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Rainfall prediction helps planners anticipate potential social and economicimpacts produced by too much or too little rain. This research investigates aclass-based approach to rainfall prediction from 1-30 days in advance. Thestudy made regional predictions based on sequences of daily rainfall maps ofthe continental US, with rainfall quantized at 3 levels: light or no rain;moderate; and heavy rain. Three regions were selected, corresponding to threesquares from a $5\times5$ grid covering the map area. Rainfall predictions upto 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. Theresults show that predictions for corner squares in the grid were less accuratethan predictions obtained by a simple untrained classifier. However, SVMpredictions for a central region outperformed the other two regions, as well asthe untrained classifier. We conclude that there is some evidence that SVMsapplied to large-scale precipitation maps can under some conditions give usefulinformation for predicting regional rainfall, but care must be taken to avoidpitfall

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