Open AccessGenerating Rainfall Data using GANs
Author(s) -
A. Mary Posonia,
P. Amarendra Reddy,
Peram Aneesh. P
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b4958.129219
Subject(s) - computer science , generative grammar , adversarial system , classifier (uml) , artificial neural network , data type , machine learning , artificial intelligence , generative adversarial network , data mining , deep learning , programming language
Rainfall prediction is one of the major discussions in the meteorology because it is a major factor on which many things in the environment rely on. Neural Nets or any other machine learning algorithms need very large amount of data in order to achieve better accuracy but sometimes data can be scarce, this type of problems can be resolved by using Generative Adversarial Networks. Generative Adversarial Networks which are known for generating data by using the existing features from the old data, like generating images etc. There are many types of datasets which are scarce, rainfall data in one among them. So, the proposed system generates the rainfall data using GAN. The generated data is used for training the classifier, which predicts the rainfall.