arXiv 2007.154042020-07-30cs.LGstat.ML

基于大尺度降水图支持向量机分类的区域降水预测

Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps

University of the Western Cape（西开普大学）；University-Central Texas（中央德克萨斯大学）

Eslam A. Hussein, Mehrdad Ghaziasgar, Christopher Thron

摘要：

降雨预测有助于规划者预测降雨过多或过少可能产生的社会和经济影响。本研究探讨一种基于类别的方法，从1-30天的降雨预报提前。该研究根据美国大陆的每日降雨图序列进行了区域预测，降雨量量化为3个级别：小雨或无雨;中雨;大雨。从覆盖地图区域的$5\times5$网格中选择三个区域，对应于三个正方形。这三个地区未来30天的降雨预测是基于支持向量机（SVM）适用于连续序列的前每日降雨量图图像。结果表明，对网格中的角点正方形的预测不如简单的未经训练的分类器获得的预测准确。然而，中心区域的SVM预测优于其他两个区域以及未经训练的分类器。我们的结论是，有一些证据表明，支持向量机应用于大尺度降水图可以在某些条件下提供有用的信息，预测区域降雨，但必须注意避免陷阱

英文摘要：

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

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