Exploiting Ensemble Learning for Rainfall Prediction using Meta Regressors and Meta Classifiers
Kovvuri N Bhargavi1, G.Jaya Suma2
1Kovvuri N Bhargavi*, Sr,Asst Prof, Department of CSE, Aditya College of Engg& Tech, Surampalem, India.
2Dr.G.Jaya Suma, Prof&HOD, Department of IT, UCEV JNTUK Vizianagaram, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2379-2384 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5806029320/2020©BEIESP | DOI: 10.35940/ijeat.C5806.029320
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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: Intense rainfall produces flooding even on dry soil. As heavy rainfall is one of the causes for flooding it is necessary to predict the Rainfall to take necessary precautions for people who are living in risk zone areas. Prediction of Rainfall tomorrow is done accurately using Machine Learning regression and classification Techniques. For Rainfall prediction multiple attributes like Windspeed, Precipitation, Cloudcover, Humidity, Temperature and RainfallToday are considered to predict Rainfall Tomorrow. An ensemble approach is used where predictions from Regression models such as Linear Regression, Polynomial Regression, Ridge Regression and Lasso regression are stacked together and fed as new attributes to Meta Regressor along with Support Vector Regression for making final predictions. Also, predictions from classifications models such as Gaussian Naive Bayes, K-nearest neighbor, Support vector Machine and Random Forest are stacked together and fed as new attributes to Meta Classifier along with Logistic regression which is a binary classifier for higher predictive performance.
Keywords: Meta Regressor ,Meta Classifier, Support Vector Regressor, Random Forest, Ridge Regression, Lasso Regression.