Transductive Support Vector Machines Applied to Rainfall Estimation

Abstract

Meteorological conditions are determinative for the agricultural production in the country,
rainfall, in particular, can be cited as most influential by having direct relation with hydric balance. Indeed, agrometeorological models, based on the cultures answers to the meteorological conditions, have been used with meteorological satellites data. Despite the large amount of available data provided by satellites, most of them are unlabeled, and the acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. The semi-supervised learners combine both labeled and unlabeled data to perform the classification task. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S3VM). The S3VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. The accuracies obtained for SVM and S3VM were, respectively, 90.6% and 95.96%.