Functional autoregressive forecasting of long-term seabed evolution.
J COAST CONSERV
337 - 351.
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.
|Title:||Functional autoregressive forecasting of long-term seabed evolution|
|Keywords:||Seabed evolution, Forecasting, Autoregressive Hilbertian model, EOF, Jackknife, COASTAL MORPHOLOGICAL EVOLUTION, ORTHOGONAL FUNCTION-ANALYSIS, SHORELINE VARIABILITY, FIELD DATA, BOOTSTRAP, TIMESCALES, JACKKNIFE|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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