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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.

Flooding is a natural disaster that causes considerable damage to different sectors and severely affects economic and social activities. The city of Saqqez in Iran is susceptible to flooding due to its specific environmental characteristics. Therefore, susceptibility and vulnerability mapping are essential for comprehensive management to reduce the harmful effects of flooding. The primary purpose of this study is to combine the Analytic Network Process (ANP) decision-making method and the statistical models of Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) for flood susceptibility mapping in Saqqez City in Kurdistan Province, Iran. The frequency ratio method was used instead of expert opinions to weight the criteria in the ANP. The ten factors influencing flood susceptibility in the study area are slope, rainfall, slope length, topographic wetness index, slope aspect, altitude, curvature, distance from river, geology, and land use/land cover. We identified 42 flood points in the area, 70% of which was used for modelling, and the remaining 30% was used to validate the models. The Receiver Operating Characteristic (ROC) curve was used to evaluate the results. The area under the curve obtained from the ROC curve indicates a superior performance of the ANP and EBF hybrid model (ANP-EBF) with 95.1% efficiency compared to the combination of ANP and FR (ANP-FR) with 91% and ANP and OWA (ANP-OWA) with 89.6% efficiency.

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