Author(s)

Mina Mahbub Hossain, Sayedul Anam

Department of Agricultural Statistics, Faculty of Agribusiness Management, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh.
Department of Business Administration, Faculty of Business and Economics, Daffodil International University, Dhaka, Bangladesh.

Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall for different regions to model the daily occurrence of rainfall and achieved satisfactory results around the world. In connection to the Markov chain of different order, logistic regression is conducted to visualize the dependence of current rainfall upon the rainfall of previous two-time period. It had been shown that wet day of the previous two time period compared to the dry day of previous two time period influences positively the wet day of current time period, that is the dependency of dry-wet spell for the occurrence of rain in the rainy season from April to September in the study area. Daily data are collected from meteorological department of about 26 years on rainfall of Dhaka station during the period January 1985-August 2011 to conduct the study. The test result shows that the occurrence of rainfall follows a second order Markov chain and logistic regression also tells that dry followed by dry and wet followed by wet is more likely for the rainfall of Dhaka station and also the model could perform adequately for many applications of rainfall data satisfactorily.

Characteristics of Rainfall in Bangladesh; Stochastic Models; Markov Chain Mode; Logistic Regression Model; Akaike’s Information Criterion (AIC)

Hossain, M. and Anam, S. (2012) Identifying the dependency pattern of daily rainfall of Dhaka station in Bangladesh using Markov chain and logistic regression model. Agricultural Sciences, 3, 385-391. doi: 10.4236/as.2012.33045.