1. Introduction
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]. Earthquakes are natural events that can also have long-term social and economic adverse impacts on societies. The vulnerability of cities and settlement areas to natural disasters such as earthquakes is to some extent a consequence of the role of human behaviors and is strongly related to the importance of planning systems in reducing the damaging effects of natural disasters [
2].
In developed countries the financial casualties of natural disasters are generally high while human casualties are low; however, in developing countries this is the reverse, indicating better planning in developed countries [
3]. It is not possible to accurately control or predict natural disasters such as earthquakes, or how particular measures can help in making cities less vulnerable to a disaster event [
4].
The complex nature and variable effects that disaster events can have on societies in general, and specifically 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]. Much of the physical and economic damage caused by such incidents is often attributed to a lack of planning and weaknesses in building standards and infrastructure codes [
6]. As cities are more vulnerable to catastrophes due to their high population, densities of buildings and infrastructure, ways of assessing seismic physical vulnerability in urban areas are needed [
7]. With the expansion of the physical and economy of urban communities, the need to reduce risks has gradually become not only a reliable factor in controlling risk, but has also become more important as a vulnerability mitigation plan. One of the managerial measures that can play a significant role in reducing the damage caused by natural disasters is the zoning of natural hazards. In order to reduce earthquake risks, comprehensive studies are needed to identify the impacts of earthquakes in urban and rural areas and identify areas with high vulnerability [
8]. Dimensions affecting the risk of cities can broadly be divided into three groups, i.e., environmental, physical and social aspects. The most important natural factors affecting earthquake hazards are fault lines, lithology, slope degree, and proximity to faults. The physical dimension is the most tangible dimension of the role of urban planning in reducing the impact of earthquakes. One of the most important physical dimensions is the urban structure and urban land use [
9].
Different areas of the city are vulnerable to crises due to the type of population living there [
10]. Criteria such as distance and proximity to faults, horizontal acceleration of land, number of floors in buildings, remoteness, proximity to treatment centers, building density, distance from hazardous sites and facilities, population density, building materials, and adjacent land uses have a significant impact on reducing or increasing earthquake damage [
11].
To identify earthquake risk areas, various studies have been conducted in recent decades. Some studies with a strong focus on holistic risk analyses include a seismic risk assessment, e.g., the authors of [
12] who introduced a new approach to seismic risk assessment and stated that in order to achieve effective risk management, it is necessary to identify social and environmental vulnerabilities in addition to determining physical and economic damages. The readiness of citizens in three New Zealand cities three years after the Christchurch earthquake was examined in [
13]. Their results showed to what degree people were aware of the likelihood of danger and vulnerability in their area of residence. The whole of Japan was zoned in [
14] using statistical methods and principal component analysis (PCA) based on gravity, earthquake, active fault and seismic parameters, and in [
15] risk assessment using an artificial neural network (ANN) was performed, which addressed the lack of accurate validation methods which can be one of the shortcomings of these studies. Using social, environmental and physical metrics, another study performed post-earthquake hazard modeling and studied the health of individuals and the threat of poisonous insects, using a hierarchical analysis process model to weigh the criteria [
16].
Many studies have also used only a limited number of criteria and have carried out a one-dimensional seismic vulnerability assessment, including: [
17], which assessed the economic damages of highway bridges in Campania, Italy and has used statistical methods of updating ground motion prediction and fragility (their results show that the structural dependence of land movement is an important factor in the economic damage caused by earthquakes); [
18], which used an expert system containing specialized knowledge for masonry structures in assessing the seismic vulnerability of old buildings in Sri Lanka; [
19], which performed a seismic vulnerability assessment by focusing on one of the Romanian cities subject to earthquake, using multi-criteria analysis and a number of physical and social criteria, the results of which identified 385 earthquake-prone structures, as well as the decision-making process to reduce the damage. Other methods and models have been used to map natural hazard vulnerability in some current studies, including certainty factors (CF) [
20], ANN [
21,
22], logistic regression (LR) [
23], support vector machine (SVM) [
24,
25,
26], convolutional neural network (CNN) [
27], ordered weight averaging (OWA) [
4], fuzzy quantifier algorithm [
28], adaptive neuron-fuzzy inference system (ANFIS) [
29,
30], and different multiple criteria decision analysis (MCDA) models [
31] such as the analytic hierarchy process (AHP) [
32,
33,
34] and the analytical network process (ANP) [
35,
36]. Several models and techniques have also been integrated and combined to produce more efficient hybrid models [
37,
38,
39]. Ghorbanzadeh et al. (2019) [
22] integrated a hazard susceptibility index with a social/infrastructural vulnerability index using a geographic information system multi-criteria decision making (GIS-MCDM). Their hazard susceptibility index was generated based on mostly environmental conditioning factors, such as the slope angle and distance to streams using an ANN. They emphasized different types of land use and construction, like industrial, residential, and recreation areas for creating the infrastructural vulnerability index. The dataset of infrastructural vulnerability index was combined with the social vulnerability factors, e.g., population, age, and family information.
Given the importance of earthquake-related issues and their effects, the main purpose of this study was to develop a model for earthquake vulnerability assessment in Sanandaj City, in order to eliminate the shortcomings of previous studies, such as: not using all the criteria that affect the earthquake; modeling with individual models containing uncertainties; lack of final validation for the final models; and so on. In addition, estimation of vulnerable population and risk assessment of different areas of the city in critical times was based on different degrees such as low, moderate and high and many other goals of this study. In this study, in order to determine the vulnerability areas and to prepare earthquake susceptibility map, five models, namely OWA, fuzzy logic, AHP, ANP and logistic regression (LR), and four hybrid models, namely (ANP-AHP)-fuzzy, (ANP-AHP)-OWA, and OWA-LR, and fuzzy-LR, were used. Accordingly, the average weight of the AHP and ANP models was used to construct the OWA and fuzzy models. Also, for simplicity, we used the (ANP-AHP)-fuzzy (A-fuzzy) and (ANP-AHP)-OWA (A-OWA) hybrid models to make training sites, so that two training datasets would be created and the OWA-LR and fuzzy-LR hybrid models would be built separately. Finally, the seismic relative index (SRI) validation method was used for the initial validation of two A-fuzzy and A-OWA hybrid models and relative operating characteristic (ROC) curves for four hybrid models and frequency ratio (FR) [
40] methods were used to validate the final hybrid models of fuzzy-LR (FLR) and OWA-LR (OLR).
6. Discussion and Conclusions
Identifying areas prone to seismic vulnerability is one of the most important issues in crisis management in cities. Although many methods and techniques have been developed to assess earthquake hazards around the world so far, the goals of all these studies are to reduce the economic losses and resulting losses. Researchers have previously focused on individual models for vulnerability, assessing the location of natural disasters such as earthquakes. However, many hybrid models have recently been used to model natural hazards [
37,
40,
81]. The purpose of this study was to introduce new hybrid learning models for seismic vulnerability mapping in Sanandaj City. In fact, the basis of this study was the application of hybrid models and synthetic neural network training in combination to predict the location of earthquake hazards. Fifteen factors were selected as effective factors in measuring seismic vulnerability. The purpose of using hybrid models in this study was to provide detailed analyses and eliminates some of the unreliable results in individual models. Artificial neural networks can be used to provide classified maps in predicting natural disasters such as earthquakes with good accuracy worldwide. After building the A-OWA and A-fuzzy hybrid models and creating training databases, the fuzzy-LR and OWA-LR hybrid models were implemented. Comparing the accuracy of hybrid models using the area under curve (AUC) and FR shown, the A-OWA hybrid model with AUC = 0.855 is more accurate than the A-fuzzy hybrid model with AUC = 0.805.
In addition, by comparing the accuracy of hybrid models, the OWA-LR model with AUC = 0.9 and lower standard error (STD error = 0.0677) and FR = 12 index value in class of high sensitivity, than the Fuzzy-LR hybrid model with AUC = 0.85 and FR = 10.30 has higher accuracy. However, other models such as A-fuzzy and A-OWA have also been powerful models, with high predictive accuracy of earthquake sensitivity in this study (
Table 8). In principle, two types of hybrid models have been used to construct training data for integration with artificial neural networks in this study. Therefore, the models used in the study may have lower accuracy when used with individual modes. In this study, all hybrid models provided accurate predictions regarding the seismic vulnerability assessment. Nevertheless, based on the results, the high accuracy of the hybrid A-OWA model and the lower standard error (STD error = 0.083) increase the accuracy of the training database. Therefore, the validation of the training database constructed and the final accuracy of the combined models by the damaged points have provided reliable accuracy in this study.
According to the results of the OWA-LR hybrid model, as an optimal model, 44% of Sanandaj city space is in the low and very low vulnerability spectrum and 25% is in the moderate vulnerability spectrum. In addition, about 32% of the city area is in vulnerable classes and the highest vulnerability is in urban areas 1 and 2. One of the main causes of seismic vulnerability in the north of Sanandaj is physical and social criteria, such as worn-out urban texture, population density, number of floors, distance from health centers, and so on. Meanwhile environmental factors such as proximity to the fault are more influential in vulnerability to the south of the city (Zone 3). Therefore, attention should be paid to the strengthening of worn-out urban texture and better access to health centers in urban areas 1 and 2 of Sanandaj City. In addition, consideration of construction in open areas of Zone 3 based on environmental factors (distance from the fault, lithology) will reduce the amenity of earthquake damage.
In order to reduce the damage caused by an earthquake, an earthquake vulnerability map can show suitable areas for building and housing and be used by planners. Human science and technology at the present moment cannot cope with the earthquake and prevent it from happening, so man must use his science and technology to adapt to environmental hazards. Due to access restrictions, it is not possible to thoroughly investigate all the factors affecting the city′s vulnerability, but with field reviews and expert opinions, this study attempted to address this issue with the highest number of indicators. Therefore, in this study, three factors affecting earthquake vulnerability in the three physical, environmental and social dimensions were used. Due to the limitations of each of the ANP and AHP models in weight determination this study used a novel approach to order the average weight of both models, to obtain the optimal weight of each agent. Finally, in order to improve the AHP and ANP models and to build two hybrid models, the earthquake vulnerability map of Sanandaj was produced by combining the weight of multi-criteria decision-making models AHP and ANP and combining this with two fuzzy and OWA models. In addition, a new hybrid approach was used to train the LR neural network model to overcome the limitations of access to seismic vulnerability points in Sanandaj city. Thus, the A-OWA and A-fuzzy hybrid models, as the basis for building educational databases, can overcome the limitations of data access in such studies. In order to ensure the accuracy of the hybrid models, the seismic relative index validation method based on 10 vulnerability points recorded by the researchers in this study in urban areas was used. As a result, by combining the logistic regression (LR) model with MCDA-fuzzy and MCDA-OWA hybrid models, two maps using these models were also prepared. After modeling, the accuracy of the first A-OWA and A-fuzzy hybrid models in building the training database were compared with the accuracy of the new hybrid models (OWA-LR and Fuzzy-LR) built through them. The results of the evaluation of the models used showed that the A-OWA hybrid database and hybrid model OWA-LR with the Area under curve (AUC = 0.855 and AUC = 0.9) are the most accurate models for mapping earthquake vulnerability. Therefore, it can be concluded that the accuracy of the educational database used in the study influenced the accuracy of the final model. Therefore, the new hybrid model OWA-LR is introduced as the most desirable model in this study. According to this model, 17.3% of the study area is very highly vulnerable. Results of the potential earthquake vulnerability map showed that Zone 1 and parts of Zone 2 are more sensitive to the earthquake and more possible to be damaged in these areas than other zones. In Zones 1 and 2, in addition to the influence of natural factors on seismic vulnerability, physical and human factors caused by high population density and households, poor quality of building materials, low width of passages have played a greater role. Although there are necessary and appropriate post-crisis service facilities such as hospitals and fire stations in these areas, inadequate performance of buildings and pedestrian networks will hamper relief operations. Due to the distribution of high vulnerability locations in the north and west of the city, it is necessary to consider these areas for earthquake resilience, earthquake preparedness and risk reduction operations. In urban Zone 3 environmental factors such as proximity to major faults and high slope also had more impact on seismic vulnerability. Also improvement in crisis management projects in the area, by taking measures such as rebuilding buildings, preventing non-regular construction, taking into account environmental conditions for the construction of houses in Zone 3, building a fire station and an emergency medical center in the vulnerable areas of Sanandaj city it is essential.