Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation

Abstract

This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semiquantitative model with ten indicator maps and a cell size of 90× 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods, and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest concentration of high landslide risk index values while the Nipe-Cristal-Baracoa system has the highest absolute values, although they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone areas.

A risk analysis for floods and lahars: case study in the Cordillera Central of Colombia

“…Dikau and Weichselgartner 2005;Hufschmidt et al 2005;Alcántara-Ayala 2002;Cutter et al 2000;Dibben and Chester 1999), the most important factors and their integration into (quantitative) risk analysis are much less clear (Kuhlicke et al 2011). Further research is also required with respect to the visualisation of risk and its communication to decision makers (Castellanos Abella and van Westen 2007).…”

“…Most often, a linear scale transformation is used (Malczewski 1999), which transforms the input values linearly into values between 0 and 1. However, this standardization method can be inappropriate when the original values include disproportionately high or low values (Castellanos Abella and van Westen 2007), which is often observed when dealing with population density data, because densities are much higher in urban than in rural areas. To avoid this problem, we used a concave curve equation as proposed by Castellanos Abella and van Westen (2007) to transform the physical vulnerability data.…”

“…The degree of loss is often expressed as monetary loss such as reconstruction costs or building value (Papathoma-Köhle et al 2010). However, due to the difficulties and the required time to assess vulnerability as the degree of impact depending on the magnitude and the characteristics of the hazardous event and the characteristics of the elements at risk (PapathomaKöhle et al 2010), in many studies, vulnerability indicators represent the amount of elements at risk rather than actual measures of vulnerability (Castellanos Abella and van Westen 2007;Keiler et al 2004;Moran et al 2003;Liu et al 2002).…”

“…Weichselgartner 2001) and developing countries (e.g. Castellanos Abella and van Westen 2007;Hegglin and Huggel 2008;D'Ercole and Metzger 2009). Although recent studies achieved such an integration by mapping technological risks using GIS (Armenakis and Nirupama in press), such studies are still rare.…”

Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories

“…The landslide inventory reports are particularly limited when landslide events are associated with contemporaneous hazards such as tropical cyclones, or when landslides occur in remote areas where they are typically underreported. These issues are emphasized by studies that employ similar methodologies (Guzzetti et al, 1994;Petley et al, 2005Petley et al, , 2007Castellanos Abella and van Westen, 2007;Chau et al, 2004). Due to reporting issues, the landslide inventory does not provide a comprehensive list of landslide occurrences globally nor does it assume that deficiencies in landslide reporting are distributed randomly.…”