AbstractAn effective system of coastal flooding forecasting in the case of storm is essential to mitigate coastal risks for the population living in low-land coastal zones (less than 10 m above MSL). Nowadays, predictions of coastal flooding are usually carried out by adopting nested numerical models. However, the models adopted to obtain the data in the nearshore area require high computational costs, which are often too demanding and not viable for large scale forecasting. Data-driven models, such as Artificial Neural Networks (ANNs) can help to solve the problem as they can map complex nonlinear relationships between input and output variables once a suitable dataset of process realizations is available. In the present study a forecasting model for coastal flooding based on ANNs, in which the input data are the offshore wave characteristics from large scale model and the output results are the flooded areas, is proposed. These outputs provided a straightforward prediction of the area interested by coastal flooding during storms. Here an application of the model to assess the flooding risk in the village of Granelli, in the Southeast of Sicily (Italy) is presented.
Antonioli, F., Falco, G.D., Presti, V.L., Moretti, L., Scardino, G., Anzidei, M., Bonaldo, D., Carniel, S., Leoni, G., Furlani, S. and Marsico, A., Petitta, M., Randazzo, G., Schicchitano, G., and Mastronuzzi, G., 2020. Relative Sea-Level Rise and Potential Submersion Risk for 2100 on 16 Coastal Plains of the Mediterranean Sea. Water, 12(8), p.2173.
Basher, R. (2006). "Global early warning systems for natural hazards: systematic and people-centred." Philosophical transactions of the royal society a: mathematical, physical and engineering sciences 364.1845: 2167-2182.
Booij, N., Ris, R., and Holthuijsen, L. (1999). A third-generation wave model for coastal regions 1. Model description and validation, J. Geophys. Res.-Oceans, 104, 7649–7666, doi:10.1029/98JC02622.
Cheung, K. F., Phadke, A. C., Wei, Y., Rojas, R., Douyere, Y. M., Martino, C. D., ... & Liao, S. (2003). Modeling of storm-induced coastal flooding for emergency management. Ocean Engineering, 30(11), 1353-1386.
Coquet, M., Mercier, D., & Fleury-Bahi, G. (2019). Assessment of the exposure to coastal flood risk by inhabitants of French coasts: The effect of spatial optimism and temporal pessimism. Ocean & Coastal Management, 177, 139-147.
Formentin, S.M.; Zanuttigh, B.; van der Meer, J.W. A neural network tool for predicting wave reflection, overtopping and transmission. Coastal Engineering Journal 2017, 59, 1750006.
Harley, M. D., Valentini, A., Armaroli, C., Perini, L., Calabrese, L., & Ciavola, P. (2016). Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy. Natural Hazards and Earth System Sciences, 16(1), 209.
Horsburgh, K.; De Vries, H. Guide to storm surge forecasting; World Meteorological Organisation, 2011.
Jain, P.; Deo, M. Neural networks in ocean engineering. Ships and offshore structures 2006, 1, 25–35.
Kopp, R.E. Probabilistic 21 st and 22 nd century sea-level projections at a global network of tide-gauge sites (2014) Earth’s Future, 2, pp. 383-406.
Lashley, C. H., Roelvink, D., van Dongeren, A., Buckley, M. L., & Lowe, R. J. (2018). Nonhydrostatic and surfbeat model predictions of extreme wave run-up in fringing reef environments. Coastal Engineering, 137, 11-27.
Lee, T.; Jeng, D. Application of artificial neural networks in tide-forecasting. Ocean Engineering 2002, 29, 1003–1022.
Le Bars, D., Drijfhout, S., & De Vries, H. (2017). A high-end sea level rise probabilistic projection including rapid antarctic ice sheet mass loss. Environmental Research Letters, 12(4)
Marino M, Faraci C, Musumeci RE. (2020a) Shoaling Waves Interacting with an Orthogonal Current. Journal of Marine Science and Engineering; 8(4):281.
Marino M, Faraci C, Musumeci RE. (2020b) An experimental setup for combined wave-current flow interacting at a right angle over a plane beach. Italian Journal of Engineering Geology and Environment, 1, pp. 99-106.
McGranahan, G., Balk, D., & Anderson, B. (2007). The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and urbanization, 19(1), 17-37.
More, A.; Deo, M. Forecasting wind with neural networks. Marine structures 2003, 16, 35–49.
Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. (2015). Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment. PloS one, 10(3), e0118571.
Peres, D.; Iuppa, C.; Cavallaro, L.; Cancelliere, A.; Foti, E. Significant wave height record extension by neural networks and reanalysis wind data. Ocean Modelling 2015, 94, 128–140.
Poelhekke, L.; Jäger, W.S.; Van Dongeren, A.; Plomaritis, T.A.; McCall, R.; Ferreira, Ó. Predicting coastal hazards for sandy coasts with a Bayesian Network. Coastal Engineering 2016, 118, 21–34.
Reid, R.O. Approximate response of water level on a sloping shelf to a wind fetch which moves towards shore. Technical report, TEXAS A AND M UNIV COLLEGE STATION, 1956.
Roelvink D.J.A., van Dongeren A., McCall R., Hoonhout B., van Rooijen A, van Geer P., de Vet L., Nederhoff K., Quataert E. (2015). “XBeach Technical Reference: Kingsday Release”. Deltares.
Sahoo, B.; Bhaskaran, P.K. Prediction of storm surge and coastal inundation using Artificial Neural Network–A case study for 1999 Odisha Super Cyclone. Weather and Climate Extremes 2019, 23, 100196.
Small, C., Nicholls, R.J. (2003). A global analysis of human settlement in coastal zones. Journal of coastal research, 584-599.
Stockdon, H. F., Holman, R. A., Howd, P. A., & Sallenger Jr, A. H. (2006). Empirical parameterization of setup, swash, and runup. Coastal engineering, 53(7), 573-588.
This work is licensed under a Creative Commons Attribution 4.0 International License.