A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods
Joint Authors
Lin, Gwo-Fong
Wang, Tsung-Chun
Chen, Lu-Hsien
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-27
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance.
To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model.
Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2.
The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts.
For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE) due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively.
Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively.
Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively.
These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model.
In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process.
The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression) model because of its accuracy and robustness.
American Psychological Association (APA)
Lin, Gwo-Fong& Wang, Tsung-Chun& Chen, Lu-Hsien. 2015. A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods. Advances in Meteorology،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1095634
Modern Language Association (MLA)
Lin, Gwo-Fong…[et al.]. A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods. Advances in Meteorology No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1095634
American Medical Association (AMA)
Lin, Gwo-Fong& Wang, Tsung-Chun& Chen, Lu-Hsien. A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods. Advances in Meteorology. 2015. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1095634
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1095634