SPATIAL INSIGHTS INTO EARTHQUAKE STRENGTH: A SULAWESI CASE STUDY USING ORDINARY AND ROBUST KRIGING METHODS

  • Nanda Lailatul Humairah Statistics Department, Faculty of Mathematics and Natural Sciences Universitas Islam Indonesia, Indonesia https://orcid.org/0000-0002-5560-7788
  • Achmad Fauzan Statistics Department, Faculty of Mathematics and Natural Sciences Universitas Islam Indonesia, Indonesia https://orcid.org/0000-0002-0533-5518
Keywords: Interpolation, Ordinary Kriging, Robust Kriging, Earthquake

Abstract

The data from the Meteorology, Climatology and Geophysics Agency (BMKG) in the last 22 years shows that there have been 230 destructive earthquakes in Indonesia with the highest incidence in 2021. One of the islands frequently hit by earthquakes is Sulawesi Island. According to the 2020 Disaster Risk Index Book (IRBI), 63 of the 81 regencies/cities on Sulawesi Island have a high category earthquake risk index. Based on this, information is needed as a first step in disaster mitigation so that the government can take preventive and anticipatory actions to reduce risks associated with earthquakes and ensure the safety of people on the island of Sulawesi, one of which is obtained through spatial interpolation. In this study, the Kriging methods of interpolation, Ordinary Kriging (OK) and Robust Kriging (RK) were used. From the analysis with OK and RK, the best theoretical semivariogram model is the Exponential model with nugget, sill and range values of ​​respectively 0.40, 0.70, and 6.50 for OK and 0.35, 0.90 and 9.50 for RK. Both methods produced the results that most areas of Sulawesi Island have the potential for shallow earthquakes with a magnitude of around 3.2 to 4.0 on the Richter scale. The potential for earthquakes with high strength is more common around the seas to the east and north of Central Sulawesi Province. The highest estimation results are at the coordinates of 120,029° East Longitude, 1.159° North Latitude, namely in the sea north of South Dampal. According to the results of K-Fold Cross Validation and Leave One Out Cross Validation, the more accurate method for estimating earthquake strength on Sulawesi Island is the RK method because the RMSE and MAPE values ​​in the RK method are smaller than the OK method.

Downloads

Download data is not yet available.

References

M. Aleksandrova et al., The World Risk Index 2021. Bündnis Entwicklung Hilft, 2021.

BNPB, “Catatan Refleksi Akhir Tahun Penanggulangan Bencana 2021,” https://bnpb.go.id/berita/catatan-refleksi-akhir-tahun-penanggulangan-bencana-2021.

A. Sompotan, Struktur Geologi Sulawesi. Bandung: Institut Teknologi Bandung, 2012.

E. Krause and K. Krivoruchko, “Concepts and applications of Kriging,” 2022.

I. Al Aswant, “Analisis perbandingan metode interpolasi untuk pemetaan ph air pada sumur bor di kabupaten Aceh besar berbasis SIG,” Final Task, Universitas Syiah Kuala, Aceh, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:127975385

Suprajitno, Pengantar Geostatistik. Jakarta: Universitas Indonesia, 2005.

Darmanto and Soepraptini, Robust Kriging Untuk Interpolasi Spasial Pada Data Spasial Berpencilan (Outlier). Malang: Universitas Brawijaya, 2009.

P. Zhou, J. Chen, and S. Wang, “A Dual Robust Strategy for Removing Outliers in Multi-Beam Sounding to Improve Seabed Terrain Quality Estimation,” Sensors, vol. 24, no. 5, p. 1476, Feb. 2024, doi: 10.3390/s24051476.

M. Qu, J. Chen, B. Huang, and Y. Zhao, “Enhancing apportionment of the point and diffuse sources of soil heavy metals using robust geostatistics and robust spatial receptor model with categorical soil-type data,” Environmental Pollution, vol. 265, p. 114964, Oct. 2020, doi: 10.1016/j.envpol.2020.114964.

M. I. Syukur, “Penerapan metode robust kriging pada data curah hujan wilayah sulawesi selatan untuk mengestimasi adanya outlier yang disebabkan oleh data hilang,” Final Task, Universitas Hasanuddin, Makassar, 2023.

A. D. R. Bahtiyar, A. Hoyyi, and H. Yasin, “Ordinary Kriging dalam estimasi curah hujan di kota Semarang,” Jurnal Gaussian, vol. 3, no. 2, pp. 151–159, 2014, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/gaussian

A. N. Alfiana, “Metode Ordinary Kriging pada Geostatistika,” Universitas Negeri Yogyakarta, Yogyakarta, 2010.

ESRI, “ArcGIS 9.2 Desktop Help: Binning the Empirical Semivariogram,” http://webhelp.esri.com/arcgisdesktop/ 9.2/index.cfm?TopicName=Binning_the_empirical_semivariogram.

N. A. C. Cressie, Statistics for Spatial Data, Revised edition. 1990.

L. Wang, “Spatial Interpolation,” http://www.fresnostate.edu/csm/ees/documents/facstaff/wang/gis200/lecture-notes/gis/chap15.pdf.

H. Mohebzadeh, “Comparison of methods for fitting the theoretical variogram to the experimental variogram for estimation of depth to groundwater and its temporal and spatial variations,” American-Eurasian Journal of Agricultural & Environmental Sciences (AEJAES), vol. 18, no. 2, pp. 64–76, 2018, doi: 10.5829/idosi.aejaes.2018.64.76.

I. Y. H. Fikliani, “Estimasi luasan serangan penyakit bulai pada tanaman jagung di kabupaten jombang dengan metode robust kriging,” Final Task, Institut Teknologi Sepuluh Nopember, Surabaya, 2016.

M. Mälicke, S. K. Hassler, M. Weiler, T. Blume, and E. Zehe, “Exploring hydrological similarity during soil moisture recession periods using time dependent variograms,” Hydrology and Earth System Sciences Discussions, vol. 2018, pp. 1–25, 2018, doi: 10.5194/hess-2018-396.

G. Babish, Geostatistics Without Tears: A Practical Guide to Geostatistics, Variograms and Kriging. 2000.

M. C. Safira, A. Fauzan, and M. A. S. Adhiwibawa, “Interpolasi polutan Nitrogen Dioksida (NO2) di kota Yogyakarta dengan pendekatan Ordinary Kriging dan Inverse Distance Weighted,” Jurnal Aplikasi Statistika & Komputasi Statistik, vol. 14, no. 2, pp. 55–66, 2022.

A. Rahmasari and Noeryanti, “Prediksi data spasial yang tidak tersampel dan mengandung pencilan menggunakan metode Robust Kriging (studi kasus: kualitas udara NO2 pemukiman di kota Yogyakarta),” Jurnal Statistika Industri dan Komputasi, vol. 06, no. 02, pp. 132–140, 2021.

G. Hatfield, Spatial statistics. In Practical Mathematics for Precision Farming. USA: Dakota State University, 2018.

A. S. Wahyudi, Sugito, and D. Ispriyanti, “Metode robust kriging untuk mengestimasi data spasial berpencilan (studi kasus: pencemaran udara gas NO2 di kota Semarang),” Jurnal Gaussian, vol. 5, no. 3, pp. 321–330, 2016, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/gaussian

D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 2019, pp. 542–545. doi: 10.1016/B978-0-12-809633-8.20349-X.

F. Ratnawati, “Implementasi algoritma Naive Bayes terhadap analisis sentimen opini film pada Twitter,” INOVTEK Polbeng - Seri Informatika, vol. 3, no. 1, p. 50, Jun. 2018, doi: 10.35314/isi.v3i1.335.

S. Hulu, “Analisis kinerja metode cross validation dan K-Nearest Neighbor dalam klasifikasi data,” Master Thesis, Universitas Sumatera Utara, Sumatera Utara, 2020.

A. Wibowo, “10 Fold-Cross Validation,” https://mti.binus.ac.id/: https://mti.binus.ac.id/2017/11/24/10-fold-cross-validation/.

P. Sokkhey and T. Okazaki, “Hybrid Machine Learning Algorithms for Predicting Academic Performance,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, 2020, doi: 10.14569/IJACSA.2020.0110104.

Geeksforgeeks, “LOOCV (Leave One Out Cross-Validation) in R Programming,” https://www.geeksforgeeks.org/loocvleave-one-out-cross-validation-in-r-programming/#:~:text=LOOCV(Leave%20One%20Out%20Cross%2DValidation)%20is%20a%20type,considered%20as%20the%20training%20set.

Z. Shao and M. J. Er, “Efficient Leave-One-Out Cross-Validation-based regularized extreme learning machine,” Neurocomputing, vol. 194, pp. 260–270, Jun. 2016, doi: 10.1016/j.neucom.2016.02.058.

G.-W. Cha et al., “Development of a prediction model for demolition waste generation using a random forest algorithm based on small datasets,” Int J Environ Res Public Health, vol. 17, no. 19, p. 6997, Sep. 2020, doi: 10.3390/ijerph17196997.

L. Sunarmintyastuti, S. Alfarisi, and F. S. Hasanusi, “Peramalan penentuan jumlah permintaan konsumen berbasis teknologi informasi terhadap produk bordir pada kota Tasikmalaya,” Jurnal Penelitian Pendidikan, vol. 16, no. 3, pp. 288–296, Jan. 2017, doi: 10.17509/jpp.v16i3.4824.

E. Woschnagg and J. Cipan, Evaluating Forecast Accuracy. University of Vienna, 2004.

Published
2024-05-25
How to Cite
[1]
N. Humairah and A. Fauzan, “SPATIAL INSIGHTS INTO EARTHQUAKE STRENGTH: A SULAWESI CASE STUDY USING ORDINARY AND ROBUST KRIGING METHODS”, BAREKENG: J. Math. & App., vol. 18, no. 2, pp. 1283-1296, May 2024.