Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)
Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/279368 |
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| _version_ | 1832553456349478912 |
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| author | Abazar Solgi Vahid Nourani Amir Pourhaghi |
| author_facet | Abazar Solgi Vahid Nourani Amir Pourhaghi |
| author_sort | Abazar Solgi |
| collection | DOAJ |
| description | Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations. |
| format | Article |
| id | doaj-art-3cb6a388c7a44d0ca1f0fd8ed4dc59a4 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-3cb6a388c7a44d0ca1f0fd8ed4dc59a42025-02-03T05:53:58ZengWileyAdvances in Civil Engineering1687-80861687-80942014-01-01201410.1155/2014/279368279368Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)Abazar Solgi0Vahid Nourani1Amir Pourhaghi2Water Resources Engineering, Faculty of Water Science, Shahid Chamran University of Ahvaz, Ahvaz 65981-34199, IranWater Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranWater Resources Engineering, Faculty of Water Science, Shahid Chamran University of Ahvaz, Ahvaz 65981-34199, IranDoubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations.http://dx.doi.org/10.1155/2014/279368 |
| spellingShingle | Abazar Solgi Vahid Nourani Amir Pourhaghi Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) Advances in Civil Engineering |
| title | Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) |
| title_full | Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) |
| title_fullStr | Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) |
| title_full_unstemmed | Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) |
| title_short | Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand) |
| title_sort | forecasting daily precipitation using hybrid model of wavelet artificial neural network and comparison with adaptive neurofuzzy inference system case study verayneh station nahavand |
| url | http://dx.doi.org/10.1155/2014/279368 |
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