Genetic Programming for storm surge forecasting.

Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Therefore, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, we propose a new method to use GP for evolving models for storm surge forecasting. Experimental results on datasets collected from the Tottori coast of Japan show that GP can evolve accurate storm surge forecasting models. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models evolved by GP are interpretable. • The paper proposes a new approach to using Genetic Programming (GP) to evolve models for storm surge forecasting. • GP can evolve more accurate models for storm surge forecasting than other existing machine learning methods. • The model evolved by GP is more interpretable than models evolved by other (black-box) methods such as neural networks. • GP can automatically select relevant features when evolving storm surge forecasting models. [ABSTRACT FROM AUTHOR]