Identification and threshold analysis of strong winds and heavy rain disaster factors based on frequent-pattern mining

Abstract

Meteorological disasters pose a significant challenge to megacities, and quantitatively identifying the influence of meteorological conditions on disasters is an urgent issue. Compared to other traditional datasets, disaster reporting data offers significant informational value for urban meteorological risk response and assessment due to its high credibility and timeliness. This study is based on meteorological disaster reporting data for Shanghai over the past 10 years and focuses on the identification and threshold analysis of meteorological disaster risk factors for disaster-affected entities, offering a novel perspective on analyzing disaster-causing risk under the combined influence of meteorological conditions. The findings suggest that disaster reporting data can be used for meteorological risk assessment. There were noticeable differences in the disaster thresholds for affected entities in central urban areas (city center) versus suburban areas (suburbs). The combined effects of strong winds and heavy rain were often significant contributors to the occurrence of disasters. Furthermore, several distinct disaster characteristics were identified through frequent pattern (FP)-growth mining. The results of this study provide technical support for early warning and disaster management under specific weather conditions by constructing a quantitative framework for understanding the impact of hazardous weather.