## 8 Experimental Results
### Emergency Case study in Vietnam  
To begin the evaluation of the proposed approach to group decision making using the Google maps we conducted an experiment based on a'real-world' emergency in Vietnam. In this experiment, experts using smart phones or smart devices are embedded in vehicles loaded _Kansei_ data sets for the proposed system. The _Kansei_ matrix was constructed with dynamic dimensions [_Kansei_ words, quantitative, and qualitative factors] represent vehicles from the DSS database of the Vietnam-Japan project disaster centre. In the proposed system, the SOM model uses a Gaussian neighborhood function with an adaptive variance and learning rate. The parameters are set in the _Kansei_ SOM application (SOM sizes = 30 \(\times\) 30, Sigma max = 12, Sigma min = 3, Iteration set = 30 and learning rates from 0.05 to 0.01). To consider decision maker preferences in the proposed system, the experts were divided into three groups based on emergency management strategies as objectives in group decision making. The proposed system describing pre-emergency situation, the situation during the emergency, and the post-disaster situation is shown in Figure 4.  
In simulations, the map result showed the _Kansei_ distance of potential emergency response vehicles from the location of the emergency. As observed from Figure 5, decision makers selected these vehicles, matched with appropriate emergency management strategies, by reducing the maximum _Kansei_ disaster distance of the disaster vectors and eliminating those that have a greater distance. Figure 5 illustrates potential vehicles going to the route in disaster domain, aggregated by expert preferences on the map. In this case study, a human agent (the decision maker) can freely select a strategy [from a range of suggested strategies] in a disaster response scenario. For example, in disaster strategy \((1)\), the decision maker can select the most appropriate strategy to maximise the response (the rescue of individuals using a range of potential implementation options which may include: ambulances, emergency vehicles such as police and emergency service vehicles, fire engines, and helicopters) in a fire disaster situation. Figure 4: The SOM map results in matching emergency strategies for emergency case study in Japan. However, in considering disaster strategy (2) (as shown in Figure 5), the identification and selection of the optimal strategy to maximise the rescue of individuals trapped in a fire must consider current'real-time' traffic congestion within a city and the optimal solution must select the quickest route (which may not be the shortest route). For instance, in the case of disaster 3 the optimal solution [as identified by a number of experts] has been achieved by implementing control limited to ambulances and fire engines deployed to react to the disaster. To identify warnings and emergency response notifications, decision maker(s) can input preferences with single or multiple locations and objectives. Let us assume that the emergency service vehicles are shown on Google map belonging to the disaster strategy (2) as shown in Figure 5.  
Following entering the decision maker(s) preferences, the experimental results are shown on the map by the combination of transportation and priority. He/she can find the optimal route(s) while the vehicles moved to the location of the fire in'real-time'. In the process of multiple decision-making, decision makers can consult a simulation of the vehicles and their movement with locations on the Google map, as shown in Figure 6.  
Prioritisation of service vehicles [to respond to an emergency] can enable the optimal path (including available path(s) plus the distance(s) from the current location of service vehicle(s) to the  
Figure 5: The SOM map results in viewed vehicles in emergency strategies. Figure 6: Initial vehicles with the proposed fire disaster appear on the Google map.