Improving Shoreline Forecasting Models with Multi-Objective Genetic Programming

Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain the relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a shoreline prediction model. Coupled with NSGA-II, the CGP individuals are evaluated and selected during evolution according to their predictive skills at five different coastal sites. This work presents a comparison between the CGP-evolved models and the base ShoreFor model. In addition to its ability to produce well-performing models, the work demonstrates the usefulness of CGP as a research tool to gain insight into the behaviors of shorelines at different points around the globe.