An Empirical-Statistical Model for Landslide Runout Distance Prediction in Indonesia

Dyah Wahyu Apriani, Christianto Credidi, Septino Khala

Abstract


There have been many attempts and methods for predicting landslide-affected areas; empirical methods, numerical methods, and laboratory models are commonly used for prediction. Laboratory and numerical models require an input of parameters that are difficult to determine accurately. At the same time, empirical statistical methods use statistical methods based on historical data of landslide events to form an empirical model. Statistical analysis of empirical observations builds a possible relationship between disaster area characteristics and slide behavior because it does not require detailed mechanics of avalanche movement; the empirical-statistical model is a simple and practical tool in the initial assessment to predict the sliding distance of an avalanche that will occur. The main discussion of this study is that the volume of avalanches (V) has a more significant influence than the height of the slope (H) on the length of the avalanche (L) that occurs. Fifty-nine data on landslide events that have occurred in Indonesia are used to a prediction model for landslide events reviewing the slope geometry parameters in the form of H, slope (θ), and V and discussing the main factors that affect the sliding distance of avalanches that have not been discussed in research in the Indonesian territory. The analysis shows that H has a significant effect on the sliding distance of the avalanche compared to V. The best model produced to predict the sliding distance of the avalanche is L = 6.918 H0,840 and produces an average error rate of 29% for the landslide measurement data.


Keywords


Landslide; Statistic Model; Landslide Runout Prediction

Full Text:

PDF

References


M.M. Nordiana, I.N. Azwin, M.N.M. Nawawi, A.E. Khalil, Slope failures evaluation and landslides investigation using 2-D resistivity method, NRIAG J. Astron. Geophys. 7 (2018) 84–89. https://doi.org/10.1016/j.nrjag.2017.12.003.

Ekaputri, J. J., Anam, M. S., Luan, Y., Fujiyama, C., Chijiwa, N., & Setiamarga, D. H. E. (2018). Application of GGBFS and Bentonite to Auto-Healing Cracks of Cement Paste. JACEE (Journal of Advanced Civil and Environmental Engineering), 1(1), 38-48. 10.30659/jacee.1.1.38-48.

J.J. Roering, J.W. Kirchner, W.E. Dietrich, Characterizing structural and lithologic controls on deep-seated landsliding: Implications for topographic relief and landscape evolution in the Oregon Coast Range, USA, Bull. Geol. Soc. Am. 117 (2005) 654–668. https://doi.org/10.1130/B25567.1.

S.N. Ward, S. Day, Particulate kinematic simulations of debris avalanches: Interpretation of deposits and landslide seismic signals of Mount Saint Helens, 1980 May 18, Geophys. J. Int. 167 (2006) 991–1004. https://doi.org/10.1111/j.1365-246X.2006.03118.x.

M. McKinnon, Statistical Analysis of Physical Characteristics and Model Parameters, University of California, 2010.

D. Rickenmann, Empirical relationships for debris flows, Nat. Hazards. 19 (1999) 47–77. https://doi.org/10.1023/A:1008064220727.

O. Hungr, J. Corominas, E. Eberhardt, State of the Art Paper #4, Estimating landslide motion mechanism, travel distance, and velocity, Landslide Risk Manag. (2005) 99–128.

G.B. Crosta, S. Imposimato, D.G. Roddeman, Continuum numerical modeling of flow-like landslides, Landslides. 49 (2006) 211–232. https://doi.org/10.1007/978-1-4020-4037-5_12.

G. Devoli, F. V. De Blasio, A. Elverhøi, K. Høeg, Statistical analysis of landslide events in Central America and their runout distance, Geotech. Geol. Eng. 27 (2009) 23–42. https://doi.org/10.1007/s10706-008-9209-0.

F. Legros, The mobility of long-runout landslides, Eng. Geol. 63 (2002) 301–331. https://doi.org/10.1016/S0013-7952(01)00090-4.

M. Qarinur, Landslide Runout Distance Prediction Based on Mechanism and Cause of Soil or Rock Mass Movement, J. Civ. Eng. Forum. 1 (2015). https://doi.org/10.22146/jcef.22728.

D. Rickenmann, Runout prediction methods, in: Debris-Flow Hazards Relat. Phenom., 2007: pp. 305–324. https://doi.org/10.1007/3-540-27129-5_13.

Sugiyono, Business Research Methods, 18th ed., Alfabeta, Bandung, 2014.

D. Karnawati, Mechanism of Earthquake Induced Landslides in Yogyakarta Province , Indonesia, (2006) 1–8.

A. Amirahmadi, S. Pourhashemi, M. Karami, E. Akbari, Modeling of landslide volume estimation, Open Geosci. 8 (2016) 360–370. https://doi.org/10.1515/geo-2016-0032.

D. Guo, M. Hamada, C. He, Y. Wang, Y. Zou, An empirical model for landslide travel distance prediction in Wenchuan earthquake area, Landslides. 11 (2014) 281–291. https://doi.org/10.1007/s10346-013-0444-y.

PVMBG, Laporan Pemeriksaan Gerakan Tanah 2015-2021, PVMBG, Bandung, 2021. https://vsi.esdm.go.id/index.php/gerakan-tanah/kejadian-gerakan-tanah?start=40.

R.M. Iverson, The debris-flow rheology myth, Int. Conf. Debris-Flow Hazards Mitig. Mech. Predict. Assessment, Proc. 1 (2003) 303–314.

Moriwaki, of the Runout Distance of a Debris • X ˜ e Š° * Hiromu M0RIWAKI Synopsis An analysis of the landslide data, shows that there exists a linear relationship between the " runout height/runout distance " and the " tangent of slope in the source area, "whic, (1987).




DOI: http://dx.doi.org/10.30659/pondasi.v27i1.22618

Refbacks

  • There are currently no refbacks.


Redaksi Pondasi PUSAT STUDI DAN KONSULTASI TEKNIK Print ISSN : 0853-814X E ISSN : 2714-7622 Gedung Fakultas Teknik lantai II Universitas Islam Sultan Agung Semarang Jl.Raya Kaligawe KM 4 PO.BOX 1235 jurnalpondasiunissula@gmail.com jurnalpondasi@unissula.ac.id Semarang 50012