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Rainfall prediction plays an important role in flood management and flood alert. With rainfall information, it is possible to predict the occurrence of floods in a given area and take the necessary measures. Due to the fact that the three months of January, February and March are most floods and most precipitation is occurring this quarter, this study aimed to investigate the factors affecting precipitation and modeling of this quarter. For precipitation modeling, the monthly rainfall data of the Hamadid and Baranzadeh station in the statistical period (1984-2014) for 30 years as a dependent variable and climatic indexes, large-scale climatic signals including sea surface temperatures and 1000 millimeter temperatures Altitude of 500 milligrams, 200 milligrams of omega and climatic elements have been used as independent variables. Due to the nonlinear behavior of rainfall, artificial neural networks were used for modeling. Factor analysis was used to determine the best architecture for entering the neural network. For prediction of precipitation, the data that showed the most relationship with precipitation was used in four patterns, in January the fourth pattern with entropy error was 045/0, the number of input layers was 91, the best makeup was 15-1, and the correlation coefficient was 94% Was. In February, the third pattern with a correlation coefficient of 97%, entropy error, was 0.36. Percentage, number of input units was 8 units, and the best type of latency layout was 10-1. The precipitation of March with all patterns was high predictive coefficient. The first pattern with entropy error was 0.038, the number of input units was 67, the hidden layer arrangement was 17-1, the correlation coefficient was 98%.

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The aim of this study was to identify the epicenter and co-occurrence factors of dust storm wave from 1 to 3 November 2017 in Kermanshah. To investigate the synoptic conditions of the causes of this phenomenon, from the European Central Center (ESMWF) mid-term weather forecast data set with a resolution of 0.125 degrees of arc including, geopotential height, omega, sea level pressure, orbital and meridional components, humidity. The Lagrangian method of HYSPLIT model was used to orient the source of dust particles. in this study, dust storm WRF-chem was simulated using a paired numerical weather forecasting model. Finally, through the processing of MODIS satellite images, its scope was determined. Examination of HYSPLIT tracking maps shows that two general paths for dust transfer to the area can be identified. 1- The northwest-southeast route, which passes through dust cores formed in the deserts of Iraq and Syria, transports dust to the western half of Iran. 2- Southwest to west of Iran and Kermanshah, which is the main source of dust on November 2 and 3, The source of the particles is Kuwait, northern Saudi Arabia and part of Iraq. The spatial distribution of the dust interpreted by the MODIS sensor images is consistent with the spatial distribution of the dust concentration simulated by the WRF-chem model.