A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes

Timely and accurate identification and classification of victims in earthquakes is crucial for improving rescue efficiency, but available information about victims and their surrounding environment is often vague and imprecise. Rescue wings is a web-based intelligent system that monitors and analyzes the statuses of identified victims to support decision making in earthquake rescue operations. A key component of the system is a Takagi-Sugeno (T-S)-type neuro-fuzzy network for disaster-stricken population classification, and one important input of the network is the output of another T-S-type recurrent neuro-fuzzy network for recognizing the movement patterns from the users´ temporal location data. A novel differential biogeography-based optimization (DBBO) algorithm is developed for parameter optimization of both the main network and the subnetwork. Experimental results have shown that the hybrid neuro-fuzzy network exhibits good classification performance in comparison with some other typical neuro-fuzzy networks, and the proposed DBBO outperforms some state-of-the-art evolutionary algorithms in network learning. The solution approach has also been successfully applied to the 2013 Ya´an Earthquake in Sichuan province, China.