Identification of Minimum Standards in Emergency Goods for Earthquake Relief in Indonesia

Oktarina, Rienna; Bahagia, Senator Nur; Diawati, Lucia; Pribadi, Krishna S.

doi:10.20965/jdr.2016.p0545

Cited by 2 publications

(1 citation statement)

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“…Thirty years of data on the earthquakes' location, depth, and magnitude were used to predict earthquake events in Indonesia and compare three di erent machine learning algorithms (Murwantara et al, 2020) . Utilizing 123 datasets with eight variables, Arti cial Neural Network (ANN) was used to predict the number of damaged buildings and casualties due to earthquakes in Indonesia (Oktarina et al, 2020) .…”

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%