Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region.
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