Bayesian Spatial Modeling of Landslide Events Using Integrated Nested Laplace Approximation (INLA): A Study Case on Natural Conditions and Community Actions in East Java, Indonesia

Salman Alfarisi; Athalia Christina; Sadiyana Yaqutna Naqiya; Ro'fah Nur Rachmawati; Amir Machmud; Endah Kinarya Palupi

doi:10.58524/ijhes.v2i3.354

Authors

Salman Alfarisi Department of Mathematics, School of Mathematics and Science, Republic of Indonesia Defense University
Athalia Christina Department of Mathematics, School of Mathematics and Science, Republic of Indonesia Defense University
Sadiyana Yaqutna Naqiya Department of Mathematics, School of Mathematics and Science, Republic of Indonesia Defense University
Ro'fah Nur Rachmawati Department of Mathematics, School of Mathematics and Science, Republic of Indonesia Defense University
Amir Machmud Graduate Institute of Environmental Engineering, National Central University
Endah Kinarya Palupi Department Nanotechnology for Sustainable Energy, Graduate of Science and Technology, Kwansei Gakuin University http://orcid.org/0000-0001-7755-5424

DOI:

https://doi.org/10.58524/ijhes.v2i3.354

Keywords:

bayesian spatial modeling, integrated nested laplace approximation (INLA), landslide, natural disasters, spatial statistics

Abstract

Bayesian Spatial Modeling Using Integrated Nested Laplace Approximation (INLA) is an advanced statistical technique that can be used to model and analyze occurrences in geographic areas.Â Landslides are one of natural disasters that occur due to natural and human factors and pose a serious threat to East Java Province which has complex natural conditions. The disaster brings various losses, including economic, infrastructural, human life, and environmental. This study investigates the factors contributing to landslides across 29 districts and 9 cities in East Java, Indonesia, using spatial regression modeling by Integrated Nested Laplace Approximation (INLA).Â The factors include the number of seaside villages, the number of slope topography villages, and the area of temporarily uncultivated gardens and fields in 2021. The modeling results show that the number of seaside villages, the number of slope topography villages, and the area of fields that are temporarily uncultivated have a significant influence on the occurrence of landslides so that efforts to mitigate and prevent such disasters can be focused on the contributing factors. We conclude that the model might be able to identify potential landslide risk areas through mapping.

References

Badan Nasional Penanggulangan Bencana (BNPB). (2023). Data kejadian bencana tanah longsor di Indonesia tahun 2023.

Badan Pusat Statistik (BPS). Provinsi Jawa Timur Dalam Angka 2022. Jawa Timur: BPS Jawa Timur.

Berild, M., O. et al. (2021). Importance Sampling with the Integrated Nested Laplace Approximation. Cornell University.

Besag, J., York, J., and MolliÃ©, A. (1991). Bayesian image restoration with applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics, 43:1â€“59

Ferrier, S., & Guisan, A. (2006). Spatial modelling of biodiversity at the community level. Journal of applied ecology, 43(3), 393-404.

Fichera, A., King, R., Kath, J., Cobon, D., & Reardon-Smith, K. (2023). Spatial modelling of agro-ecologically significant grassland species using the INLA-SPDE approach. Scientific Reports, 13(1), 4972.

Gerber, F. (2013). Disease Mapping with the Besagâ€“Yorkâ€“MolliÃ© Model Applied to a Cancer and a Worm Infections Dataset. Master Thesis in Biostatistics.

Haribulan, R., Gosal, P. H., & Karongkong, H. H. (2019). Kajian Kerentanan Fisik Bencana Longsor Di Kecamatan Tomohon Utara. Spasial, 6(3), 714-724.

Isnaini, R. (2019). Analisis bencana tanah longsor di wilayah Jawa Tengah. Islamic Management and Empowerment Journal, 1(2), 144-145.

Latue, T., & Latue, P. C. (2023). Pemodelan Spasial Daerah Rawan Banjir di DAS Batu Merah Kota Ambon. Buana Jurnal Geografi, Ekologi Dan Kebencanaan, 1(1), 1-13.

Martino, S. & Riebler, A. (2019). Integrated Nested Laplace Approximations (INLA). Cornell University.

Maulina, R. F., Djuraidah, A., & Kurnia, A. (2019). Pemodelan Kemiskinan di Jawa Menggunakan Bayesian Spasial Probit Pendekatan Integrated Nested Laplace Approximation (INLA). Media Statistika, 12(2), 140-151.

MerzhÃ¤user, L., G. et al. (2023). Integrated Nested Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling. Cornell University.

Moraga, P. (2020). Geospatial Health Data modeling and Visualization with R-INLA and Shiny. CRC Press: Boca Raton.

Moraga, P., Dean, C., Inoue, J., Morawiecki, P., Noureen, S. R., & Wang, F. (2021). Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique. Spatial and Spatio-temporal Epidemiology, 39, 100440.

Morris, M. et al. (2019). Bayesian hierarchical spatial models: Implementing the Besag York MolliÃ© model in stan. National Library of Medicine.

Mulyasari, I., & S. A. (2021). Pemetaan kejadian tanah longsor di Kabupaten Jember, Jawa Timur. Jurnal Teknik Sipil Dan Lingkungan, 13(1), 1â€“10.

Naryanto, H. S., Soewandita, H., Ganesha, D., Prawiradisastra, F., & Kristijono, A. (2019). Analisis Penyebab Kejadian dan Evaluasi Bencana Tanah Longsor di Desa Banaran, Kecamatan Pulung, Kabupaten Ponorogo, Provinsi Jawa Timur Tanggal 1 April 2017. Jurnal Ilmu Lingkungan, 17(2), 272.

Poggio, L., Gimona, A., Spezia, L., & Brewer, M. J. (2016). Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLA. Geoderma, 277, 69-82.

Putri, L. A., et al. (2017). The Impact of Land Use Change on the Environment in Lumajang Regency, East Java. IOP Conference Series: Earth and Environmental Science, 101(1), 012034.

Rakuasa, H., Helwend, J. K., & Sihasale, D. A. (2022). Pemetaan Daerah Rawan Banjir di Kota Ambon Menggunakan Sistim Informasi Geografis. Jurnal Geografi: Media Informasi Pengembangan Dan Profesi Kegeografian, 19(2).

Riebler, A., SÃ¸rbye, S. H., Simpson, D., and Rue, H. (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4):1145â€“1165.

Riffenburgh, R. H. (2012). Bayesian Statistics. Statistics in Medicine (Third Edition). 355-364

Rokhman, M. F., et al. (2018). Spatial Analysis of Landslide Susceptibility in Kediri District, East Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 157(1), 012029.

Rosyid, R., et al. (2020). Landslide Susceptibility Zonation in Jember Regency, East Java, Indonesia: An Analysis using GIS and AHP. Geoenvironmental Disasters, 7(1), 1-18.

Rubio, V., G. (2021). Bayesian inference with INLA. CRC Press:Boca Raton.

Rue, H., Martino, S., Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology). Vol.71(2)

Samat, N. and Mey, L.(2017) Malaria Disease Mapping in Malaysia based on Besaag-York-Mollie (BYM) Model. Journal of Physics : Conference Series 890(1).

Sankey, T. (2017). Statistical Descriptions of Spatial Patterns. Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1351

Santoso, P. B., et al. (2019). Analysis of Landslide Susceptibility in Pacitan Regency, East Java, Indonesia using GIS-based Multicriteria Decision Analysis. IOP Conference Series: Earth and Environmental Science, 387(1), 012023.

Saputro, D. R. S. et al. (2018). Proporsionalitas Autokorelasi Spasial Dengan Indeks Global (Indeks Moran) Dan Indeks Lokal (Local Indicator Of Spatial Association (Lisa)). Universitas Muhammadiyah Surakarta.

Schoot, R. V. D. et al. (2021). Bayesian statistics and modelling. Nature reviews methods primer.

Scott, L. M. (2015). Spatial Pattern, Analysis of. International Encyclopedia of the Social & Behavioral Sciences (Second Edition). 178-184

Simpson, D., Rue, H., Riebler, A., Martins, T. G., and SÃ¸rbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32:1â€“28.

Sugiyono. (2018). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: PT Alfabet.

Suhardiman, A., & H. S. (2022). Analisis faktor-faktor penyebab tanah longsor di Kabupaten Malang. Jurnal Sumber Daya Alam Dan Lingkungan, 12(1), 1â€“11.

Wang, J., & Zuo, R. (2021). Spatial modelling of hydrothermal mineralization-related geochemical patterns using INLA+ SPDE and local singularity analysis. Computers & Geosciences, 154, 104822.