Record Details

Prediction of Rainfall Using Logistic Regression

Pakistan Journal of Statistics and Operation Research

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Field Value
 
Title Prediction of Rainfall Using Logistic Regression
 
Creator Imon, A.H.M. Rahmatullah; Department of Mathematical Sciences
Ball State University, Muncie, IN 47306
USA
Roy, Manos C.; Department of Statistics
University of Rajshahi, Rajshahi-6205
Bangladesh
Bhattacharjee, S. K.; Department of Statistics
University of Rajshahi, Rajshahi-6205
Bangladesh
 
Subject Statistics
Rainfall,Climatic Variables, Spurious Observations, Outliers, Logistic Regression, Generalized Standardized Pearson Residuals, Cross Validation, Cohen’s Kappa, Misclassification.

 
Description The use of logistic regression modeling has exploded during the past decade for prediction and forecasting. From its original acceptance in epidemiologic research, the method is now commonly employed in almost all branches of knowledge. Rainfall is one of the most important phenomena of climate system. It is well known that the variability and intensity of rainfall act on natural, agricultural, human and even total biological system. So it is essential to be able to predict rainfall by finding out the appropriate predictors. In this paper an attempt has been made to use logistic regression for predicting rainfall. It is evident that the climatic data are often subjected to gross recording errors though this problem often goes unnoticed to the analysts. In this paper we have used very recent screening methods to check and correct the climatic data that we use in our study. We have used fourteen years’ daily rainfall data to formulate our model. Then we use two years’ observed daily rainfall data treating them as future data for the cross validation of our model. Our findings clearly show that if we are able to choose appropriate predictors for rainfall, logistic regression model can predict the rainfall very efficiently.
 
Publisher College of Statistical and Actuarial Sciences
 
Contributor
 
Date 2012-07-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.pjsor.com/index.php/pjsor/article/view/535
10.18187/pjsor.v8i3.535
 
Source Pakistan Journal of Statistics and Operation Research; Vol 8. No. 3, 2012; 655-667
2220-5810
1816-2711
 
Language eng
 
Relation http://www.pjsor.com/index.php/pjsor/article/view/535/267