Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
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Keywords:
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Machine Learning, Ground Surface Deformation, Vulnerability, Earthquake
Abstract
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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Usman, F., A. Syamsir, and J. Melasari (2022). Mapping of Earthquake-Induced Land Deformation on Urban Area Using Interferometric Synthetic Aperture Radar Data of Sentinel-1. Recent Advances in Earthquake Engineering. Springer; 491–502
Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. DesRoches (2020). The Promise of Implementing Machine Learning in Earthquake Engineering: a State of the Art Review. Earthquake Spectra, 36(4); 1769–1801
Xiong, P., L. Tong, K. Zhang, X. Shen, R. Battiston, D. Ouzounov, R. Iuppa, D. Crookes, C. Long, and H. Zhou (2021). Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data. Science of the Total Environment, 771; 145256
Zhou, F., R. Li, G. Trajcevski, and K. Zhang (2021). Land Deformation Prediction via Slope Aware Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35; 15033–15040
Azarakhsh, Z., M. Azadbakht, and A. Matkan (2022). Estimation, Modeling, and Prediction of Land Subsidence Using Sentinel-1 Time Series in Tehran Shahriar Plain: A Machine Learning Based Investigation. Remote Sensing Applications: Society and Environment, 25; 100691
Badan Informasi Geospasial (2018). DEMNAS Seamless Digital Elevation Model (DEM) dan Batimetri Nasional. Badan Informasi Geospasial
Bothara, J., D. Beetham, D. Brunsdon, M. Stannard, R. Brown, C. Hyland, W. Lewis, S. Miller, R. Sanders, and Y. Sulistio (2010). General Observations of Effects of the 30th September 2009 Padang earthquake, Indonesia. Bulletin of the New Zealand Society for Earthquake Engineering, 43(3); 143
BPS-Statistics of Padang Municipality (2022). Padang Municipality in Figures. Padang, Indonesia: BPS-Statistics of Padang Municipality
Chian, S., S. Wilkinson, J. Whittle, R. Mulyani, J. Alarcon, A. Pomonis, K. Saito, S. Fraser, K. Goda, and J. Macabuag (2019). Lessons Learnt From the 2009 Padang Indonesia, 2011 Tohoku Japan and 2016 Muisne Ecuador Earthquakes. Frontiers in Built Environment, 5; 73
Demšar, J., T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič (2013). Orange: Data Mining Toolbox in Python. The Journal of Machine Learning Research, 14(1); 2349–2353
ESA (2022). Contains Modied Copernicus Sentinel Data [2020-2022]. ESA
Geiß, C., P. A. Pelizari, M. Marconcini, W. Sengara, M. Edwards, T. Lakes, and H. Taubenböck (2015). Estimation of Seismic Building Structural Types Using Multisensor Remote Sensing and Machine Learning Techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 104; 175–188
Guo, Y., S. Hu, W. Wu, Y. Wang, and J. Senthilnath (2020). Multitemporal Time Series Analysis Using Machine Learning Models for Ground Deformation in the Erhai Region, China. Environmental Monitoring and Assessment, 192(7); 1–6
Hakam, A., D. Febriansyah, and B. M. Adji (2020). Liquefaction Mapping Procedure Development: Density and Mean Grain Size Formulations. Geomate Journal, 18(70); 155–161
Hamim, S. A., F. Usman, et al. (2019). Determination of Land Subsidence Caused by Land Use Changing in Palembang City using Remote Sensing Data. Third International Conference on Sustainable Innovation 2019 Technology and Engineering (IcoSITE 2019). Atlantis Press; 101–106
Hanoon, M. S., A. N. Ahmed, N. Zaini, A. Razzaq, P. Kumar, M. Sherif, A. Sefelnasr, and A. El-Shafie (2021). Developing Machine Learning Algorithms for Meteorological Temperature and Humidity Forecasting at Terengganu State in Malaysia. Scientific Reports, 11(1); 1–19
Harirchian, E., V. Kumari, K. Jadhav, S. Rasulzade, T. Lahmer, and R. Raj Das (2021). A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to Rc Buildings. Applied Sciences, 11(16); 7540
Jiao, P. and A. H. Alavi (2020). Articial Intelligence in Seismology: Advent, Performance and Future Trends. Geoscience Frontiers, 11(3); 739–744
Mangalathu, S., H. Sun, C. C. Nweke, Z. Yi, and H. V. Burton (2020). Classifying Earthquake Damage to Buildings Using Machine Learning. Earthquake Spectra, 36(1); 183–208
Mattioli, G. S., R. A. Herd, M. H. Strutt, G. Ryan, C. Widiwijayanti, and B. Voight (2010). Long Term Surface Deformation of Soufrière Hills Volcano, Montserrat from Gps Geodesy: Inferences From Simple Elastic Inverse Models. Geophysical Research Letters, 37(19)
Meng, Z., C. Shu, Y. Yang, C. Wu, X. Dong, D. Wang, and Y. Zhang (2022). Time Series Surface Deformation of Changbaishan Volcano Based on Sentinel-1B SAR Data and its Geological Significance. Remote Sensing, 14(5); 1213
Milczarek, W., A. Kopeć, D. Głąbicki, and N. Bugajska (2021). Induced Seismic Events Distribution of Ground Surface Displacements Based on Insar Methods And Mogi and Yang Models. Remote Sensing, 13(8); 1451
Muin, B. and H. Nawir (2011). Geotechnical Aspects Of the Sumatra Earthquake. Soils and Foundations, 51(2); 333-341
Murwantara, I. M., P. Yugopuspito, and R. Hermawan (2020). Comparison of Machine Learning Performance for Earthquake Prediction in Indonesia Using 30 Years Historical Data. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3); 1331–1342
Naghibi, S. A., B. Khodaei, and H. Hashemi (2022). An Integrated InSAR Machine Learning Approach for Ground Deformation Rate Modeling in Arid Areas. Journal of Hydrology, 608; 127627
Oktarina, R., N. Bahagia, L. Diawati, and K. S. Pribadi (2020). Artificial Neural Network for Predicting Earthquake Casualties and Damages in Indonesia. IOP Conference Series: Earth and Environmental Science, 426; 012156
Qiu, J. and X. Qiao (2017). A Study on the Seismogenic Structure of the 2016 Zaduo, Qinghai Ms6. 2 Earthquake using InSAR Technology. Geodesy and Geodynamics, 8(5); 342–346
Rahardjo, P. P., A. S. Lestari, B. Wijaya, A. Lim, S. Herina, S. Rustiani, S. Wiguna, and V. Hadsari (2014). Kajian Geoteknik untuk Infrastruktur Kota Padang Menghadapi Ancaman Gempa dan Tsunami. Research Report Engineering Science (in Indonesia)
Sari, D. P., D. Rosadi, A. R. Effendie, and D. Danardono (2019). K-means and Bayesian Networks to Determine Building Damage Levels. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(2); 719–727
Sari, D. P., D. Rosadi, A. R. Effendie, and D. Danardono (2021). Discretization Methods for Bayesian Networks in The Case of the Earthquake. Bulletin of Electrical Engineering and Informatics, 10(1); 299–307
Tapir, S., J. Yatim, and F. Usman (2005). Evaluation of Building Performance Using Artificial Neural Network: Study on Service Life Planning in Achieving Sustainability. The Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, Rome, Italy, 30
Triyoso, W., A. Suwondo, T. Yudistira, and D. P. Sahara (2020). Seismic Hazard Function (SHF) Study of Coastal Sources of Sumatra Island: SHF Evaluation of Padang and Bengkulu Cities. Geoscience Letters, 7(1); 1–7
USGS (2022). Search Earthquake Catalog. Retrieved 1st September, 2021, from Earthquake Catalog
Usman, F. (2021). Surface Deformation of Padang City Area Induced by Over M w 5.0 Earthquake Events. 2021 International Conference on Computer Science and Engineering (IC2SE), 1; 1–7
Usman, F., A. Syamsir, and J. Melasari (2022). Mapping of Earthquake-Induced Land Deformation on Urban Area Using Interferometric Synthetic Aperture Radar Data of Sentinel-1. Recent Advances in Earthquake Engineering. Springer; 491–502
Xie, Y., M. Ebad Sichani, J. E. Padgett, and R. DesRoches (2020). The Promise of Implementing Machine Learning in Earthquake Engineering: a State of the Art Review. Earthquake Spectra, 36(4); 1769–1801
Xiong, P., L. Tong, K. Zhang, X. Shen, R. Battiston, D. Ouzounov, R. Iuppa, D. Crookes, C. Long, and H. Zhou (2021). Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data. Science of the Total Environment, 771; 145256
Zhou, F., R. Li, G. Trajcevski, and K. Zhang (2021). Land Deformation Prediction via Slope Aware Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35; 15033–15040
Authors
Usman, F., Nanda, & Sumantyo, J. T. S. (2022). Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning. Science and Technology Indonesia, 7(4), 435–442. https://doi.org/10.26554/sti.2022.7.4.435-442
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