TY  - CONF
AU  - Radovanović, Milan M.
AU  - Malinović-Milićević, Slavica
AU  - Radenković, Sonja
AU  - Milenković, Milan
AU  - Milovanović, Boško
AU  - Milanović Pešić, Ana
AU  - Popović, Vladimir
PY  - 2022
UR  - https://dais.sanu.ac.rs/123456789/13864
AB  - This paper investigates hidden dependencies between the flow of particles coming from the Sun and 20 flood events in the United Kingdom (UK). The dataset analyzed in the study contains historical data covered on the daily level for the period October 2001 – December 2019. Solar activity parameters were used as model input, while rainfall data 10 days before and during each flood event were used as model output. To determine the degree of randomness for the time series of input and output parameters the correlation analysis has been performed. Machine Learning Classification Predictive Modelling is then applied to try to establish an eventual link between input and output data. Specifically, the decision tree, as the machine learning approach is used. In addition, it is analyzed the accuracy of classification models forecast. It is found that the most important factors for flood forecasting are proton density, differential proton flux in the range of 310-580 keV, and ion temperature. Research in this paper has shown that the classification model is accurate and adequate to predict the appearance of precipitation-induced floods.
PB  - Russian Federation : Faculty of Geography, Lomonosov Moscow State University
C3  - Рациональное природопользование: традиции и иновации. Материалы III Международной конференци
T1  - Influence of Space Weather on Precipitation-Induced Floods – Applying of Solar Activity Time Series in the Prediction of Precipitation-Induced Floods by Using the Machine Learning
SP  - 90
EP  - 97
UR  - https://hdl.handle.net/21.15107/rcub_dais_13864
ER  - 

@conference{
author = "Radovanović, Milan M. and Malinović-Milićević, Slavica and Radenković, Sonja and Milenković, Milan and Milovanović, Boško and Milanović Pešić, Ana and Popović, Vladimir",
year = "2022",
abstract = "This paper investigates hidden dependencies between the flow of particles coming from the Sun and 20 flood events in the United Kingdom (UK). The dataset analyzed in the study contains historical data covered on the daily level for the period October 2001 – December 2019. Solar activity parameters were used as model input, while rainfall data 10 days before and during each flood event were used as model output. To determine the degree of randomness for the time series of input and output parameters the correlation analysis has been performed. Machine Learning Classification Predictive Modelling is then applied to try to establish an eventual link between input and output data. Specifically, the decision tree, as the machine learning approach is used. In addition, it is analyzed the accuracy of classification models forecast. It is found that the most important factors for flood forecasting are proton density, differential proton flux in the range of 310-580 keV, and ion temperature. Research in this paper has shown that the classification model is accurate and adequate to predict the appearance of precipitation-induced floods.",
publisher = "Russian Federation : Faculty of Geography, Lomonosov Moscow State University",
journal = "Рациональное природопользование: традиции и иновации. Материалы III Международной конференци",
title = "Influence of Space Weather on Precipitation-Induced Floods – Applying of Solar Activity Time Series in the Prediction of Precipitation-Induced Floods by Using the Machine Learning",
pages = "90-97",
url = "https://hdl.handle.net/21.15107/rcub_dais_13864"
}

Radovanović, M. M., Malinović-Milićević, S., Radenković, S., Milenković, M., Milovanović, B., Milanović Pešić, A.,& Popović, V.. (2022). Influence of Space Weather on Precipitation-Induced Floods – Applying of Solar Activity Time Series in the Prediction of Precipitation-Induced Floods by Using the Machine Learning. in Рациональное природопользование: традиции и иновации. Материалы III Международной конференци
Russian Federation : Faculty of Geography, Lomonosov Moscow State University., 90-97.
https://hdl.handle.net/21.15107/rcub_dais_13864

Radovanović MM, Malinović-Milićević S, Radenković S, Milenković M, Milovanović B, Milanović Pešić A, Popović V. Influence of Space Weather on Precipitation-Induced Floods – Applying of Solar Activity Time Series in the Prediction of Precipitation-Induced Floods by Using the Machine Learning. in Рациональное природопользование: традиции и иновации. Материалы III Международной конференци. 2022;:90-97.
https://hdl.handle.net/21.15107/rcub_dais_13864 .

Radovanović, Milan M., Malinović-Milićević, Slavica, Radenković, Sonja, Milenković, Milan, Milovanović, Boško, Milanović Pešić, Ana, Popović, Vladimir, "Influence of Space Weather on Precipitation-Induced Floods – Applying of Solar Activity Time Series in the Prediction of Precipitation-Induced Floods by Using the Machine Learning" in Рациональное природопользование: традиции и иновации. Материалы III Международной конференци (2022):90-97,
https://hdl.handle.net/21.15107/rcub_dais_13864 .