بررسي رابطه تغييرات بارش ساليانه تبريز با ناهنجاريهاي دمايي كره زمين و شبيه سازي عددي آن با استفاده از شبكه عصبي مصنوعي

An Investigation on the Relationship between the Precipitation Variations in Tabriz with the Global Temperature Anomalies and its Numerical Simulation Using the Artificial Neural Network

In this research, the data relating to global land/oceans temperature anomalies and annual mean precipitation of Tabriz station were used for the period of 1951-2005. The main methodologies used in this research include the Pearson correlation coefficient method, analysis of trend component of time series, simple linear and polynomial regression (as a semi-linear model) and Artificial Neural Networks methods. The results of applying Pearson analysis indicated a significant negative and an inverse correlation between global land/oceans temperature anomalies and annual precipitation in Tabriz station. This is an indicative of increase in precipitation and occurrence of wet years during the negative global temperature anomalies and, on contrary, precipitation reduction and occurrence of droughts during the positive global temperature anomalies. The analysis of long term trend components of time series showed that the annual mean precipitation of Tabriz has a decreasing trend towards the length of the period, but annual global land/oceans temperature anomalies has an increasing trend towards the length of the period. Also we simulated the relationships between annual precipitation in Tabriz station and global warming using Artificial Neural Networks. Applying of different methods recognized artificial neural network as a better and more accurate simulation model compared to the other models applied in this research, i.e. simple regression model, and semi- linear polynomial regression with the power of 6 models. Different artificial neural network methods were used to demonstrate this relation, among which the Multi Layer Perceptron (MLP) with three hidden layers analysis with back propagation learning algorithm showed excellent capability in predicting the correlation between the series.