Document of bibliographic reference 323966

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

Functional autoregressive forecasting of long-term seabed evolution

Abstract

There is a need for decadal predictions of the seabed evolution, for example to inform resurvey strategies when maintaining navigation channels. The understanding of the physical processes involved in morphological evolution, and the viability of process models to accurately model evolution over these time scales, are currently limited. As a result, statistical approaches are used to supply long-term forecasts. In this paper, we introduce a novel statistical approach for this problem: the autoregressive Hilbertian model (ARH). This model naturally assesses the time evolution of spatially-distributed measurements. We apply the technique to a coastal area in the East Anglian coast over the period 1846 to 2002, and compare with two other statistical methods used recently for seabed prediction: the autoregressive model and the EOF model. We evaluate the performance of the three methods by comparing observations and predictions for 2002. The ARH model enables a reduction of 10% of the root mean squared errors. Finally, we compute the variability in the predictions related to time sampling using the jackknife, a method that uses subsamples to quantify uncertainties.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000294735000003

Bibliographic citation

Guillas, S.; Bakare, A.; Morley, J.; Simons, R. (2011). Functional autoregressive forecasting of long-term seabed evolution. J. Coast. Conserv. 15(3): 337-351. https://dx.doi.org/10.1007/s11852-009-0085-4

Topic

Marine

Is peer reviewed

true