Mapping of Earthquake-Induced Land Deformation on Urban Area Using Interferometric Synthetic Aperture Radar Data of Sentinel-1

Usman, Fathoni; Syamsir, Agusril; Melasari, Jihan

doi:10.1007/978-981-16-4617-1_39

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The ground surface deformation (GSD) naturally happens due to an isostatic load of Holocene deposits from sediments and natural compactions or as a consequence of tectonic and volcanic activities (Hamim et al, 2019) . A study on determining the GSD based on radar data of Sentinel 1 in the Padang City area has been conducted (Usman et al, 2022) . The vulnerability of buildings based on GSD has been determined by using the unsupervised K-Mean cluster analysis to classify the GSD in the Kota Padang Regency (Usman, 2021) .…”

Section: Introductionmentioning

confidence: 99%

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

Usman

Nanda

Sumantyo

2022

Sci. Technol. Indones

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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.

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Section: Introductionmentioning

confidence: 99%

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

Usman

Nanda

Sumantyo

2022

Sci. Technol. Indones

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Comparative analysis on digital surface model of urban area from Sentinel-1 SAR interferometry and aerial photogrammetry for disaster mitigation plan

et al. 2021

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This paper presents an effort to evaluate the generated digital elevation model (DEM) from an active sensor onboard satellite of Sentinel-1A and from aerial photos taken using an unmanned aerial vehicle (UAV). The objective is to compare the quality of generated DEM and review the processes for disaster mitigation and prevention plans application. The radar data acquisition used in this study is pair of SLC-type radar data. The interferogram is processed from the coherence and the phase of complex data of the pair radar imageries. Meanwhile, aerial photography was taken within the smaller urban area in Padang City. The photogrammetry process to generate the DEM was conducted using the structure from motion (SfM) technique. The quality and procedures are reviewed by comparing the DEM products with other publicly available DEM data from DEMNAS, SRTM, and AW3D. This study found that generating the DEM from Sentinel-1 interferometry SAR is a challenging process. The product is unmatched and has lower quality compared to available DEM data due to several identified factors. In contrast, high computational cost photogrammetry produced good quality DEM if sufficient ground control points (GCP) were set.

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Mapping of Earthquake-Induced Land Deformation on Urban Area Using Interferometric Synthetic Aperture Radar Data of Sentinel-1

Cited by 2 publications

References 11 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

Comparative analysis on digital surface model of urban area from Sentinel-1 SAR interferometry and aerial photogrammetry for disaster mitigation plan

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