DOI https://doi.org/10.36487/ACG_repo/2025_98
Cite As:
Farina, P, Catani, F, Rosi, A, Setiawan, I, Junaidi, A, Afrizal, K & Wijayanto, A 2020, 'Development of an early warning system for shallow landslide
hazard in the Grasberg area, Indonesia', in PM Dight (ed.),
Slope Stability 2020: Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 1425-1438,
https://doi.org/10.36487/ACG_repo/2025_98
Abstract:
In the middle of the 20th century, one of the largest open pit mining facilities in the world was established at Grasberg on top of the main Papuan ridge. In time, this expanded to become the most notable man-made landscape feature on the entire island. Mining operations are supported by a large array of workshops and facilities, scattered from the top of the mountain down to the seashore. They include large camps and mining villages hosting the workforce and their families.
In this work, we present the results of a project to help mining company staff define triggers for an early warning system (EWS) for shallow landslides using meteorological forecasts and rain gauge measurements. This would help to mitigate the risk for the Grasberg mine and surrounding valleys of sudden detachment, routing and runout of shallow landslides.
To achieve such a scope, the work has been split into three main tasks:
Keywords: landslide hazard mapping, early warning systems, machine learning
References:
Benedetti, A, Casagli, N, Bosi, V, Dapporto, S, Ciolli, S, Palmieri, M & Zinoni, F 2005, ‘Modello statistico per la previsione operativa dei fenomeni franosi nella regione Emilia-Romagna’ (Statistical model for the operational forecast of landslides in the Emilia-Romagna region), Boll Soc Geol Italy, vol. 124, pp. 333–344.
Brunsden, D 1973, ‘The application of system theory to the study of mass movement’, Geol Appl Idrogeoeol, vol. 8, pp. 185–207.
Caine, N 1980, ‘The rainfall intensity-duration control of shallow landslides and debris flows’, Geografiska Annaler, vol 62, pp. 23–27.
Catani, F, Lagomarsino, D, Segoni, S & Tofani, V 2013, ‘Landslide susceptibility estimation by Random Forests technique: sensitivity and scaling issues’, Natural Hazards and Earth System Sciences, vol. 13, pp. 2815–2831.
Catani, F, Tofani, V & Lagomarsino, D 2016, ‘Spatial patterns of landslide dimension: A tool for magnitude mapping’, Geomorphology, vol. 273, pp. 361–373.
Dai, FC & Lee, CF 2001, ‘Frequency-volume relation and prediction of rainfall-induced landslides’, Engineering Geology, vol. 59, pp. 253–266.
Goovaerts, P 1997, Geostatistics for natural resources evaluation, Oxford University Press, Oxford.
Martelloni, G, Segoni, S, Fanti, R & Catani, F 2012, ‘Rainfall thresholds for the forecasting of landslide occurrence at regional scale’, Landslides, vol. 9, issue 4, pp. 485–495.
Guzzetti, F, Peruccaci, S, Rossi, M & Stark, CP 2007, ‘Rainfall thresholds for the initiation of landslides in central and southern Europe’, Meteorology and Atmospheric Physics, vol. 98, pp. 239–267.
Petley, D 2012, ‘Global patterns of loss of life from landslides’, Geology, vol. 40, pp. 927–930.
Rosi, A, Peternel, T, Jemec-Auflič, M, Komac, M, Segoni, S & Casagli, N 2016, ‘Rainfall thresholds for rainfall-induced landslides in Slovenia’, Landslides, vol. 13, pp. 1571–1577.
Rosi, A, Segoni, S, Battistini, A, Rossi, G, Catani, F & Casagli, N 2017, ‘Definition of a fully functional EWS based on rainfall thresholds, the case of study of Tuscany Region’, Proceedings of the Workshop on World Landslide Forum, Springer, Cham, pp. 169–174.
Rosi, A, Canavesi, V, Segoni, S, Dias Nery, T, Catani, F & Casagli, N 2019, ‘Landslides in the mountain region of Rio de Janeiro: a proposal for the semi-automated definition of multiple rainfall thresholds’, Geosciences, vol. 9, doi: 10.3390/geosciences9050203
Rosi, A, Tofani, V, Tanteri, L, Stefanelli, CT, Agostini, A, Catani, F & Casagli, N 2018, ‘The new landslide inventory of Tuscany (Italy) updated with ps-insar: Geomorphological features and landslide distribution’, Landslides, vol. 15, pp. 5–19.
Segoni, S, Rosi, A, Fanti, R, Gallucci, A, Monni, A & Casagli, N 2018,’ A regional-scale landslide warning system based on 20 years of operational experience’, Water, vol. 10,
Wichmann, V 2017, ‘The gravitational process path (GPP) model (v1.0) - a GIS-based simulation framework for gravitational processes’, Geoscientific Model Development, vol. 10, pp. 3309–3327.
Zhou, C, Yin, K, Cao, Y, Ahmed, B, Li, Y, Catani ,F & Pourghasemi, HR 2018, ‘Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China’, Computers and Geosciences, vol. 112,
pp. 23–37.