Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM
This research proposes a dam deformation prediction model based on clustering partitioning and Bidirectional Long Short-Term Memory (BiLSTM) networks to address the limitations of traditional monitoring models in characterizing the distribution characteristics of deformation zones in concrete gravit...
Main Authors: | Madiniyeti Jiedeerbieke, Tongchun Li, Yang Chao, Huijun Qi, Chaoning Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10485413/ |
Similar Items
-
Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
by: Antreas Pogiatzis, et al.
Published: (2020-12-01) -
A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM
by: Zihui Huang, et al.
Published: (2024-01-01) -
Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments
by: Yen-Hao Hsieh, et al.
Published: (2022-07-01) -
Forecasting PM2.5 Concentration Using a Single-Dense Layer BiLSTM Method
by: Aji Teguh Prihatno, et al.
Published: (2021-07-01) -
Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM
by: Yuezhou Wu, et al.
Published: (2023-09-01)