Forecasting of rainfall using different input selection methods on climate signals for neural network inputs
Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this...
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| Format: | Article |
| Language: | English |
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Shahid Chamran University of Ahvaz
2019-07-01
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| Series: | Journal of Hydraulic Structures |
| Subjects: | |
| Online Access: | https://jhs.scu.ac.ir/article_14573_301125da2b8668ac16a45f19c8754283.pdf |
| _version_ | 1797465904995368960 |
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| author | Alireza Dariane Mohammadreza Ashrafi Gol Farzaneh Karami |
| author_facet | Alireza Dariane Mohammadreza Ashrafi Gol Farzaneh Karami |
| author_sort | Alireza Dariane |
| collection | DOAJ |
| description | Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and temperature) with 3 to 12 months lead-times are used as inputs to predict precipitation for 3, 6, 9 and 12 months periods in 6 selected stations across Iran. A genetic algorithm (GA) and self-organized neural network (SOM) along with the application of winGamma software were comparatively used as input selection methods to choose the appropriate input variables. Examining the results, out of 96 predictions performed at all stations, in 43 cases, GA, in 28 cases, winGamma, and in 25 cases SOM have the best results compared to the other two methods. According to this, as a generalized assumption, it can be said that at least for the selected stations in this paper, the GA method is more reliable than the other two methods, and can be used to make predictions for future applications as a reliable input selection method. Moreover, among different climatic signals, Pacific Decadal Oscillation (PDO), Trans-Niño Index (TNI) and Eastern Tropical Pacific SST (NINO3) are the most repetitive indices for the most accurate forecast of each station. |
| first_indexed | 2024-03-09T18:28:08Z |
| format | Article |
| id | doaj.art-34010ad663dc4743aa2f8b3ef359cee5 |
| institution | Directory Open Access Journal |
| issn | 2345-413X 2345-4156 |
| language | English |
| last_indexed | 2024-03-09T18:28:08Z |
| publishDate | 2019-07-01 |
| publisher | Shahid Chamran University of Ahvaz |
| record_format | Article |
| series | Journal of Hydraulic Structures |
| spelling | doaj.art-34010ad663dc4743aa2f8b3ef359cee52023-11-24T07:42:56ZengShahid Chamran University of AhvazJournal of Hydraulic Structures2345-413X2345-41562019-07-0151425910.22055/jhs.2019.29625.111314573Forecasting of rainfall using different input selection methods on climate signals for neural network inputsAlireza Dariane0Mohammadreza Ashrafi Gol1Farzaneh Karami2Water Resources Division, Department of Civil Engineering, KN Toosi University of TechnologyDepartment of Civil Engineering, KN University of Technology, Tehran, IranDepartment of Civil Engineering, KN University of Technology, Tehran, IranLong-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and temperature) with 3 to 12 months lead-times are used as inputs to predict precipitation for 3, 6, 9 and 12 months periods in 6 selected stations across Iran. A genetic algorithm (GA) and self-organized neural network (SOM) along with the application of winGamma software were comparatively used as input selection methods to choose the appropriate input variables. Examining the results, out of 96 predictions performed at all stations, in 43 cases, GA, in 28 cases, winGamma, and in 25 cases SOM have the best results compared to the other two methods. According to this, as a generalized assumption, it can be said that at least for the selected stations in this paper, the GA method is more reliable than the other two methods, and can be used to make predictions for future applications as a reliable input selection method. Moreover, among different climatic signals, Pacific Decadal Oscillation (PDO), Trans-Niño Index (TNI) and Eastern Tropical Pacific SST (NINO3) are the most repetitive indices for the most accurate forecast of each station.https://jhs.scu.ac.ir/article_14573_301125da2b8668ac16a45f19c8754283.pdfprecipitation predictionlarge scale climatic signalsself-organized artificial neural networksinput selection methodgenetic algorithm |
| spellingShingle | Alireza Dariane Mohammadreza Ashrafi Gol Farzaneh Karami Forecasting of rainfall using different input selection methods on climate signals for neural network inputs Journal of Hydraulic Structures precipitation prediction large scale climatic signals self-organized artificial neural networks input selection method genetic algorithm |
| title | Forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| title_full | Forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| title_fullStr | Forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| title_full_unstemmed | Forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| title_short | Forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| title_sort | forecasting of rainfall using different input selection methods on climate signals for neural network inputs |
| topic | precipitation prediction large scale climatic signals self-organized artificial neural networks input selection method genetic algorithm |
| url | https://jhs.scu.ac.ir/article_14573_301125da2b8668ac16a45f19c8754283.pdf |
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