Study performance of the hybrid Firefly algorithm and artificial neural network in simulation of the daily evaporation values

Simulation of evaporation process is a very important affair in the planning and management of water resources and due to the complexity of the process of evaporation, determine its exact parameters require the use of detailed procedures that reasonably accurately grasping the process simulation. Using a hybrid method for simulation and optimization of the process of evaporation can be fitted with a high-precision simulation. In this study, we used the data on the daily synoptic station rudsar evaporation of the year 1391 to 1394 for four years, the performance of the hybrid Firefly algorithm to estimate daily evaporation in comparison with artificial neural network was evaluated. Due to the importance of climatic parameters and correlation with the amount of evaporation, input models in the form of 6 different measurement scenario. in both models have been applied, the superior was a model scenario was that the entry contains the maximum air temperature, minimum temperature, air relative humidity, wind speed, the Sundial. During the simulated evaporation of the structure by means of artificial neural network has a root equal squares of error 1.2 mm in days and corrolation 0.67, the average absolute error has been 0.83 as well as solidarity. And also the same results relating to the hybrid Firefly algorithm in this scenario against the root of the squares of the error 0.88 mm in days, the average absolute error 0.62 and also fitted 0.93 correlation.