Towards the Development of Disaster Management Tailored Machine Learning Systems

Abstract

Natural disasters such as cyclones and floods recur frequently in certain parts of the world. In this work, we provide a framework to build an easily deployable disaster management application, over five stages. First, we interview four categories of people to understand current problems and approaches in disaster management. Next, we analyze responses and establish that identified disaster management efforts are hitherto unable to effectively harness existing technology. We accordingly build a guided recommendation toolbox of existing Machine Learning (ML), Internet of Things (IOT), and NLP technologies, that satisfies critical system requirements for the identified efforts; this is qualitatively evaluated by senior data scientists and disaster management researchers, and found to reduce development time, as well as increase reliability and user-friendliness. Finally we provide a model to decentralize disaster management. Our work promotes the development of NLP systems tailored for disaster management, bridging the gap between research and real world applications.