Investigating the relationship between temperature and remote linkage patterns in the Iranian Caspian Sea basin Using Neural Network for Regional Planning
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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Temperature is one of the most important parameters of the climate that is effective in determining the role and distribution of other climatic elements. The purpose of this study was to investigate the relationship between monthly and annual temperature in Mazandaran watershed and distant link patterns. In this regard, average annual temperature data of 97 concurrent and meteorological stations within and outside the basin, as well as 33 distant link patterns during the statistical period (2014-2014), were used. First, the data were standardized in the standard software and standardized in the SPSS software. The grid was then designed based on the descending gradient algorithm (SCG), the hyperbolic animation function for the hidden layers and the same moving function for the output data. The entire network structure was considered to be 1 to 2 hidden layers and more than 600 networks were created. During this process, 70% of the data was for training and 30% for the test. Then, to evaluate the performance of the model, we used the statistical criteria including the sum of squares of errors and relative error. The results showed that the monthly data, temperature of a month ago and ONI and NINO3 patterns are more important and show more relevance with the monthly temperature of the study area. In annual data, the AMO, NCP, AO, NAO patterns and the one-year-olds' temperature were above 60%. Therefore, it can be stated that the mentioned patterns are more related to the annual temperature of the basin than other patterns.
Language:
Persian
Published:
Quarterly of Geography (Regional Planing), Volume:8 Issue: 3, 2018
Pages:
435 to 448
magiran.com/p1929138  
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