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首页> 外文期刊>Journal of hydroinformatics >Emulating the estuarine morphology evolution using a deep convolutional neuralnetwork emulator based on hydrodynamic results of a numerical model
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Emulating the estuarine morphology evolution using a deep convolutional neuralnetwork emulator based on hydrodynamic results of a numerical model

机译：Emulating the estuarine morphology evolution using a deep convolutional neuralnetwork emulator based on hydrodynamic results of a numerical model

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摘要

Coastal and estuarine areas present remarkable environmental values, being key zones for the development of many human activities suchas tourism, industry, fishing, and other ecosystem services. To promote the sustainable use of these services, effectively managing theseareas and their water and sediment resources for present and future conditions is of utmost importance to implement operational forecastplatforms using real-time data and numerical models. These platforms are commonly based on numerical modelling suites, which can simu-late hydro-morphodynamic patterns with considerable accuracy. However, in many cases, considering the high spatial resolution models thatare necessary to develop operational forecast platforms, a high computing capacity is also required, namely for data processing and storage.This work proposes the use of artificial intelligence (AI) models to emulate morphodynamic numerical model results, allowing us to optimizethe use of computational resources. A convolutional neural network was implemented, demonstrating its capacity in reproducing the erosionand sedimentation patterns, resembling the numerical model results. The obtained root mean squared error was 0.59 cm, and 74.5 years ofmorphological evolution was emulated in less than 5 s. The viability of surrogating numerical models by Al techniques to forecast themorphological evolution of estuarine regions was clearly demonstrated.

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