GEOLOGICAL DISASTER PREDICTION
Deformation Prediction of Landslide-Induced Highway Instability Using D-InSAR and CNN-LSTM: A Case Study in Zhouqu County, China
Geological disasters pose significant risks to highway infrastructure. This paper presents an enhanced predictive approach that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) data with a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning model.
Executive Impact: Enhanced Infrastructure Resilience
This study demonstrates how advanced D-InSAR and CNN-LSTM integration can significantly improve the accuracy and reliability of landslide prediction, crucial for protecting critical transportation infrastructure.
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Reduced Prediction Error (RMSE)
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Mean Absolute Error (MAE)
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Prediction Model Accuracy (R²)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Performance
Case Study
Accuracy Highlight
D-InSAR Processing Workflow
Differential Interferometric Synthetic Aperture Radar (D-InSAR) is a powerful remote sensing technique used for monitoring ground surface deformation. It leverages SAR images acquired at different times to detect subtle ground movements with high precision. This module illustrates the sequential steps involved in extracting ground displacement from raw satellite data.
Enterprise Process Flow
Coarse Coregistration
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Fine Coregistration
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Interferometric Phase Creation
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Simulated Interferometric Phase Creation
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Flat Earth Phase Elimination
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Differential Interferogram
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Phase Unwrapping
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Conversion from Phase to Vertical Displacement
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Geocoding
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Vertical Displacement Map
Predictive Model Performance Comparison
To validate the efficacy of the proposed CNN-LSTM model, its performance was rigorously compared against traditional time series analysis models such as ARIMA, Support Vector Regression (SVR), and a standalone LSTM model. The results, evaluated using RMSE, MAE, and R², clearly demonstrate the superior predictive capabilities of the integrated CNN-LSTM approach.
| Model | RMSE (cm) | MAE (cm) | R² |
|---|---|---|---|
| ARIMA | 1.82 | 1.39 | 0.78 |
| SVR | 1.74 | 1.21 | 0.81 |
| LSTM | 1.33 | 1.02 | 0.87 |
| CNN-LSTM | 1.01 | 0.74 | 0.92 |
Yahuokou Landslide: Real-world Impact & Mitigation
The Yahuokou landslide in Xiazhuang Village, Zhouqu County, Gansu Province, highlights the critical need for advanced monitoring and prediction systems. Initiating slow sliding on July 16, 2019, the landslide body near the Lianghekou section of the Minjiang River progressed significantly, posing immense risks to local roads and the G75 Expressway. This study leveraged D-InSAR and CNN-LSTM to provide vital deformation data and enhance road safety.
Yahuokou Landslide Monitoring & Prediction
On July 19, 2019, a major landslide occurred near Xiazhuang Village, Dongshan Township, Zhouqu County, Gansu Province, severely impacting local roads and posing risks to the G75 Expressway. The landslide, estimated at 4 million cubic meters, moved 20 meters by July 20. Field investigations confirmed significant deformation, validating D-InSAR and CNN-LSTM monitoring results. The proposed model provided an early warning 26 hours in advance, demonstrating its effectiveness in disaster mitigation and protecting lives and property. This case underscores the importance of integrating satellite-based monitoring with intelligent predictive algorithms for infrastructure resilience.
CNN-LSTM Model Superiority
The Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model combines the strengths of CNNs in feature extraction with LSTMs in capturing temporal dependencies. This synergistic approach enables the model to effectively process D-InSAR time series data and achieve exceptional prediction accuracy for ground deformation.
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R² Prediction Accuracy (CNN-LSTM)
The CNN-LSTM model's R² value of 0.92 signifies that 92% of the variance in ground deformation can be predicted by the model, demonstrating its robust capability in capturing complex patterns and providing reliable forecasts. This level of accuracy is crucial for effective early warning systems and proactive infrastructure management.
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