Forecasting Maximum Seasonal Temperature Using Artificial Neural Networks 'Tehran Case Study'

摘要

The main purpose of this research is maximum temperature prediction using neural network techniques. For this purpose, 70% of the data were allocated for network training and 30% of the data were devoted for testing and validation. The most appropriate neural network structure for predicting Tehran maximum winter temperature is a model with three neurons in the input layer, and a hidden layer with 9 neurons and the use of a hyperbolic tangent function in the hidden layer, that is, 3-9-1 arrangement in which the root mean of square error, correlation coefficient and the mean of absolute error for the training phase and the testing phase are respectively 0.001, 0.997, 0.61 and 0.104, 0.997, 0.311. The determination coefficient and correlation coefficients for both training and testing periods equal 0.99 and 0.99 and the correlation coefficient is significant at the level of 1%.