论文标题
使用支持向量机的大规模降水图的区域降雨预测
Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps
论文作者
论文摘要
降雨预测可帮助计划者预测降雨过多或太少会产生的潜在社会和经济影响。这项研究调查了提前1-30天的基于班级的降雨预测方法。该研究根据美国大陆的每日降雨图的序列进行了区域预测，其降雨量为3级：降雨或没有降雨；缓和;和大雨。选择了三个区域，对应于$ 5 \ times5 $网格覆盖地图区域的三个广场。这三个区域的降雨预测最多可提前30天，这是基于应用于先前每日降雨图图像的连续序列的支撑矢量机（SVM）。结果表明，与简单未经训练的分类器获得的预测相比，网格中的角正方形的预测不太准确。但是，对中央区域的SVM预测优于其他两个区域以及未训练的分类器。我们得出的结论是，有一些证据表明，在某些条件下，适用于大规模降水图的SVM可以为预测区域降雨提供有用的信息，但必须注意避免陷阱
Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a $5\times5$ grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfall
