Multi-expert attention network for long-term dam displacement prediction.

Monitoring and predicting the dam displacement of concrete dams has attracted increasing attention for ensuring the long-term health conditions. Most existing models focus on just temporal features and ignore the spatial features relations of monitoring data. To address these problems, a dam displacement prediction model based on multi-expert network is developed. In the proposed model, the long short-term memory network is employed to extract the temporal features of each monitoring sensor. Then the multi-head attention network is employed to obtain the spatial features and the adjacency values. The multi-expert graph is employed to describe the spatial relations between different monitoring sensors. The graph convolutional network is employed to integrate the spatio-temporal features and predict the long-term dam displacement. Through a real-world comparative study against eight prediction models, the proposed model performs better than other models in both cyclical and non-cyclical time series. Therefore, the proposed model is suitable for evaluating the dam health condition. [ABSTRACT FROM AUTHOR]