Regional accuracy was examined using extreme gradient boosting (XGBoost) to improve frost prediction accuracy, and accuracy differences by region were found. When the points were divided into two groups with weather variables, Group 1 had a coastal climate with a high minimum temperature, humidity, and wind speed and Group 2 exhibited relatively inland climate characteristics. We calculated the accuracy in the two groups and found that the precision and recall scores in coastal areas (Group 1) were significantly lower than those in the inland areas (Group 2). Geographic elements (distance from the nearest coast and height) were added as variables to improve accuracy. In addition, considering the continuity of frost occurrence, the method of reflecting the frost occurrence of the previous day as a variable and the synthetic minority oversampling technique (SMOTE) pretreatment were used to increase the learning ability.

中文翻译：

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考虑地理特征的霜冻预报

使用极端梯度提升（XGBoost）检查区域精度以提高霜冻预测精度，并发现不同区域的精度差异。根据天气变量将点分为两组时，第一组为沿海气候，最低气温、湿度和风速都较高，第二组则表现出相对内陆的气候特征。我们计算了两组的准确率，发现沿海地区（第 1 组）的准确率和召回分数明显低于内陆地区（第 2 组）。地理元素（与最近海岸的距离和高度）被添加为变量以提高准确性。此外，考虑到霜冻发生的连续性，