论文标题
一种多模式的机器学习方法，以检测西西里岛的极端降雨事件
A Multi-Modal Machine Learning Approach to Detect Extreme Rainfall Events in Sicily
论文作者
论文摘要
在2021年，300毫米的降雨，几乎是平均年降雨量的一半，在卡塔尼亚（意大利西西里岛）附近。此类事件发生在短短几个小时内，对该地区的环境，社会，经济和卫生系统产生了巨大影响。这就是为什么检测极端降雨事件的原因是计划行动能够逆转可能加剧戏剧性的未来情况的关键先决条件。在本文中，根据我们的最大程度地了解了基于机器学习的聚类算法的亲和力传播算法，这是我们的最佳知识，以识别Sicily中的多余降雨事件。通过使用我们收集的高频，大数据集，从2009年到2021年，我们将其命名为RSE（降雨Sicily SiCily Extreme DataSet）。然后使用天气指标来验证结果，从而证实了西西里岛东部最近发生异常降雨事件的存在。我们认为，易于使用和多模式的数据科学技术，例如本研究中提出的一种技术，可能会导致成功与气候变化进行对比的政策制定。
In 2021 300 mm of rain, nearly half the average annual rainfall, fell near Catania (Sicily island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. This is the reason why, detecting extreme rainfall events is a crucial prerequisite for planning actions able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to identify excess rain events in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate changes.
