Work

A Dam Displacement Prediction Method Based on a Model Combining Random Forest, a Convolutional Neural Network, and a Residual Attention Informer

Year: 2024

Type: article

Abstract: To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, RF is utilized to assess the importance of input features, selecting key factors that significantly influence dam displacement. Then, CNN is employed to perform deep feature extraction on the input data, mining effective information. Subsequently, the Informer model integrated with a residual attention mechanism establishes the mapping relationship between the extracted features and dam displacement, enhancing the focus on critical features. Finally, the Northern Goshawk Optimization (NGO) algorithm is adopted to optimize the model’s hyperparameters. Experimental results on actual engineering data demonstrate that the proposed model exhibits superior prediction accuracy and stability compared to other typical models, offering higher precision and reliability.

Source: Water

DOI: 10.3390/w16243687

Authors: Chunhui Fang, Ying Jiao, Xue Wang, Taiqi Lu, Hao Gu

Institutions: Zhejiang University of Water Resource and Electric Power, Zhejiang Tongji Vocational College of Science and Technology, Hohai University

Language: English

Topic: Dam Engineering and Safety

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955