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

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.

Editorial: Atmospheric disturbances: responses to phenomena from lithosphere to outer space

“…Therefore, the monitoring and understanding of spatio-temporal atmospheric changes are important for research in a number of scientific disciplines as well as for geoinformation technologies. Here, first of all, the importance of research into atmospheric changes related to natural disasters should be emphasized (Molchanov et al, 2004;Price et al, 2007;Maurya et al, 2016;Kumar et al, 2017;Vyklyuk et al, 2017Vyklyuk et al, , 2019Manta et al, 2020;Malinović-Milićević et al, 2023). For example, the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model based on the effects of ionisation provided by radon released from active tectonic faults before earthquakes is created in Pulinets et al (2022).…”