Calibration of shoreline change model using an optimization algorithm

As the prediction and preparation for coastline change are regarded as an important social issue, improving the accuracy of the numerical shoreline change model to predict long-term shoreline changes is becoming increasingly important. Most shoreline change models contain several parameters to refle...

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Bibliographic Details
Published in:Journal of coastal conservation 2020-06, Vol.24 (3), p.1-16, Article 33
Main Authors: Kang, Haneul, Chun, Insik, Oh, Byungcheol
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Calibration
Coastal erosion
Coastal Sciences
Coasts
Data
Earth and Environmental Science
Geography
Littoral environments
Mathematical models
Model accuracy
Nature Conservation
Oceanography
Optimization
Optimization algorithms
Optimization techniques
Parameters
Remote Sensing/Photogrammetry
Shoreline changes
Shoreline protection
Shorelines
Statistical methods
Wave climate
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Summary:As the prediction and preparation for coastline change are regarded as an important social issue, improving the accuracy of the numerical shoreline change model to predict long-term shoreline changes is becoming increasingly important. Most shoreline change models contain several parameters to reflect the local characteristics of the study area, and their performance can be significantly enhanced by calibrating the parameters with the field data. However, as the number of the parameters increases, the model calibration becomes extremely difficult, especially in the model application for a long and complicated shoreline. In the present study, a method for calibrating the parameters of a shoreline change model that uses an optimization algorithm is proposed. Subsequently, its performance is demonstrated by applying the model to the Gangneung littoral cell set on the East Coast of Korea. First, the parameter calibration was performed using the records of 17 years (1980–1996) for all input data, including artificial coastal developments, conservation efforts, wave climate statistics, and the observed shoreline data. Subsequently, the calibrated model was verified using the data of the next 17 years (1997–2013). The results showed that the model calibration using the optimization technique significantly improved the accuracy of the shoreline change model.
ISSN:1400-0350
1874-7841
DOI:10.1007/s11852-020-00750-y