the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Ice breakup forecast in the reach of the Yellow River: the support vector machines approach
Abstract. Accurate lead-time forecast of ice breakup is one of the key aspects for ice flood prevention and reducing losses. In this paper, a new data-driven model based on the Statistical Learning Theory was employed for ice breakup prediction. The model, known as Support Vector Machine (SVM), follows the principle that aims at minimizing the structural risk rather than the empirical risk. In order to estimate the appropriate parameters of the SVM, Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is performed through exponential transformation. A case study was conducted in the reach of the Yellow River. Results from the proposed model showed a promising performance compared with that from artificial neural network, so the model can be considered as an alternative and practical tool for ice breakup forecast.
Please read the editorial note first before accessing the preprint.
-
Please read the editorial note first before accessing the preprint.
-
Preprint
(861 KB)
-
Please read the editorial note first before accessing the preprint.
- Preprint
(861 KB) - Metadata XML
- Editorial note
- BibTeX
- EndNote
- Printer-friendly version
- Supplement
- Printer-friendly version
- Supplement
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 787 | 438 | 76 | 1,301 | 101 | 88 |
- HTML: 787
- PDF: 438
- XML: 76
- Total: 1,301
- BibTeX: 101
- EndNote: 88
Cited
11 citations as recorded by crossref.
- Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine B. Yao et al. 10.1080/18756891.2010.9727746
- Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration H. Tabari et al. 10.1007/s00271-012-0332-6
- A stacking ensemble learning framework for annual river ice breakup dates W. Sun & B. Trevor 10.1016/j.jhydrol.2018.04.008
- Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine B. Yan et al. 10.1155/2014/850141
- SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment H. Tabari et al. 10.1016/j.jhydrol.2012.04.007
- Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada R. Barzegar et al. 10.1016/j.jhydrol.2019.06.075
- A comparison of numerical and machine-learning modeling of soil water content with limited input data F. Karandish & J. Šimůnek 10.1016/j.jhydrol.2016.11.007
- Application of machine-learning models for diagnosing health hazard of nitrate toxicity in shallow aquifers F. Karandish et al. 10.1007/s10333-016-0542-2
- River ice breakup timing prediction through stacking multi-type model trees W. Sun 10.1016/j.scitotenv.2018.07.001
- Combining k-nearest-neighbor models for annual peak breakup flow forecasting W. Sun & B. Trevor 10.1016/j.coldregions.2017.08.009
- Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran A. Seifi & H. Riahi 10.2166/wcc.2018.003
H. Zhou
W. Li
C. Zhang
J. Liu
Please read the editorial note first before accessing the preprint.
- Preprint
(861 KB) - Metadata XML