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
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  - MDPI AG
T2  - Mathematics
T1  - Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods
SP  - 795
VL  - 11
IS  - 4
DO  - 10.3390/math11040795
UR  - https://hdl.handle.net/21.15107/rcub_dais_14049
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 = "MDPI AG",
journal = "Mathematics",
title = "Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods",
pages = "795",
volume = "11",
number = "4",
doi = "10.3390/math11040795",
url = "https://hdl.handle.net/21.15107/rcub_dais_14049"
}

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
MDPI AG., 11(4), 795.
https://doi.org/10.3390/math11040795
https://hdl.handle.net/21.15107/rcub_dais_14049

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
https://hdl.handle.net/21.15107/rcub_dais_14049 .

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 .,
https://hdl.handle.net/21.15107/rcub_dais_14049 .