Prediction of Seawater Intrusion Run-Up Distance Based on K-Means Clustering and ANN Model

Coastal regions are increasingly vulnerable to sea-level rise and extreme storm events, making the accurate prediction of wave run-up on seawalls crucial for effective flood and erosion protection. This study presents a novel hybrid approach combining K-means clustering with artificial neural networks (ANNs) to predict wave run-up distance. The method begins with dimensionless analysis to scale all the variables, followed by data segmentation using K-means clustering to group wave characteristics such as the Froude number, scaled distance from the wave front to the shoreline, and wave nonlinearity. These clusters help to focus the ANN on more homogeneous wave conditions, significantly improving prediction accuracy. Two-dimensional flume experiments systematically varied wave height, period, and steepness, producing a robust dataset that accounts for a range of wave conditions. The model’s performance is demonstrated through a high R2 value of 0.97 and low mean squared error (MSE) of 0.0092, surpassing traditional ANN models in its ability to capture complex wave dynamics.