# Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review

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## Abstract

**:**

## 1. Introduction

## 2. Application of Coastal-Area Morphodynamic Models

## 3. Equilibrium Modelling Approaches

## 4. Machine Learning (ML) Approaches

_{1}, X

_{2}, …, X

_{n}) = P(X

_{1}) ⋅ P(X

_{2}|X

_{1}) ⋅ P(X

_{3}|X

_{1},X

_{2}) ⋅ …⋅ P(Xn|X

_{1},X

_{2}, …, X

_{n−1})

_{1}, X

_{2}, …, and X

_{n}are the node variables in the Bayesian network, and P(X

_{n}|X

_{1}, X

_{2}, …, X

_{n−1}) represents the conditional probability of variable Xn given the occurrence of X

_{1}, X

_{2}, …, X

_{n−1}simultaneously. In the literature, Bayesian networks have been widely applied to predict coastal morphodynamic changes caused by human activities [34,35]. A Bayesian network can quantify uncertainties from the input and output. Uncertainty quantification is the key difference between a Bayesian network and other ML approaches. Even coastal morphodynamic models cannot provide direct probabilistic estimations.

_{i}is the discrete variable in the Bayes network. LR can be used to measure the effect of an increasing number of variables or a decreasing number of variables on the outcome. Different human activities can be described by different variables of O

_{i}[34,35,40]. The long-likelihood ratio, LR, can quantify the relative importance of different human activities. For example, human activities are quantified by classifying the types of infrastructures [41]. Nourishment events at different locations can be classified by using a nourishment index [35]. The flexible utilisation of anthropogenic data enables the wide application of the BN approach. Later research studied the effects of beach grass on dune morphological changes with the application of the hybrid BN approach including continuous variables [42]. The BN approach is useful for both prediction and scientific studies. However, the BN model has some limitations. First, its data-driven nature means that it cannot consider the actual physical processes of estuary development but rather focuses more on the inductive value of the data. Moreover, its ability to predict situations that are not present in a dataset is limited. Conditional probability tables for the outcome factor often exhibit similar probabilities across all states, indicating a high level of uncertainty.

## 5. How to Select Proper Modelling Approaches for Studying Human Impacts

## 6. Future Directions

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**(

**a**) government policy (

**b**) Human land reclamation activities in Pearl River Estuary (modified from [4]). (

**b**) as result of (

**a**).

**Figure 2.**(

**a**) Aerial image of Shuidongwan Lagoon, South China Sea; (

**b**) schematisation of geomorphic elements and exchange of Shuidongwan Lagoon for ASMITA (the schematisation was based on [32]).

**Figure 3.**(

**a**)Approaches to modelling human impacts to analyse coastal and estuarine evolution; (

**b**) different states of equilibrium; (

**c**) system states and resilience(Reprinted from What do you mean, ‘resilient geomorphic systems’? 305, with permission from Elsevier [33]).

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**MDPI and ACS Style**

Deng, J.; Yu, H.
Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review. *J. Mar. Sci. Eng.* **2023**, *11*, 1426.
https://doi.org/10.3390/jmse11071426

**AMA Style**

Deng J, Yu H.
Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review. *Journal of Marine Science and Engineering*. 2023; 11(7):1426.
https://doi.org/10.3390/jmse11071426

**Chicago/Turabian Style**

Deng, Junjie, and Hongze Yu.
2023. "Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review" *Journal of Marine Science and Engineering* 11, no. 7: 1426.
https://doi.org/10.3390/jmse11071426