Search

Coastal protection is of paramount importance because erosion and flooding affect millions of people living along the coast and can largely influence countries' economy. The implementation of nature‐based solutions for coastal protection, such as sand engines, has become more popular due to these interventions' adaptability to climate change. This study explores synergies between Artificial Intelligence (AI) and hydro‐morphodynamic models for the creation of efficient decision‐making tools for the choice of optimal sand engines configurations. Specifically, we investigate the use of long‐short‐term memory (LSTM) models as predictive tools for the morphological evolution of sand engines. We developed different LSTM models to predict time series of bathymetric changes across the sand engine as well as the time‐decline in the sand engine volume as a function of external forces and intervention size. Finally, a MATLAB framework was developed to return LSTM model results based on users' inputs about sand engine size and external forcings. Plain Language Summary: Sand engines are a type of coastal protection where a large volume of sand is added to the coastline to protect low‐lying areas from erosion and flooding. Sand engines, like other Nature‐based solutions, are gaining popularity due to their potentially lower maintenance costs compared to other concrete‐based coastal protection strategies and a number of co‐benefits. However, there are currently no design guidelines or decision‐making tools for sand engines. Here we address this gap in the state of knowledge and use Artificial Intelligence (AI) techniques to analyze the evolution of sand engines under different waves and external forcings. AI models are also used to predict the volume of sand being transferred by the waves from the location of deposition to the surrounding areas. To facilitate the use of AI models, this study proposes a computer software, sand engine surface, which includes all the AI models developed in this study, predicting the evolution of sand engine and volume of sand being transported. Key Points: This study presents models to predicts the evolution of Sand Engine over time in terms of Volumetric changes and Morphological changesAll our Long Short Term Memory models' results are accessible through Sand Engine Surface ApplicationSand Engine Surface is MATLAB framework providing results about time‐dependent morphological changes of sand engines based on users' inputs [ABSTRACT FROM AUTHOR]

Deltaic tidal flat is an important ecosystem that supports the livelihoods of millions of people globally. Due to the complexity and stochastic nature of tidal flat development, it is a challenging task to predict the tidal flat prograding limit accurately. In this study, we fill out this gap from the perspective of numerical simulation by focusing on the tidal flat extension limit of Nanhui Shoal (NHS), the largest marginal shoal of the Changjiang Estuary. Our results suggest that the tidal flat of NHS extended 29.16 m/y seaward in 1989-2002, had a rapid progradation of 158.08 m/y in 2002-2012 and a retreat rate of 26.88 m/y in 2012-2019, while the fluvial sediment supply decreased 56.78% from 1989-2002 to 2003-2012 and another 20.68% from 2003-2012 to 2013-2019. During the 1989-2002 period, the advancing rate of the -5misobaths followed a Gaussian frequency distribution, in agreement with simulation results of cellular automata. We suggest that in the absence of human perturbations, as the sediment input to NHS declines to the threshold of 0.01×108 t/y, the seaward advance of tidal flats may significantly slow down. However, human interferences generated large uncertainties on the development tendency of the NHS. [ABSTRACT FROM AUTHOR]

As climate-driven risks for the world's coastlines increase, understanding and predicting morphological changes as well as developing efficient systems for coastal forecast has become of the foremost importance for adaptation to climate change. Artificial Intelligence is a powerful technology that has been rapidly evolving recently and can offer new means of analysis for the coastal science field. Yet, the potential of these technologies for coastal geomorphology remains relatively unexplored with respect to other scientific fields. This article investigates the use of Artificial Neural Networks and Bayesian Networks in combination with fully coupled hydrodynamics and morphological models (Delft3D) for predicting morphological changes and sediment transport along coastal systems. Two sets of Artificial Intelligence models were tested, one set relying on localized modeling outputs or localized data sources and another set having reduced dependency from modeling outputs and, once trained, solely relying on boundary conditions and coastline geometry. The first set of models provides regression values greater than 0.95 and 0.86 for training and testing, respectively. The second set of reduced dependency models provides regression values greater than 0.84 and 0.76 for training and testing, respectively. Our results highlight the potential of AI and statistical models for coastal applications. [ABSTRACT FROM AUTHOR]