Exploring the Probability of Drought and selecting the most Appropriate Indicator for Climatic Regions of Iran

 Iran is a vast country with a different climate due to its geographical location and topographical conditions. The current research was carried out to select the most appropriate drought index in Iran's climatic regions and investigate the probability of its occurrence through uncertainty methods. 

 In this research, in the first step, through multi-criteria decision-making methods, the most suitable index for each climate zone is selected based on the percentage of suitability, and finally, based on artificial neural network methods, probability analysis is calculated and the probability of the phenomenon occurring. In this research, after choosing the appropriate index, the statistical data of the country's synoptic station in a statistical period of 28 years (1990-2017) has been used to express the probability of drought, and the Kernel method has been used to converge the data.

 The final result of the cloud theory analysis of the studied data shows that in all the stations examined in the target year, i.e., 2017, the country of Iran and all the representative stations have shown climatic conditions close to the normal range. The highest likelihood of occurrence belongs to Tabriz station (96%) and Hamedan station (94%).

 Based on the results, the selective uncertainty model has a high ability in probability analysis and has predicted the probability of drought with an acceptable percentage of confidence.

Innovation: 

Due to the difference in climatic regions of Iran, the elimination of user intervention, and the use of scientific and mathematical calculations, the error rate in selecting the index is reduced. Then, with the help of uncertainty methods such as cloud theory, the ability to predict the probability of drought in the future increases.