Rainfall Prediction Using Intelligent Retrieval and Data Analytics
S. Dhamodaran1, J. Refonaa2, R. Ranjith Kumar3, G. Pavan Kumar4
1S. Dhamodaran, Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
2J. Refonaa, Assistant Professor Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
3R. Ranjith Kumar, Student, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
4G. Pavan Kumar Student, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
Manuscript received on 05 May 2019 | Revised Manuscript received on 12 May 2019 | Manuscript published on 30 May 2019 | PP: 759-761 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5635058719/19©BEIESP
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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: Many new approaches are being researched on to predict the rain beforehand in order 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. Machine learning is gaining a ital growth in almost all the technologies and prediction of weather is not an exception. In this paper we have proposed a system that could predict the rain before hand using machine learning techniques. We have used machine learning model that uses a unique algorithm for constructing the rain patterns. This patterns are then used to get the meteoric information on the web and then the datasets are retrieved for the Pinglin lookout of Central Meteoric Administration of Taiwan’s Ministry of Communications. Initially the algorithm works on gathering some of the information like rain, humidness and temperature of a particular areas and then making use of random forest for predicting the rain. Apache Spark is used for evaluating the results of the designed model]. When compared to other approaches classification of data seems to be an efficient way of 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.
Keyword: Big data, Apache Spark, Random Forest, Decision Tree, Rainfall, Prediction, Machine Learning Techniques
Scope of the Article: Machine Learning