نویسنده
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسنده [English]
 Abstract  In this research the data relating to Global land/oceans temperature anomalies and annual mean precipitation of Jolfa station have been used for the 1960-2003 period. The main methodologies being used in this research are the Pearson correlation coefficient method, analysis of trend component of time series, simple linear and Artificial Neural Networks methods. The results of applying Pearson analysis indicate significant negative and inverse correlation between Global land/oceans temperature anomalies and annual precipitation in the Jolfa station. This is an indicative of the increase of precipitation and occurrence of wet years in during the negative Global temperature anomalies and on the contrary the precipitation reduction and occurrence of droughts in during the positive temperature anomalies. The analysis of long term trend components of time series showed that the annual mean precipitation of Jolfa has a decreasing trend towards the length of the period, but annual Global land/oceans temperature anomalies has a increasing trend towards the length of the period. Also we have simulated the relationships between annual precipitation in Jolfa station and Global Warming using Artificial Neural Networks. The applying different methods demonstrated that artificial neural network is recognized as a better and more accurate simulation model compared to the other models applied in this research, i.e., simple regression model. Different artificial neural network methods were used to demonstrate this relation, among which the Multi Layer Perceptron (MLP) with 4 hidden layers analysis with back propagation learning algorithm showed excellent capability in predicting the correlation between the series. Â
کلیدواژهها [English]
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