Heavy Rainfall Prediction using Gini Index in Decision Tree 
Ayisha Siddiqua L1, Senthil kumar N C2
1AyishaSiddiqua L, M.Tech Software Engineering, Vellore Institute of Technology, Vellore, India.
2Senthikumar N C, Assistant Professor(SG), Vellore Institute of Technology, Vellore, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4558-4562 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8503118419/2019©BEIESP | DOI: 10.35940/ijrte.D8503.118419
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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: In existing systems, it happens that sometimes the data is not accurate and proper data mining techniques not being used and this increases the complexity. We as humans are bound to make mistakes while predicting weather conditions which might result in damage to both life and property. To avoid this, we use data mining algorithms for early warning of climatic conditions such as like maximum temperature, minimum temperature wind speed, rainfall, humidity, pressure, dew point, cloud, sunshine and wind direction from data to predict rainfall. But by using proper algorithms for datasets and using the right metrics, we can achieve the accurate results in prediction of rainfall. Hence, we apply the Decision tree algorithm using Gini Index in order to predict the precipitation with accuracy and it is completely based on the historical data.
Keywords: Rainfall, Prediction, Decision Tree, Gini Index
Scope of the Article: Software Engineering Decision Support.