Leeway Prediction of Oceanic Disastrous Target via Support Vector Regression
Nipon Theera-Umpon*, and Udomsak Boonprasert**
*Department of Electrical Engineering, Faculty of Engineering, Chang Mai University, Chiang Mai 50200, Thailand
**The Royal Thai Navy, Sattahip, Chonburi 20180, Thailand
This paper demonstrates an application of support vector machine (SVM) to the oceanic disasters search and rescue operation. The support vector regression (SVR) for system identification of a nonlinear black-box model is utilized in this research. The SVR-based ocean model helps the search and rescue unit by predicting the disastrous target’s position at any given time instant. The closer the predicted location to the actual location would shorten the searching time and minimize the loss. One of the most popular ocean models, namely the Princeton ocean model, is applied to provide the ground truth of the target leeway. From the experiments, the results on the simulated data show that the proposed SVR-based ocean model provides a good prediction compared to the Princeton ocean model. Moreover, the experimental results on the real data collected by the Royal Thai Navy also show that the proposed model can be used as an auxiliary tool in the search and rescue operation.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.