TY - GEN

T1 - Verification of Diffusion Model for Coastal Profile Evolution

AU - Baramiya, Denis

AU - Lavrentiev, Mikhail

AU - Spigler, Renato

N1 - Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/10/5

Y1 - 2020/10/5

N2 - We continue studying the possibility to predict the so-called long-term evolution of a depth profile in coastal zone. Long-term means at the scale of years, namely 10s years of measurements and several years of prediction. Prediction of coastal profile evolution is done by first calibrating a diffusion-type model, earlier proposed in the literature to describe this phenomenon, and then extrapolating the model's coefficients (by the Support Vector Machine) over time. A realistic prediction is then made by solving numerically a diffusion equation whose coefficients have been obtained by the extrapolated values of such coefficients, numerically obtained using the measured data. In previous works, we studied the model in the 'frequency-type' domain. Observations made over more than 20 years were used to accomplish the aforementioned calibration. We have then analysed the diffusion model directly in the time-space domain. In this paper, we consider the possibility of prediction the coastal profiles evolution for 3 years using only 10 or even 5 years measurements. Numerical experiments made by using the JARKUS dataset have shown that our model is indeed capable to predict the depth profile evolution occurring 3 years from now, within a relative error normally less than 10%.

AB - We continue studying the possibility to predict the so-called long-term evolution of a depth profile in coastal zone. Long-term means at the scale of years, namely 10s years of measurements and several years of prediction. Prediction of coastal profile evolution is done by first calibrating a diffusion-type model, earlier proposed in the literature to describe this phenomenon, and then extrapolating the model's coefficients (by the Support Vector Machine) over time. A realistic prediction is then made by solving numerically a diffusion equation whose coefficients have been obtained by the extrapolated values of such coefficients, numerically obtained using the measured data. In previous works, we studied the model in the 'frequency-type' domain. Observations made over more than 20 years were used to accomplish the aforementioned calibration. We have then analysed the diffusion model directly in the time-space domain. In this paper, we consider the possibility of prediction the coastal profiles evolution for 3 years using only 10 or even 5 years measurements. Numerical experiments made by using the JARKUS dataset have shown that our model is indeed capable to predict the depth profile evolution occurring 3 years from now, within a relative error normally less than 10%.

KW - depth profile evolution

KW - diffusion model

KW - prediction

UR - http://www.scopus.com/inward/record.url?scp=85104681969&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF38699.2020.9389256

DO - 10.1109/IEEECONF38699.2020.9389256

M3 - Conference contribution

AN - SCOPUS:85104681969

T3 - 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast

BT - 2020 Global Oceans 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020

Y2 - 5 October 2020 through 30 October 2020

ER -