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ABSTRACT

The world today is witnessing the height of geopolitical competition; An arena where only energy resources are no longer considered as the support of countries, but the corridors have become the place of power competition and every country tries to find a place for itself in the network created by these emerging corridors. In other words, the global geopolitical trend intensifies competition over maritime bottlenecks and secures access to cross-border markets. This research is a type of "theoretical foundation" research; Its method is also descriptive-analytical and it is collected in a certain way from comparative methodology with library and internet tools. It seems that in the chronopolitical alcove in the field of Iran-Iraq relations, each of the actors is trying to position their role in the international corridors, which they pursue in line with several goals; Such as Iraq's pursuit of the "FAW Corridor" initiative, and Iran, which seeks to develop the circle of economic cooperation and take a place in the emerging alliances of the East. Although it is not possible to confidently predict the future of relations between the two countries from a chronopolitical perspective, signs of less cooperation and more competition in the relations between the two countries due to various variables and reasons can be seen on the horizon. The most important indicator of possible competition includes competition in transit routes (corridors).

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Drought is one of the most destructive natural disasters in human societies that cause irreparable impacts on agriculture, environment, society and economics. So, awareness of occurrence of droughts can be effective in reducing losses. In this study, in order to modeling and forecasting drought severity in a 37 year time period (1971-2007) in 21 meteorological stations, located in the cold semi-arid region of north-west Iran, artificial neural networks was used. The input data was annual rainfall data and annual drought precipitation index for all stations that 80% of the data (1971-2000) used for training the network and other 20% (2001-2007) used for testing it and in the next step drought severity predicted for the years 2008 to 2012 by the trained algorithm without using actual and existed data in this period. The appropriate structure for the network, based on Multi Layer Perceptron with three hidden layer, Back Propagation algorithm, Sigmoid transfer function and 10 neurons in middle layer. The results show that the artificial neural networks are well able to predict the non-linear relationship between rainfall and drought as it can simulate drought precipitation index values largely consistent with the real values with more than 97% regression and less than 5% error. So, drought can be predicted by this method in future and also it is useful in water resources management, drought management and climate change.