According to water power, structure and foundation conditions of aqueduct, it has established aqueduct safety assessment indicator system and standards. Based on statistical learning theory, support vector machine shifts the learning problems into a convex quadratic programming problem with structural risk minimization criterion, which could get the global optimal solution, and be applicable to solving the small sample, nonlinearity classification and regression problems. In order to evaluate the safety condition of aqueduct, it has established the aqueduct safety assessment model which is based on support vector machine. It has divided safety standards into normal, basically normal, abnormal and dangerous. According to the aqueduct safety assessment standards and respective evaluation level, the sample set is generated randomly, which is used to build a pair of classifier with many support vectors. The results show that the method is feasible, and it has a good application prospect in irrigation district canal building safety assessment.
Dynamic prediction of soil salinization in an irrigation district based on the support vector machine
