The prediction of water level based on support vector machine under construction condition of steel sheet pile cofferdam. (14th September 2020)

• Record Type: Journal Article
• Authors: Wang, Jianjun; Jiang, Zijie; Li, Fan; Chen, Weiming
• Is Part Of: Concurrency and computation. Volume 33:Number 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-14
• Subjects: municipal bridge -- steel cofferdam -- SVM -- water level prediction; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6003
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Although the construction of steel sheet pile cofferdam has good practicability in the process of water conservancy project construction, the construction period of the water project is long due to the large amount of work, and the cofferdam itself is greatly affected by the water level, topography, geological period, and other factors. With the continuation of time and the change of complex hydrogeological environment, it is easy to cause the accumulation of hidden safety hazards in the construction of the project during the construction period, and the unreasonable and untimely risk warning and control have led to some major construction accidents. In this paper, the SVM (Support Vector Machine) medium‐ and short‐term water level prediction model is established. The SVM tool is used to establish a prediction model that takes complex hydrological scenes and weather changes into account comprehensively, so that the medium‐ and short‐term water level can be predicted more accurately, thus achieving dynamic adjustment and better adapting to the actual requirements of steel sheet pile cofferdam construction. The results show that there is a good co‐integration relationship between the prediction factors selected by the medium‐ and short‐term water level prediction model, which proves the rationality of the multivariable predictions selected in this paper. At the same time, in the precipitation concentration period, the relative error of the SVM prediction model is relatively small, and it can achieve dynamic water level prediction with the update of the medium‐ and short‐term weather forecast, which can meet the requirements of engineering construction and accuracy.