Rainfall Prediction for Udaipur, Rajasthan Using Machine Learning Models Based on Temperature, Vapour Pressure and Relative Humidity
Jitendra Shreemali1, Praveen Galav2, Gaurav Kumawat3, Pankaj Chittora4
1Jitendra Shreemali, Professor, Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2Praveen Galav, Department of Science and Technology, Government of India, (New Delhi), India.
3Gaurav Kumawat, Assistant Professor, Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
4Pankaj Chittora, Assistant Professor, Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 24 February 2020 | Revised Manuscript received on 10 March 2020 | Manuscript Published on 18 March 2020 | PP: 133-137 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10240386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1024.0386S20
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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 study aims at Rainfall prediction using Machine Learning models using the minimum of features. The prediction here is based on temperature, vapour pressure and relative humidity. Numerous studies carried out earlier used more features than this study. A training-test split of 75-25 was used. The best results were obtained by combining the best of the candidate models into an ensemble model to identify that predictor importance of vapour pressure was 0.89 while that of relative humidity was 0.11 with temperature not seen as a significant predictor for rainfall though the high correlation of temperature (°C) with vapour pressure (Torr) and relative humidity (Percentage) suggests that the two predictor variables subsume the impact of temperature.
Keywords: Rainfall Prediction, Neural Network, Ensemble Model, CHAID, Random Forest.
Scope of the Article: Machine Learning