ASSESSMENT OF NEURAL NETWORK SCHEMES TO CLASSIFY CLOUD DATA

FAISAL HOSSAIN
TUFA DINKU
NEERAJ AGARWAL
EMMANOUIL N. ANAGNOSTOU

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DOI: 10.2190/9VHU-BXA4-WK48-T3DY

Abstract

Using remotely-sensed data from the Tropical Rainfall Measuring Mission (TRMM), a cloud classification study was undertaken employing neural network schemes. The objective of this study was to assess the accuracy of each scheme for classifying clouds. In the first phase, a data preprocessing and feature selection scheme was undertaken to identify a suitable set of features that could be useful in cloud classification. In the next phase, seven neural network classification schemes were implemented to understand the utility of each of these schemes. Parametric schemes performed poorly, while the perceptron, K-nearest neighbor approaches and the least means square algorithm yielded promising results. Further study is proposed so as to improve rainfall prediction.

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