*The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*

number of texts still creates confusion, as many studies and mission statements do not focus specifically on architectural practices in disaster response. Instead, such clustering has a general approach, making it challenging to select studies or tools to answer questions about spatial or constructive problems. Therefore, we will use the SOM as a first filter to narrow the focus to specific tasks (architectural practices). The SOM algorithm allows us to have an organized view of the data; that is to say, we can make a selection based on a specific approach. Thus, it will enable us to concentrate on the information relevant to the focus of this experiment.

The filtering will be performed on both datasets beginning with the mission statements of humanitarian organizations. For this purpose, the numerical vectors representing the texts from humanitarian organizations were fed as inputs in a 10x10 SOM grid. The algorithm started with an initial distribution of random weights, and over


#### **Figure 3.**

*The SOM HO is a SOM grid of 10×10 data from the humanitarian organization data.*

1 million epochs eventually settled into a map of stable zones or clusters. The output layer can be visualized as a smooth changing spectrum where each SOM node has its coordinates and an associated n-dimensional vector or Best Matching Unit (BMU). For visualization purposes, a color assigned to the weight value (n-dimensional vector) of each BMU and a list of keywords are displayed together. The keywords are the most common terms used in the texts that are clustered in each SOM node. The size of the word represents the number of occasions the word appeared in the group of text. **Figure 3** shows the consistency in clustering, such as similar keywords being positioned close to each other. This trained SOM grid can be considered a common ground for the mission statements of humanitarian organizations, which will be called SOM HO.


#### **Figure 4.**

*Shows the activated cells of the SOM HO, where humanitarian organizations that share a common interest in academic writing are found.*

#### *The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*

Although this SOM HO represents data in an organized manner, it also contains data on humanitarian organizations that may not be relevant to this thesis's focus. As our focus is on architectural practices for natural disaster response, we will perform filtering of the humanitarian organization's data based on similar interests shared with academic publications. When a new dataset is fed to a trained SOM, each unique data point measures its Euclidean distance to each BMU of the trained SOM. The closer the distance, the better the data point fits that node. When feeding the data on academic literature into the trained SOM HO, this data activates a specific number of cells. Hence, the data we will take for creating our final common ground comes only from the cells activated by the academic publication data. **Figure 4** shows the activated cells of the SOM HO, where humanitarian organizations that share a common interest in academic writing are found.

After collecting all the activated cells, 1081 humanitarian organizations out of the original list of 1930 were filtered. Some of the selected humanitarian organizations are: The International NGO Safety Organization, Nansen International Office for Refugees, Peoplesafe, Rise Against Hunger, SeedChange, and Association of Assistance Solidarity Supportiveness of Refugees and Asylum Seekers.

A similar filtering process was performed on the data on academic writing––initially 8364. First, the encoded abstracts were fed into a new 2D SOM algorithm of 10×10, where each text was clustered based on the similarity of content (the training procedure was like that in the case of humanitarian organization mission statements)—creating a SOM of academic publications, which we will call SOM AP. The filtering constraint was defined by a sample of publications that will be taken as exemplary text. Those samples of academic publications concentrate on architectural practices for disaster response. Such selected academic publications were fed into the trained SOM AP and found some cells with similar approaches. Out of the 8364 abstracts, 835 were selected. **Figure 5** shows the Map of Events AP and the selected cells matching the selected publications.

#### **Figure 5.**

*SOM 10×10 of abstracts and the selected cells based on specific literature. Resulting in the final filtering of 835 abstracts.*

#### *Avantgarde Reliability Implications in Civil Engineering*


#### **Figure 6.**

*Common Ground, a SOM of 10x10 trained with joined data from filtered humanitarian organizations (1082) and academic writing from the field of disaster response (835).*

The filtered dataset of humanitarian organizations (1082) was joined with the filtered dataset of academic publications (835), creating a new dataset of 1917 texts. These data were fed as input in a new SOM grid of 10×10 that, after a million iterations, settled into a map of clustered texts or what we will call a final Common Ground (**Figure 6**). Such a Common Ground joins two discourses regarding disaster response in an organized manner that serves as a ground for articulating informed decisions, which will emerge out of specific requirements and interests.
