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- W51145412 abstract "This research identifies and validates a data-driven statistical method formorphological and morphodynamic modelling and prediction. The method, calledthe spatial regression model has been modified to account for a single forcing factor.In the model, spatial behaviour across a domain is accounted for as surface functions;changes between successive surfaces then explain the spatial and temporal evolution,which is used to calculate a prediction. In the model extension, the covariabilitybetween the time series of the morphology and forcing are assessed to derive anestimate that scales future forcing.The model is applied to idealised morphological scenarios and two study sites, whichare the nearshore zone of Poole Bay, governed by wave and anthropogenic factors,and the Great Yarmouth sandbank system, governed by tidal, wave and stormconditions. The idealised scenarios results show that the model identifiesmorphological evolution. For scenarios with temporal periodicity, stochastic effectsand noise, it generated predictions with Brier Skill Scores (BSS) of ≥0.97.Accounting for forcing improves the prediction for scenarios with complexbehaviour. At the Great Yarmouth site, a BSS of 0.65 is obtained using themorphological characteristics only. In using the wind time series as a proxy for theforcing, the skill score decreases. For the Poole Bay site, a 0.64 BSS is obtainedassessing the morphological characteristics only. Accounting for the wave time seriesas the forcing improves the score compared to that without forcing for the same timeperiod. In assuming no change occurs across the domain, the model improves onresults for the scenarios but generates larger errors at the real sites. Model sensitivityis dictated by the characteristics and complexity of the morphological behaviour andthe spatial and temporal resolution of the associated time series datasets, which inturn influences the prediction accuracy. The spatial regression model can be a usefultool for morphodynamic modelling applications at large scales, where accounting forexternal forcing conditions can improve prediction results, given certain behaviourand data properties." @default.
- W51145412 created "2016-06-24" @default.
- W51145412 creator A5063034127 @default.
- W51145412 date "2011-07-28" @default.
- W51145412 modified "2023-09-23" @default.
- W51145412 title "Modelling and prediction of seabed morphodynamics using a multi-dimensional statistical method with forcing" @default.
- W51145412 hasPublicationYear "2011" @default.
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