Monthly Rainfall Forecasting Using Bayesian Belief Networks
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2012, Vol 3, Issue 11
Abstract
Bayesian Belief Networks (BBNs) provide an effective graphical model for factoring joint probability distributions under uncertainty. In this paper we introduce application of BBNs in weather forecasting. We work with a database of observations (monthly rainfall) in a network of 20 stations in Khorasan provinces (Iran), measured for the years 1985-2011 on a grid of approximately 600km resolution. Firstly, we analyze the efficiency of Tabu search algorithm to structural learning of BBN. In this step, a directed acyclic graph shows dependencies among stations in the area of study; we also use Netica software for parametric learning of BBNs. The comparisons show usefulness of proposed method as a probabilistic rainfall forecasting model.
Authors and Affiliations
Alireza Sadeghi Hesar| Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran,Alireza.sadeghi89@yahoo.com, Hamid Tabatabaee| Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran, Mehrdad Jalali| Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Keywords
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