Machine learning for relevance of information in crisis response

Efficient communication during crisis response situations is a major challenge for involved emergency responders. Lack of relevant information or too much irrelevant information hampers the emergency responders’ decision-making process, workflow and situational awareness. Despite efforts to better centralize relevant information during crisis response, a gap still exists between the information supply and information needs of responders. Our contribution to bridge the information gap is a software system that monitors communication and may send information to emergency responders that were not addressed in the initial communication. The system, Task-Adaptive Information Distribution (TAID) is capable of disseminating information in a timely manner and adapting itself to the fast-changing information needs in a crisis response environment. This TAID system was trained with practical examples for which information relevance is known. To assess relevance, TAID uses a built relevance model for crisis response using methods from machine learning. In TAID this was set up as a classification task in which input information and knowledge about (ir)relevance of information was used. The technical results of the built relevance models are promising. The TAID-effect on crisis response was measured with simulation experiments which show that the expected effect of the TAID system on crisis response is restricted to specific circumstances. In a number of cases the intervening of TAID improved the situational awareness of emergency responders and reduced the duration of the crisis with very low additional costs, which results from time taken due to reading additional messages