Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling
This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS) and artificial intelligence. To enhance the accu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/472523 |
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| _version_ | 1832558846734761984 |
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| author | Chien-Lin Huang Nien-Sheng Hsu Chih-Chiang Wei Chun-Wen Lo |
| author_facet | Chien-Lin Huang Nien-Sheng Hsu Chih-Chiang Wei Chun-Wen Lo |
| author_sort | Chien-Lin Huang |
| collection | DOAJ |
| description | This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS) and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons. |
| format | Article |
| id | doaj-art-2b6228e7f92c4059a57336425596fac9 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-2b6228e7f92c4059a57336425596fac92025-02-03T01:31:25ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/472523472523Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast ModelingChien-Lin Huang0Nien-Sheng Hsu1Chih-Chiang Wei2Chun-Wen Lo3Department of Civil Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, TaiwanDepartment of Civil Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, TaiwanDepartment of Digital Content Designs and Management, Toko University, No. 51, Section 2, University Road, Pu-Tzu City, Chiayi County 61363, TaiwanDepartment of Civil Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, TaiwanThis study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS) and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.http://dx.doi.org/10.1155/2015/472523 |
| spellingShingle | Chien-Lin Huang Nien-Sheng Hsu Chih-Chiang Wei Chun-Wen Lo Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling Advances in Meteorology |
| title | Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling |
| title_full | Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling |
| title_fullStr | Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling |
| title_full_unstemmed | Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling |
| title_short | Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling |
| title_sort | using artificial intelligence to retrieve the optimal parameters and structures of adaptive network based fuzzy inference system for typhoon precipitation forecast modeling |
| url | http://dx.doi.org/10.1155/2015/472523 |
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