A general methodology for data-based rule building and its application to natural disaster management.

Abstract

Risks derived from natural disasters have a deeper impact than the sole damage suffered by the affected zone and its population. Because disasters can affect geostrategic stability and international safety, developed countries invest a huge amount of funds to manage these risks. A large portion of these funds are channeled through United Nations agencies and international non-governmental organizations (NGOs), which at the same time are carrying out more and more complex operations. For these reasons, technological support for these actors is required, all the more so because the global economic crisis is placing emphasis on the need for efficiency and transparency in the management of (relatively limited) funds. Nevertheless, currently available sophisticated tools for disaster management do not fit well into these contexts because their infrastructure requirements usually exceed the capabilities of such organizations. In this paper, a general methodology for inductive rule building is described and applied to natural-disaster management. The application is a data-based, two-level knowledge decision support system (DSS) prototype which provides damage assessment for multiple disaster scenarios to support humanitarian NGOs involved in response to natural disasters. A validation process is carried out to measure the accuracy of both the methodology and the DSS