Multi-station prediction of river stage-discharge using EEMD-WT-LSSVM
محل انتشار: یازدهمین سمینار بین المللی مهندسی رودخانه
سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 424
فایل این مقاله در 7 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
IREC11_074
تاریخ نمایه سازی: 27 تیر 1398
چکیده مقاله:
This study proposed a hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled and feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies
کلیدواژه ها:
River stage-discharge process ، LSSVM ، discrete Wavelet Transform (DWT) ، Ensemble Empirical Decomposition Mode (EEMD) ، Multi-station modeling
نویسندگان
Alireza Faregh Gharamaleki
M.Sc. East Azerbaijan regional water company, Tabriz, East Azerbaijan, Iran
Farhad Alizadeh
Ph.D. candidate East Azerbaijan regional water company
Ali Akhoundzadeh
M.Sc. East Azerbaijan regional water company, Tabriz, East Azerbaijan, Iran
Khadije Tabatabaee
M.Sc. East Azerbaijan regional water company, Tabriz, East Azerbaijan, Iran