| Floods rank among the most destructive natural disasters, inflicting severe harm on human lives, societies, and ecosystems. A flood can be characterized as a complex nonlinear phenomenon, thus the use of Machine Learning (ML) tech-niques and of Artificial Intelligence (AI) tools tend to be of vital importance. The authors of this paper propose an innovative hybrid approach that employs the Fuzzy C-Means (FCM) Algorithm and a novel Fuzzy Inference System (FIS) in order to determine the relation between the inflow and the outflow. Overall, the whole effort is based on a fuzzy rule-based methodology. It is a novel and hy-brid research effort, that employs both FCM (for the determination of clusters) and a novel FIS for the outflow prediction. In fact, it incorporates fuzziness blur-ring the crisp margins of the clusters, offering a rational model. The model has proved to be quite robust in the testing phase, achieving an overall R2 of 0.9104. Furthermore, the proposed hybrid approach seems capable of assessing the be-ginning of flood events. |
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