Landslides Susceptibility Mapping in R Program (Case study in Lima Puluh Kota Regency)

Authors

  • Ahyuni - Ahyuni Universitas Negeri Padang
  • Bigharta Bekti Susetyo Universitas Negeri Padang
  • Isra Haryati Diva Universitas Negeri Padang
  • Zakiyah Mar’ah Universitas Negeri Makassar
  • Hamdi Nur Universitas Bung Hatta
  • Adenan Yandra Nofrizal Shizuoka University
  • Azwirda Aziz STIE Swadaya

DOI:

https://doi.org/10.11113/mjfas.v18n2.2534

Keywords:

Landslide susceptibility zone, Weight of Evidence, R program

Abstract

Landslide susceptibility zonation is necessary to be considered in land use planning at various scales., different approaches and analytical methods can be used to evaluate and zone the area and processed with GIS software. However, there are constraints in its use, such as the cost of the licenses of software and source code that cannot be accessed and evaluated by users. The recent development of open-source software that can integrate data, analysis, and graphs in a representation such as the R program, has opened up opportunities for researchers to reevaluate and modify interpretation further from available ones to address issues. In this regard, this study aims to create functions in R using the Weight of Evidence (WoE) method, a form of bivariate statistic approach to acquire the significant factors controlling landslides and generate a susceptibility map. The case study is located in Limapuluh Kota Regency, West Sumatra Province of Indonesia, a hilly and mountainous region where its districts are prone to landslides. Eight of eleven factors such as geology, landform, land cover, elevation, density of vegetation greenness, slope, rainfall intensity, and proximity to stream were regarded to control landslides which set up four classes of landslide susceptibility zone (very low, low, moderate, and high).

References

Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91.

Rossi, M., Reichenbach, P., "LAND-SE: A software for statistically based landslide, "Geoscientific Model Development, pp. 9533-9543, 2016.

Fausto, G., Alberto C., Mauro C., Paola R., "Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy," Geomorphology, pp. 181-216, 1999.

Anis, Z., Wissem, G.,Vali, V., Smida, H., Essghaie, G.M. (2019). GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia.Open Geosciences, 11:708–726 October 2019.

Pamela, Sadisun, I. A., Arifianti, Y. (2018). Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118, 012037.

Silalahi, F.E.S., Pamela, Arifianti, Y., Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(1).

Singh Pradhan, A. M., Dawadi, A., Kim, Y. T. (2012). Use of different bivariate statistical landslide susceptibility methods: A case study of Khulekhani watershed, Nepal. Journal of Nepal Geological Society, 44, 1–12.

Sumaryono, Muslim D., Sulaksana N., Triana Y.D. (2015)Weights of Evidence Method for Landslide Susceptibility Mapping in Tandikek and Damar Bancah, West Sumatra, Indonesia, International Journal of Science and Research (IJSR), Vol. 4, Issue 10, October 2015, 1283-1290.

Christos P., Christos C., "Comparison and evaluation of landslide susceptibility maps obtained from the weight of evidence, logistic regression, and artificial neural network models," Natural Hazards, vol. 93, no. August 2018, pp. 249-274, 2018.

Roger, S. B., Edzer, P. and Virgilio G.R., Applied Spatial Data Analysis with R, New York: Springer, 2013.

Hadley, W., "Tidy Data," Journal of Statistical Software, Vol. 59, no. 10, pp. 1-23, 2014.

Althuwaynee, O. F., Musakwa, W., Gumbo, T., Reis, S. (2017). Applicability of R statistics in analyzing landslides spatial patterns in Northern Turkey. 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA).

Thinnukool O., Kongchouy, N., Choonpradub C. (2014). Detection of Land Use Change Using R Program (A Case Study of Phuket Island, Thailand). Research Journal of Applied Sciences, 9: 228-237.

Tonini, Marj Abellan, Antonio. (2013). Rockfall detection from terrestrial LiDAR point clouds: A clustering approach using R. Journal of Spatial Information Science. 8. 10.5311/JOSIS.2014.8.123.

Darman, Herman (Ed) et al. 2011. Sedimentology Newsletter Sumatra. Indonesian Sedimentologist Forum (IAGI): Indonesia.

Sumaryono, Muslim D., Sulaksana N., Triana Y.D. (2015). Weights of Evidence Method for Landslide Susceptibility Mapping in Tandikek and Damar Bancah, West Sumatra, Indonesia, International Journal of Science and Research (IJSR), Volume 4 Issue 10, October 2015, 1283-1290.

Pamela, Sadisun, I. A., Arifianti, Y. (2018). Weights of Evidence Method for Landslide Susceptibility Mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118, 012037.

Pimiento E (2010) Shallow landslide susceptibility: modeling and validation.Dept of Physical Geography and Ecosystem Science—Lund University.Thesis, pp 25-29

Silalahi, F.E.S., Pamela, Arifianti, Y., Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(1).

Downloads

Published

16-05-2022