1
Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran
2
m
Abstract
Review: Precipitation is one of the most important and most important climatic elements that plays a significant role in determining the role and distribution of other climatic elements. In order to model the Khorramabad rainfall, the monthly rainfall data of Khorramabad synoptic station in the statistical period (1951-2014) for 64 years was used as a dependent variable and climatic and climatic indices as independent variables. Factor analysis was used to use the most important climatic elements affecting the studied area. Stepwise regression analysis methods were used to identify the most important climatic index affecting the dependent variable. The results of the research revealed that after the network test with hidden layers, with different learning and multiplicity of experiments and errors, the climatic indices of the six models with the correlation coefficient were 63%, 74%, 76%, 88%, 86% Rainfall modeling can be done and with climatic elements, the first factor affecting the climate of the region, which accounts for more than 50% of the data, with a correlation coefficient of 90% and with factors of rainfall of 99% and temperature factors with a negative charge with correlation coefficient 98.8%, the second factor influencing the climate of the operating area (wind) was 76%, the third factor (temperature) 91%, paid modeling.
Gandomkar,A. , salehvand,I. and Fatahi,E. (2019). Monthly precipitation Modeling using climate
Monthly precipitation Forecast using climate indices using Perceptron artificial neural network and regression Case study: city of Khorramaba. Journal of Meteorology and Atmospheric Science, 2(1), 1-14.
MLA
Gandomkar,A. , , salehvand,I. , and Fatahi,E. . "Monthly precipitation Modeling using climate
Monthly precipitation Forecast using climate indices using Perceptron artificial neural network and regression Case study: city of Khorramaba", Journal of Meteorology and Atmospheric Science, 2, 1, 2019, 1-14.
HARVARD
Gandomkar A., salehvand I., Fatahi E. (2019). 'Monthly precipitation Modeling using climate
Monthly precipitation Forecast using climate indices using Perceptron artificial neural network and regression Case study: city of Khorramaba', Journal of Meteorology and Atmospheric Science, 2(1), pp. 1-14.
CHICAGO
A. Gandomkar, I. salehvand and E. Fatahi, "Monthly precipitation Modeling using climate
Monthly precipitation Forecast using climate indices using Perceptron artificial neural network and regression Case study: city of Khorramaba," Journal of Meteorology and Atmospheric Science, 2 1 (2019): 1-14,
VANCOUVER
Gandomkar A., salehvand I., Fatahi E. Monthly precipitation Modeling using climate
Monthly precipitation Forecast using climate indices using Perceptron artificial neural network and regression Case study: city of Khorramaba. Journal of Meteorology and Atmospheric Science, 2019; 2(1): 1-14.
