**4. Validation**

To validate the proposed approach, we will take international news (reports on natural disasters and their impact on the communities) since, after a natural disaster, the first source of information comes from that source. It should be emphasized that any query can be taken, being this something very particular or something

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

descriptive, as in the case of the news. For experimental purposes, the specific interest (query) was extracted from news describing three natural disasters of the last 5 years.

The first one was the 2016 Earthquake of magnitude 7.8 on the Ecuadorian coast:

*"A magnitude 7.8 earthquake rocked Ecuador's coast on April 16, 2016 — killing almost 700 people and leveling homes, schools, and infrastructure. More than 6,000 people were severely injured. The quake's epicenter was offshore, about 17 miles from the town of Muisne in Manabí province and 100 miles northwest of Quito, the capital. After the quake, more than 700,000 people needed assistance. An estimated 35,000 houses were destroyed or badly damaged, leaving more than 100,000 people in need of shelter. Water, sanitation, and healthcare facilities were also destroyed."* [21, 22]

The second one from the 2019 earthquake of 5.4 magnitude in Costa Rica.

*"A magnitude 5.4 earthquake shook much of Costa Rica at 7:33 p.m., according to preliminary data from the National Seismological Network (RSN). The tremor had an epicenter near Arancibia, Puntarenas, which is located about 45 miles northwest of San José and its surrounding Greater Metropolitan Area. RSN indicates the quake was felt throughout the Central Valley, home to nearly three-quarters of Costa Rica's population. There have not been any immediate reports of substantial damage. "According to preliminary data from the emergency committees, so far there is no report of damage after the perceived earthquake, "said the National Emergency Commission (CNE) in a post. The National Seismological Network has already reported at least one aftershock, which occurred at 7:38 p.m. and had a similar epicenter."* [23]

The third one from the 2019 earthquake with a magnitude of 6.5 in Indonesia.

*"A 6.5-magnitude earthquake struck the remote Maluku Islands in eastern Indonesia on Thursday morning, killing at least 20 people. Indonesian officials said the quake, which was detected at 8:46 a.m. local time, did not present the threat of a tsunami. But it was classified as a "strong" earthquake in Ambon, a city of more than 300,000 people and the capital of Maluku Province. The United States Geological Survey said the epicenter was about 23 miles' northeast of Ambon. At least 20 people were killed in the quake, the authorities said, including a man who was killed when a building partially collapsed at an Islamic university in Ambon, according to Reuters. More than 100 people were reported injured in the quake, and the authorities said about 2,000 had been displaced from their homes."* [24]

All queries were pre-processed and fed into the trained Word2Vec model to extract its word embeddings. The output of the Word2Vec model was encoded with HOS (see section Data Processing), having a final vector of 200 dimensions for each query. Each query vector was then fed into the final Common Ground, SOM of 10×10, (**Figure 7**), finding the closest Euclidean distance to a BMU. **Figure 7** displays the closest BMU concerning each query vector. For the first case study, the keywords in the selected cell are: community, earthquake, risk, and safety. For the second case study, the keywords in the selected cell are: disaster, community, study, and management. For the third case study, the keywords in the selected cell are: energy, disaster, policy, study.

**Figure 7.** *On color, the BMU had the closest Euclidean distance to each query vector.*
