Wind and water levels influence wave overtopping and consequent coastal flood threat, which is especially important in hyper-tidal bays where even modest variations in wave heights may be devastating if they coincide with high tides. The influence of wind and wave characteristics on wave propagation, as well as the sensitivity of significant wave height, are numerically investigated along the Gaza Strip's beachfront as an example. Wind waves with a high amplitude and short duration are susceptible to opposing winds, and their steepening effect varies throughout the bay shoreline, underlining the impact of shoreline geometry and bathymetry on wave hazard. The findings contribute to our existing knowledge of the complex interplay between wind and waves, as well as the crucial variables that maximize danger and hazard variability along the coastline. The findings of this study can assist port and harbor managers prevent financial losses due to downtime, influence sustainable coastal sea defense design, and better understand how wave danger may change in the future owing to shifting storm tracks. The findings can also be used to improve coastal infrastructure design and disaster response planning. Two scenarios were investigated with a wind direction of 330 and 30. It seems that when the wind direction is 330, it produces a higher Hs of 1.2 m and relatively larger wave return period with a range of 12-22 s and a higher wave energy dissipation of 220 N/Ms. In contrast, when the wind direction is 30, it produces a smaller HS of 1m with a short wave return period of 15-17s and smaller wave energy dissipation of 120 N/Ms. Overall, a wind direction of 30 has fewer occurring chances over the year but it seems to produce a destructive wave that are spread over the whole coast with a rapid return period.
Scour around bridge piers is a well-known threat to bridge stability worldwide. It can cause losses in lives and the economy, especially during floods. Therefore, an artificial intelligence approach called artificial neural network (ANN) was used to predict the scour depth around bridge piers. The ANN model was trained with laboratory data, including pier width, flow velocity, particle diameter, sediment critical velocity, flow depth, and scour depth. The data was divided into 70% for training, 15 for validation, and 15% for testing. Besides, the ANN model was trained using various training algrthins and a single hidden layer with 20 neurons in the hidden layer. The results showed that the ANN model with Bayesian regularization backpropagation training algorithm provides a better predicted scour depth with a correlation coefficient (R) equal to 0. 9692 and 0.926 for training and test stages, respectively. Besides, it showed a low mean squared error (MSE), which was 0.0034 for training and 0.0066 for the test. These results were slightly better than the ANN with Levenberg-Marquardt backpropagation with R training equals 0.9552 (MSE training = 0.0047), and R test equals 0.838 (MSE test = 0.007).On the other hand, the ANN model with a scaled conjugate gradient backpropagation training algorithm showed worse predictions (R training = 0.7407 and R test = 0.6409). Besides, the ANN model shows better outcomes than the linear regression model. Finally, the sensitivity analysis has shown that the pier width is the most crucial parameter for estimating scour depth using the ANN model.