Natural crises are dangerous events that can threaten lives and lead to severe damages. Crisis-related data can be heterogeneous and be provided from multiple data sources. These data can be formally described using ontologies and then integrated and structured forming knowledge graphs. Inferring new information from knowledge graphs can strongly assist in the various phases of the crisis management process. Different approaches exist in the literature for inferring new information from knowledge graphs. In this paper, we present a case study of a flood crisis where we discuss three approaches for inferring flood-related information, and we experimentally evaluate these approaches using real flood-related data and synthetic data for further analysis. We discuss the interest of using each of these approaches and detail its advantages as well as its limitations.