Improving a Shoreline Forecasting Model with Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{alnajar2023improving,

• author = "Mahmoud {Al Najar} and Rafael Almar and Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson",
• title = "Improving a Shoreline Forecasting Model with Symbolic Regression",
• booktitle = "ICLR 2023 Workshop on Tackling Climate Change with Machine Learning",
• year = "2023",
• address = "Kigali Rwanda",
• month = "4 " # may,
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Machine Learning, Interpretable ML, XAI, Symbolic Computation, Earth Observation & Monitoring, Extreme Weather, Ocean, Atmosphere, Hybrid Physical Models, Time-series Analysis",
• URL = "https://www.climatechange.ai/papers/iclr2023/21",
• URL = "https://www.climatechange.ai/papers/iclr2023/21/paper.pdf",
• URL = "https://hal.science/hal-04281530",
• size = "12 pages",
• abstract = "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 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 physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones.",
• notes = "Published as a workshop paper at Tackling Climate Change with Machine Learning, ICLR 2023",

}

Genetic Programming entries for Mahmoud Al Najar Rafael Almar Erwin W J Bergsma Jean-Marc Delvit Dennis G Wilson

Citations