روش رگرسیون بردار پشتیبانی برای پیش بینی موج طوفان

To avoid property loss and reduce risk caused by typhoon surges, accurate prediction of surge deviation is an important task. Many conventional numerical methods and experimental methods for typhoon surge forecasting have been investigated, but it is still a complex ocean engineering problem. In this paper, support vector regression (SVR), an emerging artificial intelligence tool in forecasting storm surges is applied. The original data of Longdong station at Taiwan ‘invaded directly by the Aere typhoon’ are considered to verify the present model. Comparisons with the numerical methods and neural network indicate that storm surges and surge deviations can be efficiently predicted using SVR.

Storm surge is caused primarily by high winds pushing on the ocean's surface. The wind causes water to pile up higher than the ordinary sea level. Low pressure at the center of a weather system also has a small secondary effect, as can the seabed bathymetry. It is this combined effect of low pressure and persistent wind over a shallow water body that is the most common cause of storm surge flooding problems. Storm surges are particularly dangerous when a high tide occurs together with the surge. In the United States, the greatest recorded storm surge was generated by 2005's Hurricane Katrina, which produced a storm surge 9 m (30 ft) high in the town of Bay St. Louis, Mississippi. While high-impact events will undoubtedly occur in the future, the advent and further improvement of predictions of storm surges may greatly reduce the loss of lives, and potentially reduce property damage. Storm surge models were developed in the 1950s. Hansen (1956) proposed a fluid dynamic model to describe the storm surge phenomenon in the North Sea. Coarse- and fine-grid cases were considered by Jelesnianski (1965) to calculate storm surges. A special program to list the amplitudes of surges from hurricanes (SPLASH) was developed by Jelesnianski (1972). FEMA (1988) developed a storm surge forecast model using the finite-difference method. Holland (1980) used wind forcing to derive storm surge models. Kawahara et al. (1982) applied a two-step explicit finite-element method for storm surge propagation analysis. Hubbert et al. (1991) proposed an operational typhoon storm surge forecast model for the Australian region. Flather (1991) applied the typhoon model of Holland (1980) to simulate typhoon-induced storm surges in the northern Bay of Bengal. The sea, lake, and overland surges from hurricanes (SLOSH) model (Jelesnianski and Shaffer, 1992) followed SPLASH and considered wetting and drying processes. Emergency managers use this SLOSH data to determine which areas must be evacuated to avoid storm surge consequences. Storm surge forecast systems are based on two-dimensional shallow-water equation models (Vested et al., 1995; Bode and Hardy, 1997). Hsu et al. (1999) developed a storm surge model for Taiwan and arrived at analytical expressions considering both gradient wind and radius. The MIKE 21 hydrodynamic model is a general numerical modeling system for simulation of unsteady two-dimensional flow, developed at the Danish Hydraulic Institute of Water and Environment (DHI, 2002). Xie et al. (2004) introduced a Princeton ocean model storm surge to consider flooding and drying, and further consider nonlinear effects in the surge process. Huang et al. (2005) applied finite-volume method (FVM), a numerical method to simulate typhoon surges in the northern part of Taiwan. Despite close attention of the engineering community, the storm surge problem is still far from its solution, because there are too many unknown parameters to account for, such as the central typhoon pressure, speed of typhoon, rainfall and influence of local topography. Artificial neural networks (ANNs) are being widely applied to various areas to overcome the problem of exclusive and nonlinear relationships. The back-propagation neural network (BPN) developed by Rumelhart et al. (1986) is the most representative learning model for the ANN. The procedure of the BPN repeatedly adjusts the weights of connections in the network so as to minimize the measure of difference between the actual output vector of the net and the desired output vector. The BPN is widely applied in a variety of scientific areas—especially in applications involving diagnosis and forecasting. ANN has been widely applied in various areas (Daniell, 1991; French et al., 1992; Karunanithi et al., 1994; Grubert, 1995; Minns, 1998; Tsai and Lee, 1999; Lee et al., 2002; Nagy et al., 2002; Lee and Jeng, 2002; Coppola et al., 2003; Jeng et al., 2004; Lee, 2004 and Lee, 2006; Makarynskyy, 2005; Bateni et al., 2007). Recently, Lee (2008) forecast a storm surge using neural networks (NNs). However, the estimated results still need to be improved. In fluid dynamic models, the accuracy of storm surges depends on the fineness of the grid. The storm surge forecast model using finite-difference method involved the solution of a large number of equations. Explicit finite-element methods for storm surge propagation analysis are not very accurate compared to implicit methods. The storm surge forecast system cannot be applied to deep-water equation models. In FVMs, similar to finite-difference methods, values are evaluated at discrete places on a meshed geometry. In this method, the typhoon model is always represented in terms of relatively simple parametric model, the distribution of wind velocity and pressure. These methods also involve five equations and many boundary conditions and the method is conservative. This work was carried out by the third author and the calculated computer time was 9.6 h on an Intel Pentium IV 2.6 GHz personal computer. Since there are too many unknown parameters to account for, predictions of storm surges by NN and support vector machine (SVM) will be useful. BPN model was adopted by the third author to predict storm surges by using one hidden layer with three hidden neurons with a learning rate of 0.07 and momentum factor of 0.9, and the network was trained for 10,000 epochs. The training time was 50 s for every case based on an Intel Pentium IV personal computer with a 2.6 GHz CPU and 1 GB DRAM. Hence one needs to determine the number of hidden layers, number of neurons in a hidden layer, momentum factor, learning rate, sigmoidal gain, etc., whereas in SVM, if a proper kernel is selected, two parameters such as C and ε are required to achieve the desired accuracy. Therefore, the support vector regression (SVR) model considered for forecasting storm surges and surge deviations is desired and considered in this paper.

This paper presents a promising support vector regression (SVR) technique for storm surge predictions. The performance of the proposed method is verified by comparing the predicted storm surge levels (with different kernel functions) with actual values. However, estimated results by SVR produce remarkably smaller estimation errors compared to those of NNs. Moreover, SVR takes lesser computer time than NNs for the two cases. From the results it can be concluded that SVR method can predict storm surges with higher estimation accuracy and shorter computation time. It is seen that from 36 h of data, storm surge levels for next 36 h can be predicted by SVR. Hence SVR can be used for online prediction of surge levels or surge deviations that are going to take place after several hours. It is expected that prediction of storm surges by SVR method will play an important role in future surge level predictions as well. Moreover, SVR method can be utilized by field engineers since it does not require any procedure to determine the explicit form as in the regression analysis or any knowledge on the network's architecture as in the NN techniques.