Generating Rainfall Data using GANs
A. Mary Posonia1, P. Sai Gowtham Reddy2, Peram Aneesh. P3
1A. Mary Posonia, Associate Professor, Department of Computer Science, Sathyabama Institute of Science and Technology, Chennai, India
2P. Sai Gowtham Reddy, UG Student , Department of Computer Science and Engineering, Sathyabama Institute of Science & Technology, Chennai, India
3Peram Aneesh. P, UG Student , Department of Computer Science and Engineering, Sathyabama Institute of Science & Technology, Chennai, India
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4124-4127 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4958129219/2019©BEIESP | DOI: 10.35940/ijeat.B4958.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Neural Network, Machine learning, Discriminator, Back propagation Network.