Article
Dam Deformation Interpretation and Prediction Based on a
Long Short-Term Memory Model Coupled with an
Attention Mechanism
Yan Su
1
, Kailiang Weng
1
, Chuan Lin
1,
* and Zeqin Chen
2
Citation: Su, Y.; Weng, K.; Lin, C.;
Chen, Z. Dam Deformation
Interpretation and Prediction Based
on a Long Short-Term Memory Model
Coupled with an Attention
Mechanism. Appl. Sci. 2021, 11, 6625.
https://doi.org/10.3390/app11146625
Academic Editor: Nikos D. Lagaros
Received: 9 June 2021
Accepted: 15 July 2021
Published: 19 July 2021
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4.0/).
1
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China; suyan@fzu.edu.cn (Y.S.);
wkl961029@126.com (K.W.)
2
Electric Power Research Institute of State Grid Fujian Electric Power Co. Ltd., Fuzhou 350007, China;
chzq1991@163.com
* Correspondence: linchuan@fzu.edu.cn
Abstract:
An accurate dam deformation prediction model is vital to a dam safety monitoring system,
as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms
ignore the interpretation and evaluation of variables and lack qualitative measures. This paper
proposes a data processing framework that uses a long short-term memory (LSTM) model coupled
with an attention mechanism to predict the deformation response of a dam structure. First, the
random forest (RF) model is introduced to assess the relative importance of impact factors and screen
input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN)
method is used to identify and filter the equipment based abnormal values to reduce the random
error in the measurements. Finally, the coupled model is used to focus on important factors in the
time dimension in order to obtain more accurate nonlinear prediction results. The results of the case
study show that, of all tested methods, the proposed coupled method performed best. In addition, it
was found that temperature and water level both have significant impacts on dam deformation and
can serve as reliable metrics for dam management.
Keywords:
dam deformation; attention mechanism; long short-term memory; dam safety monitor-
ing; prediction
1. Introduction
As a crucial social engineering infrastructure, dams must be operated safely to guar-
antee the needs of a steadily growing national economy are met. Unfortunately, due to
the inherent physical limitations of dam materials, dams often have unhealthy structural
responses such as dam body cracking and abnormal deformation [
1
]. In order to reduce the
probability of engineering failures, most dams are equipped with precise health monitoring
systems to evaluate their operational behavior and health through real-time measurements
of multiple structural and environmental indicators. Among the many monitoring indi-
cators, dam deformation is easy to measure and intuitively reflects the overall structural
response state [
2
]. In order to improve the effectiveness of management strategies, research
focused on accurately predicting dam deformation has increased in recent years. This area
of research commonly uses simulations, and the most commonly used forecasting models
can be categorized as mathematical statistical models or artificial intelligence models.
Hydrostatic-seasonal-time (HST) can be considered a representative flagship statis-
tical regression model, it quantitatively interprets the influencing factors behind dam
deformation based on the assumptions of mechanical theory and, then, performs a lin-
ear approximation fitting using the observed data. It was originally proposed by Willm
et al. [
3
] to forecast deformation of concrete dams and has since been widely implemented.
However, there is a strong correlation between dam water level and ambient temperature,
Appl. Sci. 2021, 11, 6625. https://doi.org/10.3390/app11146625 https://www.mdpi.com/journal/applsci
