Volume 20, Issue 59 (12-2020)
jgs 2020, 20(59): 81-97 |
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salehvand I, gandomkar A, fatahi E. (2020). Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin. jgs. 20(59), 81-97. doi:10.29252/jgs.20.59.81
URL: http://jgs.khu.ac.ir/article-1-2860-en.html
URL: http://jgs.khu.ac.ir/article-1-2860-en.html
1- Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran, .
2- Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran, . ,agandomkar2007@yahoo.com
3- Meteorological Institute faculty member, Tehran, Iran, .
2- Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran, . ,
3- Meteorological Institute faculty member, Tehran, Iran, .
Abstract: (4991 Views)
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%.
Keywords: Factor analysis, synoptic systems, climatic indices, climatic elements, perceptron neural network
Type of Study: Research |
Subject:
climatology
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This work is licensed under a Creative Commons — Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)