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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Volume 8 | Issue 1

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Paper Title: Rainfall Prediction Using Machine Learning
Authors Name: Angela Bothaju , Maunika Patnaik , K. Keerthana , K. Uday Deep , Pardha Saradhi
Unique Id: IJSDR2006036
Published In: Volume 5 Issue 6, June-2020
Abstract: There are certain catastrophic events which could have drastic complications that may lead to destruction of anything in their path; Rainfall is one such event that could be severe, both in its overabundance and its acute shortage. Hence accurate rainfall prediction is a core requirement in terms of economic as well as in administrative efforts. This importance has made us to focus on adding accuracy to rainfall prediction using the machine learning techniques with traditional unsupervised learning technique. We have considered a rainfall dataset from meteorological department in order implement our prediction model .This process is carried out using two important machine learning techniques which initially involves clustering (K-Medoids) to be performed and later the clustered data is given as input to the Classifier algorithm (Naïve-Bayes Classifier) in order to make appropriate prediction. We had implemented this model of rainfall prediction to make accurate and efficient prediction because using any one kind of algorithm does not produce accurate results. Our main idea of using clustering coupled with simple predictors is to prove that this beats more complex methods such as Support Vector Machines and Random Forests in terms of accuracy, speed and execution time. In order to support our intuition, we have implemented Naïve Bayes Classifier algorithm alone with no optimization applied which practically yielded predictions that are less accurate.
Keywords: K-Medoid, Naïve Bayes, Clustering, Classification, Machine Learning.
Cite Article: "Rainfall Prediction Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.215 - 222, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006036.pdf
Downloads: 000201506
Publication Details: Published Paper ID: IJSDR2006036
Registration ID:191912
Published In: Volume 5 Issue 6, June-2020
DOI (Digital Object Identifier):
Page No: 215 - 222
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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