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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: International Journal of Advanced Biological and Biomedical Research
- نوع مقاله: Journal Article
- کلمات کلیدی: Hybrid model,Wavelet - Artificial Neural Network,Wavelet - Adaptive Neural Fuzzy Inference System,Gamasyab River,Monthly flow prediction
- چکیده:
- چکیده انگلیسی: Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran). For this purpose, original time series using wavelet theory decomposed to multi time sub-signals, then these decomposed sub-signals as in input data are used in Adaptive Neural Fuzzy Interference System (ANFIS) and Artificial Neural Network (ANN) for monthly flow prediction. The obtained result shows that WANFIS model has better performance than WANN and can be used for short term and long term flow prediction. One of the weaknesses of fuzzy models is the model estimation error in minimum and maximum points. Which this problem can solve by using hybrid models of wavelet - fuzzy inference system.Also based on results of hybrid model of wavelet- network, it can be concluded that to achieve accurate estimation of the number of different intermediate layers are examined and using one intermediate layer in all conditions is not enough to achieve the best results. Generally, hybrid model of wavelet - Adaptive Neural Fuzzy Interference System have better performance in estimation of the extent points and it is better method for prediction of Gamasyab River flow.
- انتشار مقاله: 09-05-1393
- نویسندگان: Abazar Solgi,Feridon Radmanesh,Heidar Zarei,Vahid Nourani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advance Researches in Civil Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Stream flow,Denoising,Artificial Neural Network,Least Square Support Vector Machine,Multi-Station,Snoqualmie watershed
- چکیده:
- چکیده انگلیسی: In this study, the ability of threshold based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow of the Snoqualmie watershed. For this aim, at first step, outflow of the watershed was forecasted via ad hoc LSSVM and ANN models just by one station individually. Therefore, MS-LSSVM and MS-ANN were employed to use entire information of all sub-basins synchronously. Finally, the streamflow of sub-basins were denoised via wavelet based thresholding method, then the purified signals were imposed into the LSSVM and ANN models in a MS framework. The results showed the superiority of ANN to the LSSVM, MS model to the individual sub-basin model, using denoised data with regard to the noisy data, e.g., DCLSSVM=0.82, DCANN=0.85, DCMS-ANN=0.91, DCdenoised-MS-ANN=0.94.
- انتشار مقاله: 29-05-1397
- نویسندگان: Gholamreza Andalib,Vahid Nourani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advance Researches in Civil Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Stream flow,Denoising,Artificial Neural Network,Least Square Support Vector Machine,Multi-Station,Snoqualmie watershed
- چکیده:
- چکیده انگلیسی: In this study, the ability of threshold based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow of the Snoqualmie watershed. For this aim, at first step, outflow of the watershed was forecasted via ad hoc LSSVM and ANN models just by one station individually. Therefore, MS-LSSVM and MS-ANN were employed to use entire information of all sub-basins synchronously. Finally, the streamflow of sub-basins were denoised via wavelet based thresholding method, then the purified signals were imposed into the LSSVM and ANN models in a MS framework. The results showed the superiority of ANN to the LSSVM, MS model to the individual sub-basin model, using denoised data with regard to the noisy data, e.g., DCLSSVM=0.82, DCANN=0.85, DCMS-ANN=0.91, DCdenoised-MS-ANN=0.94.
- انتشار مقاله: 29-05-1397
- نویسندگان: Gholamreza Andalib,Vahid Nourani
- مشاهده