A Hybrid Genetic Algorithm Based Rainfall Prediction Model Using Deep Neural Network
P. Satish1, S. Srinivasulu2, R. Swathi3

1P. Satish*, CSE, Sri Venkateswara college of Engineering, Tirupati, India.
2S. Srinivasulu, CSE, Sri Venkateswara college of Engineering, Tirupati, India.
3Dr.R. Swathi, CSE, Sri Venkateshwara College of Engineering, Tirupati, India.

Manuscript received on September 15, 2019. | Revised Manuscript received on 29 September, 2019. | Manuscript published on October 10, 2019. | PP: 5370-5373| Volume-8 Issue-12, October 2019. | Retrieval Number: L37771081219/2019©BEIESP | DOI: 10.35940/ijitee.L3777.1081219
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Abstract: This paper is intended to design and develop an efficient prediction algorithm in the context of forecasting in the precipitation as well as the intensity of the rainfall in a local region over a relative short period of time. Despite of several existing addressing the problem of forecasting rainfall, still it is remind as an open challenge in the context of dynamic data acquisition and aggression. Prediction of rain is main application of science and technology to predict the state of the atmosphere. The main objective of the study is to develop a Genetic Algorithm based approach that utilize dimensionality reduction technique and Multi-layer Perceptron for efficient and dynamic analysis of real time data. Furthermore a deep neural networks based framework is proposed to predict rainfall of a certain region. A Hybrid genetic algorithm presents a novel solution to predicting the rainfall in certain area or different regions by using Deep Neural Network.
Keywords: Deep Neural Network, Genetic Algorithm, Multi-layer perceptron, Precipitation..
Scope of the Article: Algorithm Engineering