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Comparative Study of Machine Learning Algorithms for Rainfall Prediction

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Comparative Study of Machine Learning Algorithms for Rainfall Prediction


Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D

https://doi.org/10.31142/ijtsrd22961



Mylapalle Yeshwanth | Palla Ratna Sai Kumar | Dr. G. Mathivanan M.E., Ph.D "Comparative Study of Machine Learning Algorithms for Rainfall Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3, April 2019, pp.677-681, URL: https://www.ijtsrd.com/papers/ijtsrd22961.pdf

Majority of Indian framers depend on rainfall for agriculture. Thus, in an agricultural country like India, rainfall prediction becomes very important. Rainfall causes natural disasters like flood and drought, which are encountered by people across the globe every year. Rainfall prediction over drought regions has a great importance for countries like India whose economy is largely dependent on agriculture. A sufficient data length can play an important role in a proper estimation drought, leading to a better appraisal for drought risk reduction. Due to dynamic nature of atmosphere statistical techniques fail to provide good accuracy for rainfall prediction. So, we are going to use Machine Learning algorithms like Multiple Linear Regression, Random Forest Regressor and AdaBoost Regressor, where different models are going to be trained using training data set and tested using testing data set. The dataset which we have collected has the rainfall data from 1901-2015, where across the various drought affected states. Nonlinearity of rainfall data makes Machine Learning algorithms a better technique. Comparison of different approaches and algorithms will increase an accuracy rate of predicting rainfall over drought regions. We are going to use Python to code for algorithms. Intention of this project is to say, which algorithm can be used to predict rainfall, in order to increase the countries socioeconomic status.

rainfall prediction, machine learning, multiple linear regression, Random Forest Regressor, adaboost Regressor


IJTSRD22961
Volume-3 | Issue-3, April 2019
677-681
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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