ENSEMBLE MODELS FOR FORECASTING FLOODS IN GREAT BRITAIN BASED ON SOLAR ACTIVITY

Abstract

This study aims to uncover hidden dependencies between streams of solar particles emanating from the Sun and flood events caused by precipitation in the UK. The uniqueness of this work lies in applying solar activity data as input parameters for machine learning and correlating them with real data on precipitation intensity and floods in the UK, which serve as output data. The analysis covered 20 significant floods from October 2001 to December 2019. The analysis was performed on a daily basis, taking into account the time shift between solar emissions and their impact on terrestrial weather conditions, which ranged from 0 to 9 days. The study employs correlation analysis to determine the degree of interrelation between time series of solar activity and floods, laying the foundation for a deeper understanding of possible cause-and-effect relationships. Subsequently, using predictive modeling methods, including decision tree algorithms and ensemble classification models, the potential connection between changes in solar activity and terrestrial floods was explored. An ensemble of models was created using the hard voting method, which takes into account various indicators such as proton density, differential proton flux, and ion temperature, with a time shift of up to 9 days. The analysis showed that these key parameters could effectively predict the occurrence of floods with up to 92% accuracy for the specified period. The results underscore the importance of integrating space weather and solar activity into the models for predicting terrestrial weather conditions, particularly in the context of floods in England. This not only allows for a better understanding of the causes of natural disasters but also enhances the effectiveness of emergency planning and reduces potential damages from them. Thus, this work makes a significant contribution to the field of space weather research and its impact on Earth, particularly in the areas of hydrometeorology and flood risk management.