Abstract
The main purpose of the present study was to mathematically integrate different decision support systems to enhance the accuracy of seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered to be a catastrophe that poses a serious threat to human infrastructures at different scales. Factors affecting seismic vulnerability were identified in three different dimensions; social, environmental, and physical. Our computer-based modeling approach was used to create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA) and multiple criteria decision analysis-multi-criteria evaluation (MCDA-MCE) for training the multi-criteria evaluation–logistic regression (MCE–LR) and fuzzy-logistic regression (fuzzy-LR) hybrid model. The resulting dataset was validated using the seismic relative index (SRI) method and ten damaged spots from the study area, in which the MCDA-MCE model showed higher accuracy. The hybrid learning models of MCE-LR and fuzzy-LR were implemented using both resulting datasets for seismic vulnerability mapping. Finally, the resulting seismic vulnerability maps based on each model were validation using area under curve (AUC) and frequency ratio (FR). Based on the accuracy assessment results, the MCDA-MCE hybrid model (AUC = 0.85) showed higher accuracy than the fuzzy-MCDA model (AUC = 0.80), and the MCE-LR hybrid model (AUC = 0.90) resulted in more accurate vulnerability map than the fuzzy-LR hybrid model (AUC = 0.85). The results of the present study show that the accuracy of modeling and mapping seismic vulnerability in our case study area is directly related to the accuracy of the training dataset.
Highlights
During the 20th century, more than 1100 destructive earthquakes occurred in various parts of the world, resulting in the deaths of more than 1,500,000 people, of which about 90% were due to insufficient engineering and safety standards for buildings [1]
In order to construct two A-MCE and A-fuzzy hybrid models, the average weight of analytical network process (ANP) and analytic hierarchy process (AHP) decision models was calculated, based on which the sensitivity classes were ranked in each layer (Table 5)
After was constructing two training thesets and A-fuzzy hybrid models, the logistic regression (LR) model run each time through databases one of the using training using input values
Summary
During the 20th century, more than 1100 destructive earthquakes occurred in various parts of the world, resulting in the deaths of more than 1,500,000 people, of which about 90% were due to insufficient engineering and safety standards for buildings [1]. The complex nature and variable effects that disaster events can have on societies in general, and cities, can partly be attributed to the variable nature of hazard distribution (especially seismic intensity), the number of people exposed, environmental vulnerability, and the degree of resistance of communities [5]. The attribution of each member of the reference set to a specific subset member is not definitive; that is, it cannot be stated with certainty whether the member belongs to this set This uncertainty assessment is done by assigning a number between 0 and 1 to this member. The numbers and information that need to be processed will become fuzzy sets and numbers
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