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

TL;DR

This study explores using support vector machines to predict regional rainfall levels up to 30 days in advance based on sequences of daily precipitation maps, showing promising results especially for central regions.

Contribution

It introduces a class-based SVM approach for large-scale rainfall prediction from precipitation maps, highlighting regional differences in prediction accuracy.

Findings

SVM predictions outperform untrained classifiers in central regions.

Prediction accuracy varies across different regions.

SVM can provide useful regional rainfall forecasts under certain conditions.

Abstract

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