Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin
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 mos...
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Kharazmi University
2020-12-01
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| Series: | تحقیقات کاربردی علوم جغرافیایی |
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| Online Access: | http://jgs.khu.ac.ir/article-1-2860-en.pdf |
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| author | iran salehvand amir gandomkar ebrahim fatahi |
| author_facet | iran salehvand amir gandomkar ebrahim fatahi |
| author_sort | iran salehvand |
| collection | DOAJ |
| description | 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%.
مترجم Google برای کسب و کار:کیت ابزار مترجممترجم وب سایت |
| format | Article |
| id | doaj-art-58e8a33db4e9414e864c554eb8900bd4 |
| institution | Kabale University |
| issn | 2228-7736 2588-5138 |
| language | fas |
| publishDate | 2020-12-01 |
| publisher | Kharazmi University |
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| series | تحقیقات کاربردی علوم جغرافیایی |
| spelling | doaj-art-58e8a33db4e9414e864c554eb8900bd42025-01-31T17:27:26ZfasKharazmi Universityتحقیقات کاربردی علوم جغرافیایی2228-77362588-51382020-12-0120598197Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basiniran salehvand0amir gandomkar1ebrahim fatahi2 Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran Department of Geography, Najaf Abad Unit, Islamic Azad University, Najafabad, Iran Meteorological Institute faculty member, Tehran, Iran 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%. مترجم Google برای کسب و کار:کیت ابزار مترجممترجم وب سایتhttp://jgs.khu.ac.ir/article-1-2860-en.pdffactor analysissynoptic systemsclimatic indicesclimatic elementsperceptron neural network |
| spellingShingle | iran salehvand amir gandomkar ebrahim fatahi Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin تحقیقات کاربردی علوم جغرافیایی factor analysis synoptic systems climatic indices climatic elements perceptron neural network |
| title | Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin |
| title_full | Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin |
| title_fullStr | Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin |
| title_full_unstemmed | Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin |
| title_short | Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin |
| title_sort | rainfall warning based on indexs teleconnection synoptic patterns of atmospheric upper levels and climatic elements a case study of karoun basin |
| topic | factor analysis synoptic systems climatic indices climatic elements perceptron neural network |
| url | http://jgs.khu.ac.ir/article-1-2860-en.pdf |
| work_keys_str_mv | AT iransalehvand rainfallwarningbasedonindexsteleconnectionsynopticpatternsofatmosphericupperlevelsandclimaticelementsacasestudyofkarounbasin AT amirgandomkar rainfallwarningbasedonindexsteleconnectionsynopticpatternsofatmosphericupperlevelsandclimaticelementsacasestudyofkarounbasin AT ebrahimfatahi rainfallwarningbasedonindexsteleconnectionsynopticpatternsofatmosphericupperlevelsandclimaticelementsacasestudyofkarounbasin |