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Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring

With the gradual transformation of hydraulic engineering from digitization and intelligence to WISDOM, remote sensing technology, artificial intelligence and deep learning methods have been widely used for automatic perception, processing, storage and analysis of hydraulic structure engineering moni...

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Format: Online
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2026
Subjects:
Cofferdam
Double-row sheet pile
Soft-ground foundation
Structural stability
Finite element method
Residual neural network
Knowledge distillation
Transfer learning
Concrete dam
Crack detection
Deformation prediction model
Sluice
Long short-term memory
Seagull optimization algorithm
Weighted Markov model
UAV
Ice jam
Frazil ice floe
Remote sensing
Frazil ice jam
Teledetection
Ice morphometry
Data processing
High concrete dam
In situ observation data
Temperature field
Temperature control index
Information entropy
Cloud model
Dam safety
Sluice seepage
Prediction model
MHHO
BiLSTM
Dam monitoring data
Gross errors
Environmental change
FCM algorithm
OPTICS algorithm
LOF algorithm
Streamflow forecast
Machine learning
Statistical techniques
Göksu Stream
Nonuniform deformation
Random coefficient model
Individual effect values
Maximum entropy principle
Swin Transformer
Vision Transformer
Feature fusion
Hydraulic structure
Soft foundation
Integrated numerical analysis
Time-dependent behavior
Global sensitivity analysis
BP neural network
Pumped storage power station
Core wall rockfill dam
Phreatic line
Vertical earth pressure
Deformation
Settlement prediction model
Concrete face rockfill dam
Variational mode decomposition
Harris hawks optimization
Support vector regression
Factor mining
Coal mine
Underground reservoir
Coal pillar dam
Artificial dam
Stability
Structure safety monitoring
Deformation prediction
Artificial intelligence
Feature engineering
Stacking method
Performance indicators
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A
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Online Access:ONIX_20260416T142754_9783725854455_18
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