Gravity Dam Deformation Prediction Model Based on I-KShape and ZOA-BiLSTM

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...

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Bibliographic Details
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:
Deformation prediction
spatial clustering
bidirectional long-short term memory (BiLSTM)
zebra optimization algorithm (ZOA)
Online Access:https://ieeexplore.ieee.org/document/10485413/

Internet

https://ieeexplore.ieee.org/document/10485413/

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