Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods

Slavica Malinović-Milićević (), Milan M. Radovanović, Sonja D. Radenković, Yaroslav Vyklyuk, Boško Milovanović, Ana Milanović Pešić, Milan Milenković, Vladimir Popović, Marko Petrović, Petro Sydor and Mirjana Gajić
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Slavica Malinović-Milićević: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Milan M. Radovanović: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Sonja D. Radenković: Belgrade Banking Academy–Faculty of Banking, Insurance, and Finance, Union University, 11000 Belgrade, Serbia
Yaroslav Vyklyuk: Department of Artificial Intelligence Systems, Lviv Polytechnic National University, Lviv, Bandera str, 12, 79013 Lviv, Ukraine
Boško Milovanović: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Ana Milanović Pešić: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Milan Milenković: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Vladimir Popović: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Marko Petrović: Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000 Belgrade, Serbia
Petro Sydor: Department of Computer Systems and Technologies, Faculty of Information Technologies and Economics, Bukovinian University, 2A Darwin St., 58000 Chernivtsi, Ukraine
Mirjana Gajić: Faculty of Geography, University of Belgrade, Studentski trg 3/III, 11000 Belgrade, Serbia

Mathematics, 2023, vol. 11, issue 4, 1-20

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.

Keywords: solar activity; precipitation; floods; machine learning; classification; modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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