A Framework on Quality Risk Early Warning for Hydraulic Engineering Construction Based on LSTM. Issue 1 (September 2020)

• Record Type: Journal Article
• Authors: She, Chunyong; Zhang, Ke; Feng, Jingchun; Cui, Le
• Is Part Of: IOP conference series. Volume 568:Issue 1(2020)
• Publication Date: 2020-09
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/568/1/012025
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: With the reform of "strengthening management and service", the focus of water conservancy project administration has gradually changed from the former mode of "attaching importance to examination and approval and ignoring supervision" to the mode of "attaching importance to service and strengthening supervision". However, government supervision relies only on human resources, so it is inevitable that there will be overlapping supervision, loopholes, low efficiency and high cost. In this paper, the LSTM model in deep learning is used to predict the risks of water conservancy projects according to the text information such as quality supervision and inspection reports, and the risk early warning system of water conservancy projects is designed to provide auxiliary decision support for the quality supervision of water conservancy projects, which is of great significance to the formulation of quality supervision measures, allocation of supervision and management resources and the quality guidance of water conservancy projects.