Random Forest Model and Application of Arch Dam’s Deformation Monitoring and Prediction
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Dam deformation prediction plays an important role in the safety assessment of dam operation. Traditional models lack forecasting precision and the simulation effect is not stable enough. Besides, if abnormal values of dam deformation exist, traditional machine algorithm model lacks the flexibility of dealing with these abnormal data, which will lead to the deviation of the forecasting results. In order to solve these problems, random forest algorithm was introduced to the field of dam deformation monitoring for the first time. Similarity matrix of random forest was applied to divide dam deformation monitoring points into several parts. Random forests prediction model was established for each part, which will avoid the defects of traditional models such as modeling of single point or using the same model for all deformation monitoring points. Establishing forecasting model for different parts of dam was more in line with engineering practice. Deformation data of one arch dam was analyzed and the feasibility of random forest model was verified. The results showed that partition of dam deformation points based on similarity matrix of random forest conformed to the physical and engineering practical significance. Compared with support vector machine and BP neural network model, the prediction model of random forests for each part had the higher prediction precision and stability, which provided a new approach in the area of dam safety monitoring.
Keywords:
Project Supported:
Clc Number:
Mobile website
® 2024 All Rights Reserved
冀公网安备13010502002656号
冀公网安备13010502002656号