Wavelet kernel support vector machines forecasting techniques: Case study on water-level predictions during typhoons

Authors:
Chih-Chiang Wei
Affiliations:
Department of Information Management, Toko University, No. 51, Sec. 2, University Rd., Pu-Tzu City, Chia-Yi County 61363, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 16
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Geometry and invariance in kernel based methods

Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
New Support Vector Algorithms

Neural Computation
An adaptive error penalization method for training an efficient and generalized SVM

Pattern Recognition
Pattern recognition with SVM and dual-tree complex wavelets

Image and Vision Computing
Type-2 fuzzy logic-based classifier fusion for support vector machines

Applied Soft Computing
Wavelet support vector machine for induction machine fault diagnosis based on transient current signal

Expert Systems with Applications: An International Journal
Fuzzy one-class support vector machines

Fuzzy Sets and Systems
The forecasting model based on wavelet ν-support vector machine

Expert Systems with Applications: An International Journal
Relevance vector machine with adaptive wavelet kernels for efficient image coding

Neurocomputing
Computationally efficient bandwidth allocation and power control for OFDMA

IEEE Transactions on Wireless Communications
The wavelet transform, time-frequency localization and signal analysis

IEEE Transactions on Information Theory
A comparison between neural-network forecasting techniques-case study: river flow forecasting

IEEE Transactions on Neural Networks
Wavelet networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper presents a novel algorithm, wavelet support vector machines (wavelet SVMs), for forecasting the hourly water levels at gauging stations. These stations are under strong precipitations and affected by tidal effects during typhoons. An admissible wavelet kernel SVMs implements the combination of wavelet technique with SVMs. The wavelet is a multi-dimension wavelet function that can approximate arbitrary nonlinear functions. Using both classical Gaussian and wavelet SVMs, this study constructed the channel level models for forecasting downstream water levels. The developed models were then applied to the Tanshui River Basin in Taiwan and the water levels at various lag times predicted by both Gaussian and wavelet SVMs were compared. Analysis results showed that the optimal situation occurred at the lag time of 3h with relative mean square errors (RMSEs) of 0.205 and 0.160m obtained by the Gaussian and wavelet SVMs, respectively at Taipei Bridge station and RMSEs of 0.154 and 0.092m at Tudigong station, respectively. As seen in the comparison, wavelet SVMs yielded more accurate predictions than Gaussian SVMs and offered a practical solution to the problem of water-level predictions during typhoon attacks.