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Applying a structured multicriteria risk mapping method in Mount Cameroon 

 

Audrey HOHMANN, MIA-VITA team member,

BRGM, Orléans, France

Email: a.hohmann[at]brgm.fr

With the support of MINIMIDT and INGV.

 

The present work aims at applying a risk mapping method in Mount Cameroon volcano (see Fig.1), which can take into account social, economic and environmental variables into GIS models. More broadly, this study focuses on the integration of GIS capability with a Multi-Criteria Decision Making Method (MCDM) by adapting Saaty’s Analytic Hierarchy Process (AHP, Saaty 1977) in order to map volcanic risk. AHP is a decision approach designed to solve complex multiple criteria problems (Saaty, 1990) using relative pairwise comparisons. To apply this approach, it is necessary to break down a complex unstructured problem into its component factors. Once these factors identified, they can be hierarchized.  One of the advantages of AHP is to include both qualitative and quantitative criteria in the evaluation.

 

Figure 1: Map of Cameroon (source: Googlemaps) and location of Mount Cameroun

 

Such a methodology has already been applied at the Merapi volcano by CVGHM/BPPTK (Fig. 2). The same definition and components of risk have been kept for our study and so the same hierarchical scheme. The main factors that are considered in risk assessment are the hazard itself, exposure aspect to the threat of disaster, vulnerability aspects and capacity to face disaster. Among these main factors, we considered that each component of the risk has an equal importance.

 

Figure 2: Components of risks taken into account for Merapi risk mapping (Source: CVGHM / BPPTK)

 

Once the geographic entities involved in the analysis are determined (i.e., in our study area, the villages in the Fako division of Mount Cameroon), the AHP method requires the identification of each risk component to be taken into account and a data collection.

 

In a typical hierarchy process, the top level reflects the overall objective of the decision problem, which is the risk in our case. The main factors affecting the decision are represented in intermediate levels, which are here the Hazard, Exposure, Vulnerability and Coping capacity.

The lowest level comprises the sub-factors to assess the main factors (Fig. 3). Once a hierarchy is set up, the decision-maker can start a prioritization procedure to determine the relative importance of each factor in each level of the hierarchy. Then the elementary entities profiles are evaluated with respect to all sub-factors, factors and finally the risk.

 

Figure 3: Hierarchical scheme for determining risk on Mount Cameroon volcano

 

Data collection was based on two different sources which contributed to the Hazard and Exposure data for the first source and Vulnerability and Coping capacity for the second source.

The multi-hazard map and GIS layers of elements at risk have been previously collected and organized during the GRINP project by Thierry and al. (2007) whereas demographic and socio-economic data have been collected during MIAVITA project by MINIMIDT and INGV. As a rule, these necessary data can be collected either from the central/local authorities’ census or from the site by household surveys. A critical parameter in such surveys is the sampling size of the population. In fact, the sample should represent all the characteristics of the concerned population, that is to say here the residents of several settlements in Fako district.

To sum up, we have considered 22 sub-factors in our hierarchical scheme. For example, the Figure 4 shows that the Hazard factor is divided into four sub-factors which correspond to different multi-hazard levels for the studied area of villages.

 

Figure 4: Example of sub-factors included in a main factor

 

In a second step, appropriate geoprocessing were made, such as (by settlement):

• Counting answers to socio-economic questionnaires

 Surface computing for each multi-hazard level

 Surface computing of elements at stake by multi-hazard levels.

 

The main points of the AHP methodology consist in:

 representing a decision making problem by a hierarchical structure constituted by its components,

 carrying out pairwise comparisons of these components relying on the judgments of experts,

 deriving priority scales and then synthetizing these scales by multiplying them by the priority of their parent nodes and adding for all such nodes.

The Figure 5 illustrates the main steps of the AHP procedure as a flowchart.

 

Figure 5: Flowchart of AHP methodology

 

The integration of the AHP in a GIS combines decision support methodology with powerful visualisation and mapping capabilities which in turn should considerably facilitate the risk mapping. Moreover, GIS offer powerful functionalities with regard to spatial querying and spatial data analysis. In this study, the program has been written in Python code using ArcPy which is a site-package available in ArcGIS 10.

 

An example of the risk mapping is given below as a proof of concept that the described AHP procedure tools can be applied to any volcano. For this demonstration, some simplification hypotheses have been stated in order to obtain first results as a basis for discussion. But we may refine the analysis and rerun the mapping with the collegial feedback of local experts on factors’ identification and weighting, ensuring transdisciplinary knowledge of the area.

Figure 6 shows the results as a graphic of sub-factor’s weight which was found previously thanks to the Pairwise Comparison Method.

 

Figure 6: Graphic of sub-factor's weight classed by main factor

 

 

There are many advantages of the proposed method but also some limitations. For example, we may underline its flexibility, its possibility to support the lack of data, and integrate quantitative and qualitative indicators. Another force, but also limitation, is the strong need to involve various agents and experts to ensure the consistency of results: it is a long process, and it cannot be updated in real-time.

 

Acknowledgments:

This study has been made possible thanks to the results of previous work carried out within the GRINP project by Thierry and al. (2007) and the socio-economic vulnerability assessment published in MIAVITA project by MINIMIDT and INGV (2011).

  

References:

Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234-281

Saaty, T. L. (1990). Multicriteria decision making: The analytic hierarchy process. Pittsburgh, PA: RWS Publications

Thierry and al. (2007), Projet GRINP – Composante 1 Réalisation d’une carte de zonage des risques du Mont Cameroun Rapport final – Rapport BRGM RC-54727-FR, 333p