Open AccessResearch on the quality risk assessment model for water conservancy projects based on the semi-supervised classification of text
Author(s) -
Zhiwei Zou,
Jingchun Feng,
Wei He,
Sheng Li,
Ke Zhang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/643/1/012130
Subject(s) - naive bayes classifier , quality (philosophy) , word2vec , construct (python library) , computer science , data mining , feature (linguistics) , water quality , support vector machine , machine learning , risk analysis (engineering) , artificial intelligence , business , ecology , philosophy , linguistics , epistemology , embedding , biology , programming language
This paper systematically analyzes the quality supervision data system of water conservancy projects. Then, according to the characteristics of water conservancy project quality supervision text, the Word2vec algorithm and TFIDF algorithm are combined to construct a feature extraction system of water conservancy project quality supervision text suitable for short length and few samples. Finally, a semi-supervisory model system consisting of logical regression, simple Bayes, and SVM is introduced to solve the problem of incomplete quality supervision risk data for water conservancy projects. To sum up, on the basis of the three parts, i.e. data system, feature extraction, and semi-supervisory text classification, we build a water conservancy project quality risk assessment framework and provide a data processing tool for machine learning of hydraulic engineering quality risks.