دورية أكاديمية
Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition
| العنوان: | Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition |
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| المؤلفون: | Dong-mei Xu, Xiang Wang, Wen-chuan Wang, Kwok-wing Chau, Hong-fei Zang |
| المصدر: | Journal of Hydroinformatics, Vol 25, Iss 3, Pp 943-970 (2023) |
| بيانات النشر: | IWA Publishing, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Information technology LCC:Environmental technology. Sanitary engineering |
| مصطلحات موضوعية: | improved complete ensemble emd (iceemdan), monthly runoff prediction, quadratic decomposition, seagull optimization algorithm (soa), support vector machine (svm), wavelet decomposition (wd), Information technology, T58.5-58.64, Environmental technology. Sanitary engineering, TD1-1066 |
| الوصف: | In runoff prediction, the prediction accuracy is often affected by the non-linear and non-stationary characteristics of the runoff series. In this study, a coupled forecasting model is proposed that decomposes the original runoff series by an improved complete ensemble Empirical Mode Decomposition (EMD) (ICEEMDAN) combined with a wavelet decomposition (WD) and then forecasts the monthly runoff using a support vector machine (SVM) optimized by the seagull optimization algorithm (SOA). In this method, a series of Intrinsic Mode Function (IMF) and a Residual (Res) are obtained by decomposing the original runoff series with ICEEMDAN. The WD method is used to perform quadratic decomposition of high-frequency components decomposed by the ICEEMDAN method to make the runoff series as smooth as possible. Then the decomposed components are input into the SOA-SVM model for prediction. Finally, the prediction results of each component are superimposed and reconstructed to obtain the final monthly runoff prediction results. RMSE, Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Efficiency Coefficient (NSEC), and R are selected to evaluate the prediction results and the model is compared with SOA-SVM model, EMD-SOA-SVM model and CEEMDAN-SOA-SVM model other models. The proposed model is applied to the monthly runoff forecast of the Hongjiadu and Manwan Reservoirs. When compared with other benchmarking models, the ICEEMDAN-WD-SOA-SVM model attains the smallest Root Mean Square Error (RMSE) and MAPE and the largest NSEC and R. The ICEEMDAN-WD-SOA-SVM model has the best prediction effect, the highest prediction accuracy, and the lowest prediction error. HIGHLIGHTS The ICEEMDAN–WD model is used to decompose the original runoff series.; The proposed ICEEMDAN–WD model can effectively reduce the complexity of the runoff series.; The proposed SOA–SVM model can effectively improve the prediction accuracy of runoff series.; The proposed model can provide high prediction accuracy and consistency.; |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1464-7141 1465-1734 |
| Relation: | http://jhydro.iwaponline.com/content/25/3/943; https://doaj.org/toc/1464-7141; https://doaj.org/toc/1465-1734 |
| DOI: | 10.2166/hydro.2023.172 |
| URL الوصول: | https://doaj.org/article/c306ab37252045d0be29a0389f1ff01a |
| رقم الأكسشن: | edsdoj.306ab37252045d0be29a0389f1ff01a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14647141 14651734 |
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| DOI: | 10.2166/hydro.2023.172 |