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The frequency of occurrence and intensity of floods is a huge threat to environment, human existence, critical infrastructure and economy. Flood risk assessments depend on probabilistic approaches and suffer from non-existence of appropriate indices of acceptable risk, dearth of information and pieces of knowledge for explicit view and understanding of the characteristics and severity level of flood hazard. This paper proposes a hybridized intelligent framework comprising fuzzy logic (FL), neural network and genetic algorithm for clustering and visualization of flood data, prediction and classification of flood risks severity level. A multidimensional knowledge model of flood incidence using star, snowflake and facts constellation schemas was proposed for the knowledge warehouse. A six-layered adaptive neuro-fuzzy inference system implementing mamdani’s inference mechanism was design to evaluate input features based on fuzzy rules held in the multidimensional data model. The system is aimed at predicticting and classifying flood risk severity levels. The perception of emergency risk management is very important in modern society. Therefore, this work provides a framework for the practical applications of data mining techniques and tools to emergency risk management. The work would assist to identify locations with significant flood risk.

Design of a hybrid intelligent system for the management of flood disaster risks