A New NeuroFuzzy Approach for Postearthquake Roa

Authors: M Izadi A Mohammadzadeh A Haghighattalab

Publish Date: 2017/03/06

Volume: 45, Issue: 6, Pages: 965-977

PDF Link

Abstract

Tracking damaged roads and damage level assessment after earthquake is vital in finding optimal paths and conducting rescue missions In this study a new approach is proposed for the semiautomatic detection and assessment of damaged roads in urban areas using preevent vector map and both pre and postearthquake QuickBird images In this research damage is defined as debris of damaged buildings presence of parked cars and collapsed limbs of trees on the road surface Various texture and spectral features are considered and a genetic algorithm is used to find the optimal features Subsequently a support vector machine classification is applied to the optimal features to detect damages The proposed method was tested on QuickBird pansharpened images from the Bam earthquake and the results indicate that an overall accuracy of 93 and a kappa coefficient of 091 were achieved for the damage detection step Finally an appropriate fuzzy inference system FIS and also an “Adaptive NeuroFuzzy Inference System” are proposed for the road damage level assessment These results show that ANFIS has achieved overall accuracy of 94 in comparison with 88 of FIS The obtained results indicate the efficiency and accuracy of the NeuroFuzzy systems for road damage assessment

Keywords: