Hu, Z.Y., 2020. Economic forecast of coastal regions based on support vector machine model. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 211–214. Coconut Creek (Florida), ISSN 0749-0208.

Support vector machine (SVM) is a new technology in data mining. It is a new tool to solve machine learning problems with the help of optimization. Support vector machines belong to a new machine learning that extends from statistical learning theory. Its structure is relatively simple, with good generalization ability and global optimality. Support vector machine has provided a unified framework for solving finite sample learning problems, and there are many solutions proposed. It can deal with those more complex problems and introduces the characteristics of the support vector machine model. Aiming at the application of the model in economic forecasting of coastal regions, a method to improve the prediction accuracy of the model is proposed. The theoretical analysis and practical application verification are performed, which shows that this method can obtain more accurate prediction results.