Abstract We use machine learning to generate binary forecasts of the occurrence of lightning within a particular location and time interval. The training data is weather variables found in the forecasts from the European Centre for Medium-range Weather Forecasts, correlated against subsequent lightning reports, for a region containing the Korean Peninsula. Lightning is uncommon, so the amount of data which does not involve lightning tends to swamp the training process. Thus we only consider spatial locations at which lightning frequently occurs, and we also undersample the subset of the remaining data-points which are not associated with lightning. Results from support vector machines and random forests had equitable threat scores of 0.0885 and 0.0828, respectively. The ETS of results from SVMs can be increased to 0.1241 if temporal resolution is reduced by a factor of 2, and 0.1499 if spatial resolution is reduced by a factor of 3.

中文翻译：

---

通过使用 SVM 和欠采样后处理 ECMWF 数据来预测朝鲜半岛周围的闪电

摘要 我们使用机器学习来生成特定位置和时间间隔内闪电发生的二进制预测。训练数据是在欧洲中期天气预报中心的预测中发现的天气变量，与随后的闪电报告相关联，针对包含朝鲜半岛的地区。闪电并不常见，因此不涉及闪​​电的数据量往往会淹没训练过程。因此，我们只考虑闪电频繁发生的空间位置，并且我们还对与闪电无关的剩余数据点的子集进行了欠采样。支持向量机和随机森林的结果分别具有 0.0885 和 0.0828 的公平威胁分数。SVM 结果的 ETS 可以增加到 0。