Land Deformation Prediction via Slope-Aware Graph Neural Networks

Zhang, Fan; Li, Rongfan; Trajcevski, Goce; Zhang, Kunpeng

doi:10.1609/aaai.v35i17.17764

Cited by 9 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further performance evaluation of the trained model is conducted by providing a new dataset to the trained model from the earthquake on 13 th March 2022, which is located at latitude -0.63 and longitude 98.6294 at 28 km depth, 190 km to the epicenter. Plotting the measured and predicted GSD values allows for qualitative analysis of the machine learning trained model's prediction results (Zhou et al, 2021) .…”

Section: Statistical Evaluationmentioning

confidence: 99%

Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Usman

Nanda

Sumantyo

2022

Sci. Technol. Indones

View full text Add to dashboard Cite

Earthquakes can inflict significant damage to structures and infrastructures. This paper presents a machine learning model to predict ground surface deformation (GDS) induced by earthquake events. The data on historical GSD is extracted from radar product of Synthetic Aperture Radar (SAR) data of one-year over five magnitude earthquakes that occurred within 200 kilometers of the Kota Padang Regency, West Sumatra. Building topology data of its footprint area, distance from shoreline, elevation, and coordinate were incorporated as the main features in the dataset. The earthquake parameters were taken from the USGS earthquake data catalog. Four machine learning algorithms of Neural Network (NN), Random Forest (RF), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) are applied. The GSD from the trained models is predicted and compared with the measured GSD from the SAR’s product. The performances of proposed algorithms are evaluated in terms of the statistical index. A new dataset from the earthquake event in March 2022 is used to predict the GSD and further test the performance of the trained models. Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. The kNN algorithm outperforms compared to others in delineating the GSD. The trained models gave deficient prediction performance on the new dataset with a correlation coefficient of 0.228 predicted by the RF algorithm. Additional earthquake datasets and more unique features will improve the performance of the machine learning algorithms.

show abstract

Section: Statistical Evaluationmentioning

confidence: 99%

Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Usman

Nanda

Sumantyo

2022

Sci. Technol. Indones

View full text Add to dashboard Cite

show abstract

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

Khalili,

Guerriero,

Pouralizadeh

et al. 2023

Nat Hazards

View full text Add to dashboard Cite

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm.

show abstract

Artificial intelligence applications for landslide mapping and monitoring on EO data

Catani,

Nava,

Bhuyan

2025

Earth Observation Applications to Landslide Mapping, Monitoring and Modeling

View full text Add to dashboard Cite

Land Deformation Prediction via Slope-Aware Graph Neural Networks

Cited by 9 publications

References 26 publications

Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

Artificial intelligence applications for landslide mapping and monitoring on EO data

Contact Info

Product

Resources

About