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- Title
A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes.
- Authors
Wei, Xianglong; Lu, Yongjun; Wang, Zhili; Liu, Xingnian; Mo, Siping
- Abstract
Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.
- Subjects
NEURAL circuitry; MACHINE learning; EMPIRICAL research; ARTIFICIAL intelligence; SELF-organizing maps; SUPPORT vector machines
- Publication
Water (20734441), 2018, Vol 10, Issue 11, p1680
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w10111680