  - JOUR
AU  - Malinović-Milićević, Slavica
AU  - Radovanović, Milan M.
AU  - Radenković, Sonja D.
AU  - Vyklyuk, Yaroslav
AU  - Milovanović, Boško
AU  - Milanović Pešić, Ana
AU  - Milenković, Milan
AU  - Popović, Vladimir
AU  - Petrović, Marko
AU  - Sydor, Petro
AU  - Gajić, Mirjana
PY  - 2023
UR  - https://dais.sanu.ac.rs/123456789/14049
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1720
AB  - This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.
PB  - Basel : MDPI
T2  - Mathematics
T1  - Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods
VL  - 11
IS  - 4
SP  - 795
DO  - 10.3390/MATH11040795
ER  - 

@article{
author = "Malinović-Milićević, Slavica and Radovanović, Milan M. and Radenković, Sonja D. and Vyklyuk, Yaroslav and Milovanović, Boško and Milanović Pešić, Ana and Milenković, Milan and Popović, Vladimir and Petrović, Marko and Sydor, Petro and Gajić, Mirjana",
year = "2023",
abstract = "This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.",
publisher = "Basel : MDPI",
journal = "Mathematics",
title = "Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods",
volume = "11",
number = "4",
pages = "795",
doi = "10.3390/MATH11040795"
}

Malinović-Milićević, S., Radovanović, M. M., Radenković, S. D., Vyklyuk, Y., Milovanović, B., Milanović Pešić, A., Milenković, M., Popović, V., Petrović, M., Sydor, P.,& Gajić, M.. (2023). Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods. in Mathematics
Basel : MDPI., 11(4), 795.
https://doi.org/10.3390/MATH11040795

Malinović-Milićević S, Radovanović MM, Radenković SD, Vyklyuk Y, Milovanović B, Milanović Pešić A, Milenković M, Popović V, Petrović M, Sydor P, Gajić M. Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods. in Mathematics. 2023;11(4):795.
doi:10.3390/MATH11040795 .

Malinović-Milićević, Slavica, Radovanović, Milan M., Radenković, Sonja D., Vyklyuk, Yaroslav, Milovanović, Boško, Milanović Pešić, Ana, Milenković, Milan, Popović, Vladimir, Petrović, Marko, Sydor, Petro, Gajić, Mirjana, "Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods" in Mathematics, 11, no. 4 (2023):795,
https://doi.org/10.3390/MATH11040795 . .

TY  - JOUR
AU  - Vyklyuk, Yaroslav
AU  - Radovanović, Milan
AU  - Stanojević, Gorica
AU  - Petrović, Marko D.
AU  - Ćurčić, Nina
AU  - Milenković, Milan
AU  - Malinović-Miličević, Slavica
AU  - Milovanović, Boško
AU  - Yamashkin, Anatoly A.
AU  - Milanović-Pešić, Ana
AU  - Lukić, Dobrila
AU  - Gajić, Mirjana
PY  - 2020
UR  - https://gery.gef.bg.ac.rs/handle/123456789/1072
AB  - The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of > 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%.
PB  - Basel : MDPI
T2  - Sustainability
T1  - Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece
VL  - 12
IS  - 24
DO  - 10.3390/su122410261
UR  - https://hdl.handle.net/21.15107/rcub_gery_1072
ER  - 

@article{
author = "Vyklyuk, Yaroslav and Radovanović, Milan and Stanojević, Gorica and Petrović, Marko D. and Ćurčić, Nina and Milenković, Milan and Malinović-Miličević, Slavica and Milovanović, Boško and Yamashkin, Anatoly A. and Milanović-Pešić, Ana and Lukić, Dobrila and Gajić, Mirjana",
year = "2020",
abstract = "The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of > 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%.",
publisher = "Basel : MDPI",
journal = "Sustainability",
title = "Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece",
volume = "12",
number = "24",
doi = "10.3390/su122410261",
url = "https://hdl.handle.net/21.15107/rcub_gery_1072"
}

Vyklyuk, Y., Radovanović, M., Stanojević, G., Petrović, M. D., Ćurčić, N., Milenković, M., Malinović-Miličević, S., Milovanović, B., Yamashkin, A. A., Milanović-Pešić, A., Lukić, D.,& Gajić, M.. (2020). Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece. in Sustainability
Basel : MDPI., 12(24).
https://doi.org/10.3390/su122410261
https://hdl.handle.net/21.15107/rcub_gery_1072

Vyklyuk Y, Radovanović M, Stanojević G, Petrović MD, Ćurčić N, Milenković M, Malinović-Miličević S, Milovanović B, Yamashkin AA, Milanović-Pešić A, Lukić D, Gajić M. Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece. in Sustainability. 2020;12(24).
doi:10.3390/su122410261
https://hdl.handle.net/21.15107/rcub_gery_1072 .

Vyklyuk, Yaroslav, Radovanović, Milan, Stanojević, Gorica, Petrović, Marko D., Ćurčić, Nina, Milenković, Milan, Malinović-Miličević, Slavica, Milovanović, Boško, Yamashkin, Anatoly A., Milanović-Pešić, Ana, Lukić, Dobrila, Gajić, Mirjana, "Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece" in Sustainability, 12, no. 24 (2020),
https://doi.org/10.3390/su122410261 .,
https://hdl.handle.net/21.15107/rcub_gery_1072 .