Main Article Content
Abstract
Java, situated in the Pacific Ring of Fire, is one of the most seismically active regions in the world, with frequent earthquakes posing significant risks to its dense population and critical infrastructure. This study aimed to analyze the spatial distribution and intensity patterns of earthquakes in Java from 2022 to 2024 using data from the Meteorology, Climatology, and Geophysics Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG). Spatial interpolation techniques—inverse distance weighted (IDW), nearest neighbor, and Thiessen polygon—were applied to evaluate their effectiveness in mapping earthquake intensity patterns. The dataset included the earthquake magnitude, location, and occurrence time, with performance evaluated using mean absolute percentage error (MAPE) and mean absolute error (MAE). Results showed that the nearest neighbor method achieved the highest accuracy (MAPE of 12.27%, MAE of 0.37), followed by IDW, while the Thiessen polygon method demonstrated limited suitability for continuous seismic phenomena. These findings underscore the importance of selecting appropriate interpolation methods for seismic risk mapping, providing actionable insights for disaster preparedness and urban planning in Java.
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References
M. Yang, S. Cui, and T. Jiang, “Global research trends in seismic landslide: A bibliometric analysis,” Earthq. Res. Adv., vol. 5, no. 1, Jan. 2024, Art. no 100329, doi: 10.5066/P9RG3MBE.
A. Zhang, X. Wang, W. Pedrycz, Q. Yang, X. Wang, and H. Guo, “Near real-time spatial prediction of earthquake-triggered landslides based on global inventories from 2008 to 2022,” Soil Dynamics Earthq. Eng., vol. 185, Oct. 2024, Art. no 108890, doi: 10.1016/j.soildyn.2024.108890.
F. Xie, Z. Wang, D. Zhao, R. Gao, and X. Chen, “Seismic imaging of the Java subduction zone: New insight into arc volcanism and seismogenesis,” Tectonophysics, vol. 854, May 2023, Art. no 229810, doi: 10.1016/j.tecto.2023.229810.
M. Megawati, K.-F. Ma, P.-F. Chen, D. Sianipar, and M.-C. Hsieh, “Source characterization of intermediate-depth earthquakes in southern Java, Indonesia,” J. Asian Earth Sci., vol. 264, Apr. 2024, Art. no 106040, doi: 10.1016/j.jseaes.2024.106040.
S.H. Alavi, A. Bahrami, M. Mashayekhi, and M. Zolfaghari, “Optimizing interpolation methods and point distances for accurate earthquake hazard mapping,” Buildings, vol. 14, no. 6, 2024, Art. no 1823, doi: 10.3390/buildings14061823.
F. Amalia and A. Fauzan, “Estimating earthquake magnitude using spatial interpolation with the inverse distance weighting and ordinary kriging approach,” J. Barekeng, vol. 10, no. 4, pp. 335–350, Apr. 2025, doi: 10.30598/barekengvol19iss2pp937-948.
L. Lombardo, H. Bakka, H. Tanyas, C. van Westen, P.M. Mai, and R. Huser, “Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides,” 2018, arXiv:1807.08513.
T. Tuncay, İ. Bayramin, A.E. Tercan, and I. Ünver, “Assessment of inverse distance weighting (IDW) interpolation on spatial variability of selected soil properties in the Cukurova Plain,” J. Agr. Sci-Tarim. Bili., vol. 22, no. 3, pp. 377–384, 2016, doi: 10.1501/Tarimbil_0000001396.
M. Irsyam and S. Hidayati, “Proposed seismic hazard maps of Sumatra and Java Islands and microzonation study for Jakarta City,” J. Earth Syst. Sci, vol. 117, no. S2, pp. 865–878, Nov. 2008.
G. Pellicone, T. Caloiero, G. Modica, and I. Guagliardi, “Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy),” Int. J. Climatol., vol. 38, no. 9, pp. 3651–3666, Jul. 2018, doi: 10.1002/joc.5525.
A. Crespi, C. Lussana, M. Brunetti, A. Dobler, M. Maugeri, and O. E. Tveito, "High-resolution monthly precipitation climatologies over Norway: assessment of spatial interpolation methods," 2018, arXiv:1804.04867.
J.P. Wilson and J.C. Gallant, Terrain Analysis: Principles and Applications. New York, NY, USA: Wiley, 2000.
C.D. Lewis, Industrial and Business Forecasting Methods. Oxford, United Kingdom: Butterworth-Heinemann, 1982.
R.J. Hyndman and A.B. Koehler, “Another look at measures of forecast accuracy,” Int. J. Forecast., vol. 22, no. 4, pp. 679–688, Oct.–Dec. 2006, doi: 10.1016/j.ijforecast.2006.03.001.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “Statistical and machine learning forecasting methods: concerns and ways forward,” PLoS One, vol. 13, no. 3, 2018, Art. no e0194889, doi: 10.1371/journal.pone.0194889.
J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Res., vol. 30, no. 1, pp. 79–82, Dec. 2005, doi: 10.3354/cr030079.