Hybrid Approach of Prediction Daily Maximum and Minimum Air Temperature for Baghdad City by Used Artificial Neural Network and Simulated Annealing

Abstract

     Temperature predicting is the utilization to forecast the condition of the temperature for an upcoming date for a given area. Temperature predictions are done by gathering quantitative data in regard to the current state of the atmosphere. In this study, a proposed hybrid method to predication the daily maximum and minimum air temperature of Baghdad city which combines standard backpropagation with simulated annealing (SA). Simulated Annealing Algorithm are used for weights optimization for recurrent multi-layer neural network system. Experimental tests had been implemented using the data of maximum and minimum air temperature for month of July of Baghdad city that got from local records of Iraqi Meteorological Organization and Seismology (IMOS) in period between 2010 to 2016. The results show that the proposed hybrid method got a high accuracy prediction results that reach nearly from real temperature records of desired year.