a surface water evaporation estimation model using bayesian belief networks with an application to the persian gulf

evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. in this paper, bayesian belief networks (bbns) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. as a case study, we estimated the surface water evaporation of the persian gulf and worked with a dataset of observations (daily evaporation) in a network of 13 weather stations  in the provinces of hormozgan and bushehr. two major categories of methods for learning bayesian networks are parameter learning and structure learning. in the first step, k2 search algorithm be used as a score-based method for structure learning of bbn. k2 algorithm connects weather stations to other and makes a virtual network of stations. in the second step, netica software be applied for parametric learning. obtained network by k2 algorithm with the help of a probabilistic inference method (reduce gradiant) in netica can predict the rate of evaporation. the results of the proposed method, indicating that this model has the more accuracy and reliability than existing statistical methods.