Rainfall Prediction using Genetic Algorithm
J. Refonaa1, M. Lakshmi2, R S S Srinivasa Rao3, P Eshwar Prasad4
1J. Refonaa, Assistant Professor, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Dr. M. Lakshmi, Principal, Professor, Sri Krishna College of Technology, Coimbatore (Tamil Nadu), India.
3R S S Srinivasa Rao, Student, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
4P Eshwar Prasad, Student, JNTUH College of Engineering, Hyderabad (Telangana), India.
Manuscript received on 19 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 597-600 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11100782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1110.0782S319
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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: Numerousresearch works are being carried out by various people to predict the occurrence of rainfall before it actually comes so as to minimize the amount of damage to a particular area or make the people of the area aware of the rain hit so that they could take some previous safety measures. Various classifiers and artificial neural networks are preferred by various researchers to predict the rain in a given geographic area. Use of genetic algorithms have seen in intense use for various research purposes and prediction of weather is not an exception. In this paper, we have proposed a system that could predict the rainbeforehand using genetic algorithm. Matlab is used for observing the performance of the algorithm. Genetic algorithm is used for feature extraction from the input dataset. When compared to other approaches use of genetic algorithms seems s to be more efficient for predicting the rain as hence it is used in the current work. The evaluation results are performed based on evaluating various parameters and the proposed model seems to provide a better efficiency when compared to the rest of the previous traditional rainfall prediction systems.
Keywords: Rainfall Prediction, Genetic Algorithm, Crossover, Mutation, Accuracy, Evaluation, Fitness.
Scope of the Article: Regression and Prediction