LITERATURE REVIEW

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In paper 1 Ms.P.Shivaranjani  and Dr.K.Karthikeyan reviewed
the supervised and unsupervised machine learning algorithms and this can
be used to perform the weather prediction using different data mining
techniques and also can be used for prediction of rainfall daily ,monthly and
yearly with various parameter and thus it provides better result.

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In paper 2 Pushpa Mohan1 and Dr. Kiran Kumari Patil2
proposedthe suitable classification methods like Support Vector Machine
(SVM), neural networks are employed for better classification outcome.
These techniques will help in predicting the rainfall, crop yield forecasting
and cost prediction of crops.

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In paper 3 Amruta A.
Taksandeand  P. S. Mohod  used the FP Growth Algorithm  to generate decision trees and rules for
classifying weather parameters such as maximum temperature, minimum
temperature, rainfall, humidity and wind speed in terms of the month and year.

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In paper 6 Godfrey C. Onwubolu1, Petr
Buryan , SitaramGarimella , Visagaperuman Ramachandran ,Viti Buadromo and Ajith
Abraham presents the data mining activity that was employed to mining weather
data using various self organizing data mining techniques. The weather data
used for the DM research include daily temperature, daily pressure and monthly
rainfall.

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In paper 7  Basvanth
Reddy and  Prof. B.A Patilproposed
weather prediction using big data environment.The method used by them is
hadoop with map reduces to analyse the sensor data, which is stored in the
National Climatic Data Centre (NCDC) is a efficient solution.

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In paper 8 Prashant
Biradar,SarfrazAnsari,YashavantParadkar and 
SavitaLohiyahave proposed the use of K-medoids and Naive Bayes algorithm for
weather forecasting system with parameters such as temperature, humidity, and
wind.This system can be used in Air Traffic, Marine, Agriculture, Forestry,
Military, and Navy etc

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In paper 9FolorunshoOlaiya,Adesesan
Barnabas Adeyemo proposed decision tree classification algorithm was
used to generate decision trees and rules for classifying weather parameters
such as maximum temperature, minimum temperature, rainfall, evaporation etc.

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In paper 10AishwaryaDhore,
AnaghaByakude, Bhagyashri Sonar, Mansi Waste presents a numerical weather
prediction model in local weather. these models are  complex and will take significant time  to accomplish.