## 6 The Proposed System
**DSS Database**: The DSS database as knowledge based in the proposed model consists of sensor data, _Kansei_ data set, which responds to decision makers based on their preference in dynamic disaster environments. Most processes of training data sets and fuzzy rules in reasoning are executed in the DSS database. 5. **Disaster map**: The proposed model is embedded with GIS map using Google map connected through API. The vehicles in tracking GPS via GIS map are identified by the locations on the map. _Emergency Strategies Quantified by Training Self-Organising-Maps_  
**Step 1. **_Input data sets getting from emergency domain sensors and expert preferences for matching disaster strategies located points in a map_: let a decision maker belonging to \(E^{S}=\{\varepsilon_{1}^{S},\varepsilon_{2}^{S},\ldots,\varepsilon_{q}^{S}\}\) be a set of experts for a selection of emergency strategies, where \(q\) is the number of experts that select one or several emergency strategies \(Q_{i}^{S}|(t=1,\ldots,c)\) with its data normalization in fuzzy weights in the range \([0,1]\) and his/her preference \(\beta_{j}^{S}\) can be defined in a five-point scale (0: _oppose_, 0.25: _almost oppose_, 0.5: _have no preference_, 0.75: _almost agree_, 1: _agree_). **Step 2. **_Construction of_**Kansei _emergency matrix_: _Kansei_ emergency matrix \(M_{n\times p}^{S}\) is constructed using fuzzy weight values in the range \([0,1]\) for the results of the _Kansei_ evaluation and the fuzzy evaluation model for emergency strategies as described in Section 5.1. Let \(V^{S}=\{V_{1}^{S},V_{2}^{S},\ldots,V_{h}^{S}\}\) be a set of emergency resources (vehicles) in the _Kansei_ disaster matrix \(M_{n\times p}^{S}\) in an emergency domain \(S\), where \(h\) is the number of emergency resources (vehicles) with rules considered by experts and data set(s) are expressed and updated in the DSS database. **Step 3. **_Visualisation of_**Kansei _disaster matrix by SOM_: to find either emergency strategy matching with expert preferences or emergency domains. Figure 3: The proposed system showing pre emergency, an actual emergency, and post emergency scenarios. **Step 3.1**. : _Definition of Kansei disaster distance_: in the training process to evaluate differences among _Kansei_ emergency attributes of emergency vectors by a SOM, the _Kansei_ emergency attribute distance \(d^{S}_{C_{i}\to C_{j}}\) between two vectors \(D^{S}_{C_{i}}\) and \(d^{S}_{C_{j}}\) represents attributes of emergency vectors \(C^{S}_{i}\) and \(C^{S}_{j}\) respectively as defined by Euclidean distance given by Equation (1):  
\[d^{S}_{C_{i}\to C_{j}}=\parallel D^{S}_{C_{i}}-D^{S}_{C_{j}}\parallel \tag{1}\]  
**Step 3.2**. : _Visualising the location emergency matrix by a SOM_: the location emergency matrix \(L^{S}_{x\times k}\) is visualised by a SOM in order to find the similar attributes of emergency vectors matching with emergency locations based on the SOM map. **Step 3.3**. : _Visualising Kansei disaster matrix by the SOM_: the _Kansei_ disaster matrix \(D^{S}_{n\times p}\) is visualized by a SOM in order to find emergency management strategy matching with expert preferences. To calculate the weights of decision maker preference distances together with a selected emergency strategy \(p^{S}_{t}|(t=1,\ldots,c)\), the decision maker preference distance \(d^{S}_{e_{i}\to e_{j}}\) is represented by \(m^{t}_{ij}\) calculated from the _Kansei_ emergency attribute distance \(v^{t}_{ij}\) evaluated by a decision maker \(e^{S}_{i}\) of his/her group at iteration \(t\) and the _Kansei_ disaster weight \(w^{t}_{ij}\) of the decision maker group. The weight of \(m^{t}_{ij}\) is expressed by Equation (2):  
\[m^{t}_{ij}=\parallel\frac{1}{P}\sum_{j=1}^{k}w^{t}_{ij}-v^{t}_{ij}\parallel \tag{2}\]  
where (\(i=1,\ldots,q,j=1,\ldots,k\)) and \(p\) is the number of decision makers in each group. **Step 3.4**. : _Updating Kansei disaster weights_: a Decision matrix \(D^{S}_{q\times k}\) is updated by its weights given by Equation (3).