estimating coastal dyke leakage flow using support vector machine (svm) and multivariate adaptive regression spline (mars) model

It is important to check for leakage flow in hydraulic and marine structures during design, as uncontrolled leakage can cause irreparable damage. soft computing methods can be used to easily model, analyze and control complex systems. this study uses support vector machine (svm) method to predict leakage discharge of coastal dykes. five different models are used to achieve this goal, with parameters including the length of the cutoff blanket, dyke depth, and water head considered. the best support vector machine model is checked using a multivariate adaptive regression spline model (mars) for prediction. results show that the model including all parameters predicts settlement discharge with very good accuracy compared to the laboratory model, with a coefficient of determination and root mean square coefficient of 0.949 and 0.058 respectively in the test stage and 0.93 and 0.06 in the test phase estimates. the dyke depth parameter has the greatest effect on leakage flow, while the water head has the least effect among input parameters to the model. although the adaptive regression multivariate spline model accurately estimates the annual dyke leakage flow rate, it is less accurate than the support vector machine method.