Improving a Shoreline Forecasting Model with Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{alnajar2023improving,
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author = "Mahmoud {Al Najar} and Rafael Almar and
Erwin W. J. Bergsma and Jean-Marc Delvit and Dennis G. Wilson",
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title = "Improving a Shoreline Forecasting Model with Symbolic
Regression",
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booktitle = "ICLR 2023 Workshop on Tackling Climate Change with
Machine Learning",
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year = "2023",
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address = "Kigali Rwanda",
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month = "4 " # may,
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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",
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URL = "https://www.climatechange.ai/papers/iclr2023/21",
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URL = "https://www.climatechange.ai/papers/iclr2023/21/paper.pdf",
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URL = "https://hal.science/hal-04281530",
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size = "12 pages",
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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.",
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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