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Prediction of Rainfall Using Logistic Regression

Author(s): A.H.M. Rahmatullah Imon | Manos C. Roy | S. K. Bhattacharjee

Journal: Pakistan Journal of Statistics and Operation Research
ISSN 1816-2711

Volume: 8;
Issue: 3;
Date: 2012;
Original page

Keywords: Rainfall | Climatic Variables | Spurious Observations | Outliers | Logistic Regression | Generalized Standardized Pearson Residuals | Cross Validation | Cohen’s Kappa | Misclassification.

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.
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