A Novel Method of Rainfall Prediction using MLP-FFN and Hybrid Neural Network Algorithm
G. Thailambal1, P. Shanmugalakshmi2, R. Durga3

1Dr. G. Thailambal, Associate Professor, Department of Computer Science, VISTAS, Chennai, India.
2Ms. P. Shanmugalakshmi, Research Scholar, Department of Computer Science, VISTAS, Chennai, India.
3Dr. R. Durga, Assistant Professor, Department of Computer Science, VISTAS, Chennai, India.

Manuscript received on 11 August 2019 | Revised Manuscript received on 18 August 2019 | Manuscript published on 30 August 2019 | PP: 2858-2862 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96070881019/2019©BEIESP | DOI: 10.35940/ijitee.J9607.0881019
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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: The present work proposes a cross breed neural system and multilayer perceptron_ feed forward system based model for precipitation forecast. The crossover models are multistep technique. At first, the information is bunched into a sensible number of groups, at that point for each bunch has prepared independently by Neural Network (NN). Also, as a preprocessing stages a component choice stage is incorporated. Feed forward choice calculation is utilized to locate the most reasonable arrangement of highlights for foreseeing precipitation. To set up the creativity of the proposed cross breed forecast model (Hybrid Neural Network or HNN) has been contrasted and two surely understood models in particular multilayer perceptron feed-forward system (MLP-FFN) utilizing diverse execution measurements. The reproduction results have uncovered that the proposed model is essentially superior to conventional strategies in anticipating precipitation.
Keywords: Rainfall Prediction, Multi-Layer Perceptron, Neural Network, Hybrid Model.

Scope of the Article: Regression and Prediction