Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 26, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Multi-Modal Data-Driven Bayesian-Optimized CNN-LSTM Model for Slope Displacement Prediction.

Xingwang Zhao1,2,3, Xinlong Wan1,3, Jian Chen1,3

  • 1Key Laboratory of Aviation-Aerospace-Ground Cooperative Monitoring and Early Warning of Coal Mining-Induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, Huainan 232001, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Robust Adaptive Filtering Framework for Smartphone GNSS/PDR-Integrated Positioning.

Micromachines·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

A new Bayesian-optimized Convolutional Neural Network and Long Short-Term Memory (Bayes-CNN-LSTM) model improves slope displacement prediction accuracy. This advanced model enhances geological hazard early warning systems for disaster prevention.

Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence
  • Disaster Prevention

Background:

  • Accurate slope displacement prediction is crucial for geological hazard early warning systems.
  • Nonlinearity and time-varying characteristics of slope displacement challenge prediction accuracy.

Purpose of the Study:

  • To develop an advanced model for improving slope displacement prediction accuracy.
  • To enhance the reliability of geological hazard early warning systems.

Main Methods:

  • A multi-modal data-driven Bayesian-optimized Convolutional Neural Network and Long Short-Term Memory (Bayes-CNN-LSTM) model was constructed.
  • The model's performance was evaluated using multi-modal monitoring data from the GuShan mine slope.
  • Comparative analysis was performed against various established models (CNN-LSTM, LSTM, CNN, SVM, TCN, Transformer).
Keywords:
Bayesian optimization algorithmCNNLSTMmulti-modal dataslope displacement

Related Experiment Videos

Last Updated: May 26, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Main Results:

  • The Bayes-CNN-LSTM model achieved high accuracy with R2 of 0.971, MAE of 0.444 mm, and RMSE of 0.618 mm.
  • The model demonstrated significant reductions in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to other models.
  • Integration of multi-modal data, including rainfall and earth pressure, improved extrapolation prediction accuracy by 30.2% (MAE) and 24.6% (RMSE) for 24-h forecasts.

Conclusions:

  • The Bayes-CNN-LSTM model significantly enhances slope displacement prediction accuracy.
  • The model improves the practicality and effectiveness of slope safety monitoring systems.
  • This approach offers a valuable reference for advancing slope safety monitoring and disaster risk reduction.