Enhancing Disaster Response with Architectonic Capabilities by Leveraging Machine and Human Intelligence Interplay

Abstract

Disaster response presents the current situation, creates a summary of known information on the disaster, and sets the path for recovery and reconstruction. During the last ten years, various disciplines have investigated disaster response in a two-fold manner. First, researchers published several studies using state of the art technologies for disaster response. Second, humanitarian organizations produced numerous protocols on how to respond to natural disasters. The former suggests questioning: If we have developed a considerable amount of studies to respond to a natural disaster, how to cross-validate its results with NGOs' protocols to enhance the involvement of specific disciplines in disaster response? To address the above question, the research proposes an experiment that considers both: knowledge produced in the form of 8364 abstracts of academic writing on the field of Disaster Response and 1930 humanitarian organizations' mission statements indexed online. The experiment uses Artificial Intelligence in the form of Neural Network to perform the task of word embedding –Word2Vec– and an unsupervised machine learning algorithm for clustering –Self Organizing Maps. Finally, it employs Human Intelligence for the selection of information and decision making. The result is a mockup that will suggest actions and tools that are relevant to a specific scenario forecasting the involvement of architects in Disaster Response.