Rainfall Estimation Using Transductive Learning

Precipitation is a crucial link in the hydrological cycle, and its spatial and temporal variations are enormous. A knowledge of the amount of regional rainfall is essential to the welfare of society. Rainfall can be estimated remotely, either from ground-based weather radars or from satellite. Despi...

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Bibliographic Details
Main Authors: Martins de Freitas, Greice, Heuminski de Ávila, Ana Maria, Papa, João Paulo
Format: Conference Proceeding
Language:English
Subjects:
Agriculture
Humans
Infrared imaging
Meteorological radar
Meteorology
NOAA
rainfall estimation
Satellites
semi-supervised support vector machines
Semisupervised learning
Support vector machine classification
Support vector machines
transductive learning
Virtual manufacturing
Online Access:Request full text
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Summary:Precipitation is a crucial link in the hydrological cycle, and its spatial and temporal variations are enormous. A knowledge of the amount of regional rainfall is essential to the welfare of society. Rainfall can be estimated remotely, either from ground-based weather radars or from satellite. 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%.
DOI:10.1109/CISP.2008.561