Sains Malaysiana 46(1)(2017): 27–34

http://dx.doi.org/10.17576/jsm-2017-4601-04

Representing Landslides as Polygon (Areal) or Points? How Different Data Types Influence the Accuracy of Landslide Susceptibility Maps

(Mempersembahkan Tanah Runtuh dalam Bentuk Poligon (Luas) atau Titik? Bagaimana Jenis Data Berbeza Mempengaruhi Ketepatan Peta Kecenderungan Tanah Runtuh)

NORBERT SIMON¹*, MAIREAD DE ROISTE¹, MICHAEL CROZIER² & ABDUL GHANI RAFEK³

¹Geology Program, School of Environmental & Natural Resources Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia

²School of Geography, Environment and Earth Sciences, Victoria University of Wellington

New Zealand

³Department of Geosciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar

31750 Tronoh, Perak Darul Ridzuan, Malaysia

Diserahkan: 19 November 2015/Diterima: 19 April 2016

ABSTRACT

In the literatures, discussions on the accuracy of different models for landslide analysis have been discussed widely. However, to date, arguments on the type of input data (landslides in the form of point or polygon) and how they affect the accuracy of these models can hardly be found. This study assesses how different types of data (point or polygon) applied to the same model influence the accuracy of the model in determining areas susceptible to landsliding. A total of 137 landslides was digitised as polygon (areal) units and then transformed into points; forming two separate datasets both representing the same landslides within the study area. These datasets were later separated into training and validation datasets. The polygon unit dataset uses the area density technique reported as percentage, while the point data uses the landslide density technique, as means of assigning weighting to landslide factor maps to generate the landslide susceptibility map that is based on the analytical hierarchy process (AHP) model. Both data groups show striking differences in terms of mapping accuracy for both training and validation datasets. The final landslide susceptibility map using area density (polygon) as input only has 48% (training) and 35% (validation) accuracy. The accuracy for the susceptibility map using the landslide density as input data achieved 89% and 82% for both training and validation datasets, respectively. This result showed that the selection of the type of data for landslide analysis can be critical in producing an acceptable level of accuracy for the landslide susceptibility map. The authors hope that the finding of this research will assist landslide investigators to determine the appropriateness of the type of landslide data because it will influence the accuracy of the final landslide potential map.

Keywords: AHP; Geographic Information System (GIS); landslide; landslide density; landslide susceptibility map

ABSTRAK

Kajian lepas telah banyak membincangkan kejituan model tanah runtuh yang berlainan. Walau bagaimanapun, sehingga sekarang, perbincangan mengenai jenis data (tanah runtuh dalam bentuk titik atau poligon) dan bagaimana jenis data ini mempengaruhi kejituan model-model tanah runtuh ini agak sukar untuk ditemui. Kajian ini menilai bagaimana pelbagai jenis data (titik atau poligon) mempengaruhi ketepatan model dalam menentukan kawasan-kawasan yang cenderung untuk mengalami tanah runtuh. Sebanyak 137 tanah runtuh telah didigitkan sebagai unit poligon (keluasan) dan data yang sama ini kemudiannya diubah kepada data titik; membentuk dua set data yang berasingan dengan kedua-duanya mewakili tanah runtuh yang sama dalam kawasan kajian. Set data ini kemudiannya dibahagikan kepada set latihan dan set penentusahan. Unit data poligon menggunakan teknik ketumpatan keluasan dan dilaporkan dalam bentuk peratusan, manakala data titik menggunakan teknik ketumpatan tanah runtuh (frekuensi) sebagai kaedah untuk menentukan pemberat dalam peta faktor tanah runtuh yang kemudiannya digunakan untuk menjana peta kecenderungan tanah runtuh berasaskan model Proses Analisis Hierarki (AHP). Kedua-dua kumpulan data ini menunjukkan perbezaan yang ketara daripada segi ketepatan pemetaan untuk set data latihan dan penentusahan. Peta kecenderungan tanah runtuh yang menggunakan ketumpatan kawasan (poligon) sebagai input hanya mempunyai ketepatan 48% dan 35% masing-masing untuk set data latihan dan pengesahan. Manakala ketepatan peta kecenderungan yang menggunakan ketumpatan tanah runtuh (titik) sebagai data input mencapai 89% dan 82% untuk kedua-dua set data latihan dan penentusahan. Keputusan ini menunjukkan bahawa pemilihan jenis data untuk analisis tanah runtuh adalah kritikal dalam menghasilkan peta kecenderungan tanah runtuh pada tahap ketepatan yang boleh diterima. Penulis berharap hasil kajian ini dapat membantu para pengkaji tanah runtuh untuk memastikan kesesuaian jenis data yang digunakan kerana pemilihan jenis input data tersebut akan mempengaruhi hasil akhir peta potensi tanah runtuh yang dihasilkan.

Kata kunci: AHP; ketumpatan tanah runtuh; peta kecenderungan tanah runtuh; Sistem Maklumat Geografi (GIS); tanah runtuh

RUJUKAN

Budetta, P., Santo, A. & Vivenzio, F. 2008. Landslide hazard mapping along the coastline of the Cliento region (Italy) by means of a GIS-based parameter rating approach. Geomorphology 94: 340-352.

Dai, F.C., Lee, C.F. & Ngai, Y.Y. 2002. Landslide risk assessment and management: An overview. Engineering Geology 64: 65-87.

Dhakal, A.S., Amada, T. & Aniya, M. 1999. Landslide hazard mapping and the application of GIS in the Kulekhani watershed, Nepal. Mountain Research and Development 19(1): 3-16.

Eyles, R.J., Crozier, M.J. & Wheeler, R.H. 1978. Landslips in Wellington City. New Zealand Geographer 34: 58-74.

Faisal, M.M., Tahir, S.H. & Edward, V.L.Z. 1999. Landslides along the Kota Kinabalu-Tambunan road. Second Asian Symposium on Engineering Geology and the Environment. Bangi: IAEG Malaysian National Group.

Fernandez, T., Irigaray, C., Hamdouni, R.E. & Chacon, J. 2003. Methodology for landslide susceptibility mapping by means of a GIS: Application to the Contraviesa area (Granada, Spain). Natural Hazards 30: 297-308.

Gahgah, M.M., Akhir, J.M., Rafek, A.G.M. & Abdullah, I. 2009. GIS based assessment on landslide hazard zonation: Case study of Cameron Highlands - Gua Musang road Kelantan, Malaysia. Sains Malaysiana 38(6): 827-833.

Goh, T.L., Rafek, A.G. & Arifin, M.A. 2014. Correlation of joint roughness coefficient with peak friction angles of discontinuity planes of granite, Peninsular Malaysia. Sains Malaysiana 43(5): 751-756.

Goh, T.L., Rafek, A.G., Ariffin, M.H. & Yunus, B.N. 2011. Direct shear test of granite discontinuity plane surface. Sains Malaysiana 40(5): 419-423.

Guidi, G., Gugliermetti, F. & Violante, A.C. 2009. Application of AHP method in selection of best available techniques for oil spill containment and clean-up. AIDIC Conference Series 9. pp. 159-166.

Ho, H.J., Lee, S. & Soedradjat, G.M. 2010. Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environ. Earth Sci. 60: 1317-1328.

Hufschmidt, G. & Crozier, M.J. 2008. Evolution of natural risk: Analysisng changing landslide hazard in Wellington, Aotearoa/New Zealand. Natural Hazards 45: 255-276.

Jaiswal, P., van Westen, C.J. & Jetten, V. 2010. Quantitative landslide hazard assessment along a transportation corridor in southern India. Engineering Geology 116: 236-250.

JTC-1. 2008. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. In Engineering Geology, edited by Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E. & Savage, W.Z. 102(3-4): 85-98.

Komac, M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74: 17-28.

Lee, S. & Talib, J.A. 2005. Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology 47: 982- 990.

Lin, Y.P., Chu, H.J. & Wu, C.F. 2010. Spatial pattern analysis of landslide using landscape metrics and logistic regression: A case study in Central Taiwan. Hydrology and Earth System Sciences Discussions 7: 3423-3451.

Matthew, J., Jha, V.K. & Rawat, G.S. 2007. Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. International Journal of Remote Sensing 28(10): 2257-2275.

Melchiorre, C., Matteucci, M., Azzoni, A. & Zanchi, A. 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94: 379-400.

Peralvarez, J.D.J., Irigaray, C., Hamdouni, R.E. & Chacon, J. 2009. Building models for automatic landslide-susceptibility analysis, mapping and validation in ArcGIS. Natural Hazards 50: 571-590.

Poudyal, C.P., Chang, C., Oh, H.J. & Lee, S. 2010. Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal, Himalaya. Environ. Earth Sciences 61: 1049-1064.

Rafek, A.G., Goh, T.L. & Ariffin, M.H. 2012. Correlation of joint roughness coefficient with peak friction angles of discontinuity planes of schists, Peninsular Malaysia. Sains Malaysiana 41(3): 293-297.

Rib, H.T. & Ta, L. 1978. Recognition and identification. In Landslides Analysis and Control, Special Report 176, edited by Schuster, R.A. Washington: National Academy of Science.

Roslee, R., Tahir, S. & Omang, A.K.S. 2006. Engineering geology of the Kota Kinabalu area, Sabah, Malaysia. Geological Society of Malaysia Bulletin 52: 17- 25.

Saaty, T.L. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences 1(1): 83-98.

Saaty, T. 1994. How to make a decision: The analytic hierarchy process. European Journal of Operational Research 48: 9-26.

Tongkul, F. 2007. Geological inputs in road design & construction in mountainous areas of West Sabah, Malaysia. In Proceedings of the 2nd Malaysia-Japan Symposium on Geohazards & Geoenvironmental Engineering, Bangi: Institute for Environment & Development (LESTARI). pp. 39-43.

van Westen, C.J. & Soeters, R. 1996. GISSIZ: Geographic Information Systems in Slope Instability Zonation Part 1: Theory Vol. Version 2. Enschede: ITC.

Yalcin, A. 2008. GIS based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena 72: 1-12.

Yang, S.R. & Yeh, Y.L. 2015. Geologic hazard risk assessment of slopeland villages in Southern Taiwan using remote sensing techniques. Sains Malaysiana 44(12): 1677-1683.

*Pengarang untuk surat-menyurat; email: norbsn@ukm.edu.my