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Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods

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Abstract

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009–2018 were used. Solar activity parameters with a time lag of 0–11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9 days in advance. Differential proton flux in the 0.068–0.115 MeV and integral proton flux > 2.5 MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

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Data availability

The dataset on integral proton flux, differential electron and proton flux, proton density, bulk speed, and ion temperature is available at https://izw1.caltech.edu/ACE/ASC/level2/new/intro.html. The 10.7 cm radio flux data is available at http://www.spaceweather.gc.ca/solarflux/sx-5-flux-en.php. Precipitation data is available at https://cds.climate.copernicus.eu/.

References

  • ACE Level 2 Data Server. https://izw1.caltech.edu/ACE/ASC/level2/new/intro.html. Accessed on 10 Aug 2023

  • Berrar, D. (2019). Cross-validation. In S. Ranganathan, M. Gribskov, K. Nakai, C. & Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology, (Volume 1, pp. 542–545). Elsevier

  • Bertola, M., Viglione, A., Lun, D., Hall, J., & Blöschl, G. (2020). Flood trends in Europe: Are changes in small and big floods different? Hydrology and Earth System Sciences, 24, 1805–1822. https://doi.org/10.5194/hess-24-1805-2020

    Article  Google Scholar 

  • Bhattacharyya, S., & Narasimha, R. (2005). Possible association between Indian monsoon rainfall and solar activity. Geophysical Research Letters, 32, L05813. https://doi.org/10.1029/2004GL021044

    Article  Google Scholar 

  • Blenkinsop, S., Muniz Alves, L., Smith, A. J. P. (2021). Climate change increases extreme rainfall and the chance of floods. In C. Le Quéré, P. Liss, P. Forster (Eds.), Critical Issues in Climate Change Science. ScienceBrief: Norwich, UK.https://doi.org/10.5281/zenodo.4779119

  • Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A. P., Parajka, J., Merz, B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A., et al. (2019). Changing climate both increases and decreases European river floods. Nature, 573, 108–111. https://doi.org/10.1038/s41586-019-1495-6

    Article  CAS  Google Scholar 

  • Cappelli, F., Tauro, F., Apollonio, C., Petroselli, A., Borgonovo, E., & Grimaldi, S. (2023). Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: A proof of concept. Stochastic Environmental Research and Risk Assessment, 37, 1247–1264. https://doi.org/10.1007/s00477-022-02332-w

    Article  Google Scholar 

  • Copernicus Climate Change Service, Climate Data Store (2023). E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. Retrieved on April 4, 2023, https://doi.org/10.24381/cds.151d3ec6

  • Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., & Jones, P. D. (2018). An ensemble version of the E-OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123(17), 9391–9409. https://doi.org/10.1029/2017JD028200

    Article  Google Scholar 

  • CRED. (2023). 2022 Disasters in numbers. Centre for Research on the Epidemiology of Disasters (CRED). Retrieved April 10, 2023, from https://cred.be/sites/default/files/2022_EMDAT_report.pdf

  • Danilovich, I. S., Loginov, V. F., & Groisman, P. Y. (2023). Changes of hydrological extremes in the center of Eastern Europe and their plausible causes. Water, 15(16), 2992. https://doi.org/10.3390/w15162992

    Article  Google Scholar 

  • Dong, L., Fu, C., Liu, J., & Zhang, P. (2018). Combined effects of solar activity and El Niño on hydrologic patterns in the Yoshino River Basin, Japan. Water Resources Management, 32, 2421–2435. https://doi.org/10.1007/s11269-018-1937-1

    Article  Google Scholar 

  • EM-DAT. (2023). Emergency events database, Flood Data Store in Europe from 2009 to 2018. Retrieved on April 10, 2023, from https://public.emdat.be/data

  • Fu, C., James, A. L., & Wachowiak, M. P. (2012). Analyzing the combined influence of solar activity and El Niño on streamflow across southern Canada. Water Resources Research, 48(5), W05507. https://doi.org/10.1029/2011WR011507

    Article  Google Scholar 

  • Georgiadi, A. G., & Groisman, P. Y. (2023). Extreme low flow during long-lasting phases of river runoff in the central part of the East European Plain. Water, 15(12), 2146. https://doi.org/10.3390/w15122146

    Article  Google Scholar 

  • Graczyk, M., Lasota, T., Trawiński, B., & Trawiński, K. (2010). Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal. In N. T. Nguyen, M. T. Le, J. Świątek (Eds.), Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science (pp. 340–350). Springer

  • Hagiwara, M., & Tanaka, H. L. (2020). A theoretical analysis of the atmospheric gravity wave that connects the thermosphere and the troposphere. Tsukuba Geoenvironmental Sciences, 16, 1–14.

    Google Scholar 

  • Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriaučiūnienė, J., Kundzewicz, Z. W., Lang, M., et al. (2014). Understanding flood regime changes in Europe: A state-of-the-art assessment. Hydrology and Earth System Sciences, 18(7), 2735–2772. https://doi.org/10.5194/hess-18-2735-2014

    Article  Google Scholar 

  • Heinrich, P., Hagemann, S., Weisse, R., & Gaslikova, L. (2023). Changes in compound flood event frequency in northern and central Europe under climate change. Frontiers in Climate, 5, 1227613. https://doi.org/10.3389/fclim.2023.1227613

    Article  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC) (2007). Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., Hanson, C. E., Eds.; Cambridge University Press: Cambridge, UK

  • Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., et al. (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology, 22, 1441–1453. https://doi.org/10.1002/joc.773

    Article  Google Scholar 

  • Klok, E. J., & Klein Tank, A. M. G. (2009). Updated and extended European dataset of daily climate observations. International Journal of Climatology, 29(8), 1182–1191. https://doi.org/10.1002/joc.1779

    Article  Google Scholar 

  • Krapivin, V. F., Soldatov, V. Y., Varotsos, C. A., & Cracknell, A. P. (2012). An adaptive information technology for the operative diagnostics of the tropical cyclones; solar-terrestrial coupling mechanisms. Journal of Atmospheric and Solar-Terrestrial Physics, 89(1), 83–89. https://doi.org/10.1016/j.jastp.2012.08.009

    Article  Google Scholar 

  • Kundzewicz, Z. W., Krysanova, V., Dankers, R., Hirabayashi, Y., Kanae, S., Hattermann, F. F., Huang, S., Milly, P. C. D., Stoffel, M., Driessen, P. P. J., et al. (2017). Differences in flood hazard projections in Europe – Their causes and consequences for decision making. Hydrological Science Journal, 62(1), 1–14. https://doi.org/10.1080/02626667.2016.1241398

    Article  Google Scholar 

  • Landscheidt, T. (2000). Solar wind near earth: Indicator of variations in global temperature. The solar cycle and terrestrial climate, Solar and space weather Euroconference. In: Proceedings of the 1st Solar and Space Weather Euroconference, 25-29 September 2000, Santa Cruz de Tenerife, Tenerife, Spain. Edited by A. Wilson. Noordwijk, Netherlands: ESA Publications Division, 2000 xi, 680 p. ESA SP, Vol. 463, p.497, 2000ESASP.463..497L. https://adsabs.harvard.edu/full/2000ESASP.463..497L

  • Leščešen, I., Basarin, B., Mudelsee, M., & Wilby, R. L. (2023). Is the flood occurrence rate decreasing in Southeast Europe? Presented at EGU General Assembly 2023 (p. 2023). Vienna: Austria.

    Google Scholar 

  • Macdonald, N. J., & Roberts, W. O. (1960). Further evidence of a solar corpuscular influence on large-scale circulation at 300 Mb. Journal of Geophysical Research, 65, 529–534. https://doi.org/10.1029/JZ065i002p00529

    Article  Google Scholar 

  • 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. Mathematics, 11(4), 795. https://doi.org/10.3390/math11040795

    Article  Google Scholar 

  • Mares, C., Mares, I., Dobrica, V., & Demetrescu, C. (2023). Discriminant analysis of the solar input on the Danube’s discharge in the Lower Basin. Atmosphere, 14(8), 1281. https://doi.org/10.3390/atmos14081281

    Article  Google Scholar 

  • Natural Resources Canada. (2023). Space weather Canada, solar radio flux, daily flux values. https://www.spaceweather.gc.ca/solarflux/sx-5-flux-en.php. Retrieved on August 10, 2023

  • Newell, M., Drews, M., Payne, M., & Larsen, M. (2023). Compound hydrometeorologic extremes in Northern Europe: A case study on flooding and drought in Denmark. Presented at EMS Annual Meeting 2023, Bratislava, Slovakia, 2023

  • Park, C. G. (1976). Solar magnetic sector effects on the vertical atmospheric electric field at Vostok. Antarctica. Geophysical Research Letters, 3(8), 475–478. https://doi.org/10.1029/GL003i008p00475

    Article  Google Scholar 

  • Prikryl, P., & Rušin, V. (2023). Occurrence of heavy precipitation influenced by solar wind high-speed streams through vertical atmospheric coupling. Frontiers in Astronomy and Space Sciences, 10, 1196231. https://doi.org/10.3389/fspas.2023.1196231

    Article  Google Scholar 

  • Prikryl, P., Rušin, V., Prikryl, E. A., Št’astný, P., Turňa, M., & Zeleňáková, M. (2021). Heavy rainfall, floods, and flash floods influenced by high-speed solar wind coupling to the magnetosphere–ionosphere–atmosphere system. Annales Geophysicae, 39(4), 769–793. https://doi.org/10.5194/angeo-39-769-2021

    Article  Google Scholar 

  • Raileanu, L. E., & Stoffel, K. (2004). Theoretical comparison between the Gini Index and information gain criteria. Annals of Mathematics and Artificial Intelligence, 41(1), 77–93. https://doi.org/10.1023/B:AMAI.0000018580.96245.c6

    Article  Google Scholar 

  • Rasheed, Z., & Nikolopoulos, E. (2022). Flood frequency estimation at global scale: Exploring the power of machine learning combined with satellite precipitation estimates. Presented at AGU Fall Meeting 2022, Chicago, IL, USA, 2022

  • Roberts, W. O., & Olson, R. H. (1973). Geomagnetic storms and wintertime 300-mb trough development in the North Pacific-North America area. Journal of the Atmospheric Sciences, 30, 135–140. https://doi.org/10.1175/1520-0469(1973)030%3c0135:GSAWMT%3e2.0.CO;2

    Article  Google Scholar 

  • Roudier, P., Andersson, J. C. M., Donnelly, C., Feyen, L., Greuell, W., & Ludwig, F. (2016). Projections of future floods and hydrological droughts in Europe under a +2°C global warming. Climate Change, 135(2), 341–355. https://doi.org/10.1007/s10584-015-1570-4

    Article  Google Scholar 

  • Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. EEE Transactions on Systems, Man, and Cybernetics, 21(3), 660–674. https://doi.org/10.1109/21.97458

    Article  Google Scholar 

  • Schmidt, L., Heße, F., Attinger, S., & Kumar, R (2020) Challenges in applying Machine Learning Models for hydrological inference: A case study for flooding events across Germany Water Resources Research, 56(5), e2019WR025924. https://doi.org/10.1029/2019WR025924

  • Srećković, V. A., Šulić, D. M., Vujčić, V., Jevremović, D., & Vyklyuk, Y. (2017). The effects of solar activity: Electrons in the terrestrial lower ionosphere. Journal of the Geographical Institute “Jovan Cvijic” SASA, 67(3), 221–233. https://doi.org/10.2298/IJGI1703221S

    Article  Google Scholar 

  • Stevančević, M., Radovanović, M., & Todorović, N. (2004). The possibility of application of electromagnetic method in mid term weather forecasting. In: Proceedings of the Collection of Papers EkoIst'04 Ecological Truth (pp. 396–399). Bor, Serbia

  • Stevančević, M., Radovanović, M., & Todorović, N. (2006). Analysis of characteristic mistakes in the heliocentric electromagnetic long-term forecast. In: Proceedings of the “Tourist Valorisation of Tara” Theme Collection of the Geographical Institute “Jovan Cvijic” SASA and Sport-Recreative Center Bajina Bašta (pp. 101–110). Belgrade, Serbia.

  • Stevančević, M. T., Todorović, N., Radovanović, M., Ducić, V., & Milenković, M. (2012). In: Stevančević, M.T. (Ed.) Belgrade School of Meteorology (Volume 5, pp. 24–218) Milan T. Stevančević: Belgrade, Serbia

  • Svalgaard, L. (1974). Solar activity and the weather. In D. E. Page (Ed.), Correlated Interplanetary and Magnetospheric Observations. Astrophysics and Space Science Library (Vol. 42, pp. 627–639). Springer

  • Todorović, N., & Vujović, D. (2014). Effect of solar activity on the repetitiveness of some meteorological phenomena. Advances in Space Research, 54(11), 2430–2440. https://doi.org/10.1016/j.asr.2014.08.007

    Article  Google Scholar 

  • Trostianchyn, A., Duriagina, Z., Izonin, I., Tkachenko, R., Kulyk, V., & Pavliuk, O. (2021). Sm-Co alloys coercivity prediction using stacking heteroge-neous ensemble mode. Acta Metallica Slovaca, 27(4), 195–202. https://doi.org/10.36547/ams.27.4.1173

    Article  Google Scholar 

  • Tyralis, H., Papacharalampous, G., & Langousis, A. (2021). Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms. Neural Computing and Applications, 33, 3053–3068. https://doi.org/10.1007/s00521-020-05172-3

    Article  Google Scholar 

  • Veretenenko, S., & Thejll, P. (2004). Effects of energetic solar proton events on the cyclone development in the North Atlantic. Journal of Atmospheric and Solar-Terrestrial Physics, 66(5), 393–405. https://doi.org/10.1016/j.jastp.2003.11.005

    Article  CAS  Google Scholar 

  • Vyklyuk, Y., Radovanović, M. M., Stanojević, G. B., Milovanović, B., Leko, T., Milenković, M., Petrović, M., Yamashkin, A. A., Milanović Pešić, A., Jakovljević, D., & Malinović Milićević, S. (2018). Hurricane genesis modelling based on the relationship between solar activity and hurricanes II. Journal of Atmospheric and Solar-Terrestrial Physics, 180, 159–164. https://doi.org/10.1007/s11069-016-2620-6

    Article  Google Scholar 

  • Vyklyuk, Y., Radovanović, M. M., Milovanović, B., Milenković, M., Petrović, M., Doljak, D., Malinović Milićević, S., Vuković, N., Vujko, A., Matsiuk, N., & Mukherjee, S. (2019). Space weather and hurricanes Irma, Jose and Katia. Astrophysics and Space Science, 364, 154. https://doi.org/10.1007/s10509-019-3646-5

    Article  Google Scholar 

  • Wilcox, J. M. (1979). Tropospheric circulation and interplanetary magnetic sector boundaries followed by MeV proton streams. Nature, 278, 840–841. https://doi.org/10.1038/278840a0

    Article  Google Scholar 

  • Wilcox, J. M., & Scherrer, P. H. (1981). On the nature of the apparent response of the vorticity area index to the solar magnetic field. Solar Physics, 74, 421–432. https://doi.org/10.1007/BF00154528

    Article  Google Scholar 

  • Wilcox, J. M., Scherrer, P. H., Svalgaard, L., Roberts, W. O., & Olson, R. H. (1973). Solar magnetic sector structure: Relation to circulation of the Earth’s atmosphere. Science, 180, 185–186. https://doi.org/10.1126/science.180.4082.185

    Article  CAS  Google Scholar 

  • Wilcox, J. M., Scherrer, P. H., Svalgaard, L., Roberts, W. O., Olson, R. H., & Jenne, R. L. (1974). Influence of solar magnetic sector structure on terrestrial atmospheric vorticity. Journal of the Atmospheric Sciences, 31(2), 581–588. https://doi.org/10.1175/1520-0469(1974)031%3c0581:IOSMSS%3e2.0.CO;2

    Article  Google Scholar 

  • Zhao, G., Bates, P., Neal, J., & Pang, B. (2021). Design flood estimation for global river networks based on machine learning models. Hydrology and Earth System Sciences, 25(11), 5981–5999. https://doi.org/10.5194/hess-25-5981-2021

    Article  Google Scholar 

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Acknowledgements

We thank the ACE SWEPAM instrument team and the ACE Science Center for providing the ACE data.

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Authors and Affiliations

  1. Geographical Institute “Jovan Cvijić” SASA, 9 Djure Jakšića St., 11000, Belgrade, Serbia

    Slavica Malinović-Milićević, Milan M. Radovanović, Milan Milenković, Ana Milanović Pešić, Boško Milovanović, Teodora Popović & Marko D. Petrović

  2. Department of Artificial Intelligence Systems at, Lviv Polytechnic National University, Bandera Str, 12, Lviv, 79013, Ukraine

    Yaroslav Vyklyuk

  3. Department of Computer System Software at Chernivtsi Yu, Fedkovych National University, 2 Kotsyubinsky Str., Chernivtsi, 58012, Ukraine

    Petro Sydor

Authors
  1. Slavica Malinović-Milićević
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  3. Milan M. Radovanović
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  4. Milan Milenković
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  6. Boško Milovanović
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Contributions

Slavica Malinović-Milićević: conceptualization, methodology, validation, formal analysis, writing the original draft, reviewing, and editing the manuscript, collection, visualization, supervision, project administration; Yaroslav Vyklyuk: conceptualization, software, validation, formal analysis, investigation, writing the original draft, reviewing, and editing the manuscript, review and editing, visualization; Milan M. Radovanović: conceptualization, methodology, investigation, writing the original draft, reviewing, and editing the manuscript, project administration, supervision; Milan Milenković: data collection, reviewing, and editing the manuscript, project administration; Ana Milanović Pešić: data collection, writing the original draft, reviewing, and editing the manuscript; Boško Milovanović: reviewing, and editing the manuscript; Teodora Popović: reviewing, and editing the manuscript, data collection; Petro Sydor: software; Marko D. Petrović: reviewing, and editing the manuscript. The authors approved the version to be published.

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Correspondence to Slavica Malinović-Milićević.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 Online Resource 1: Detailed information about studied floods. (XLSX 36 KB)

10661_2024_12537_MOESM2_ESM.xlsx

Supplementary file2 Online Resource 2: Maximum values of correlation coefficients between input factors and precipitations and corresponding lag. (XLSX 34 KB)

10661_2024_12537_MOESM3_ESM.tif

Supplementary file3 Online Resource 3: Decision Tree of flood forecast taking into account the lag delay from 0 to 11 days. (TIF 97635 KB)

Supplementary file4 Online Resource 4: Decision trees for (a) five and (b) eleven days forecast. (TIFF 6212 KB)

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Malinović-Milićević, S., Vyklyuk, Y., Radovanović, M.M. et al. Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods. Environ Monit Assess 196, 400 (2024). https://doi.org/10.1007/s10661-024-12537-x

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