Regional rainfall prediction using support vector machine classification of large-scale precipitation maps

dc.contributor.authorHussein, Eslam A.
dc.contributor.authorGhaziasgar, Mehrdad
dc.contributor.authorThron, Christopher
dc.date.accessioned2021-01-12T07:24:21Z
dc.date.available2021-01-12T07:24:21Z
dc.date.issued2020
dc.description.abstractRainfall 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 × 5 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 pitfalls.en_US
dc.identifier.citationHussein, E. A. et al. (2020). Regional rainfall prediction using support vector machine classification of large-scale precipitation maps. Proceedings of 2020 23rd International Conference on Information Fusion, FUSION. Pretoria.en_US
dc.identifier.uri10.23919/FUSION45008.2020.9190285
dc.identifier.urihttp://hdl.handle.net/10566/5638
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectWater resourcesen_US
dc.subjectImagesen_US
dc.subjectSVMsen_US
dc.subjectVector machineen_US
dc.subjectRainfallen_US
dc.titleRegional rainfall prediction using support vector machine classification of large-scale precipitation mapsen_US
dc.typeArticleen_US

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