Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements

The volume of material required for the construction of new and expansion of existing beach sites is an important parameter for coastal management. This information may play a crucial role when deciding which beach sites to develop. This work examines whether artificial neural networks (ANNs) can predict the spatial variability of nourishment requirements on the Croatian coast. We use survey data of the nourishment volume requirements and gravel diameter from 2016 to 2020, fetch length, beach area and orientation derived from national maps which vary from location to location due to a complex coastal configuration on the East Adriatic coast, and wind, tide, and rainfall data from nearby meteorological/oceanographic stations to train and test ANNs. The results reported here confirm that an ANN can adequately predict the spatial variability of observed nourishment volumes (R and MSE for the test set equal 0.87 and 2.24 x 10(4), respectively). The contributions of different parameters to the ANN's predictive ability were examined. Apart from the most obvious parameters like the beach length and the beach areas, the fetch length proved to be the most important input contribution to ANN's predictive ability, followed by the beach orientation. Fetch length and beach orientation are parameters governing the wind wave height and direction and hence are proxies for forcing.

Automated summary

This study successfully predicts the spatial variability of nourishment requirements on the Croatian coast using artificial neural networks (ANNs), with R and MSE values of 0.87 and 2.24 x 10(4) for the test set. Fetch length and beach orientation were found to be the most important parameters contributing to the ANN's predictive ability, as they govern wind wave height and direction, acting as proxies for forcing.