Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition, Classification and Standardization
- g: weight given by the Relative Importance Index (R) of experts for each indicator, ranging from 1 to 5;
- A: maximum weight, in this case A = 5;
- N: number of experts.
2.3. Processing Layers in the IDRISI Software Using SOM for Supervised Classification
2.4. Methods
2.4.1. Self-Organizing Map (SOM)
Competition
Synaptic Adaption
Cooperation
2.4.2. Multilayer Perceptron (MLP)
2.5. Strategy Development for Hazard Mitigation
2.5.1. SWOT Analysis
The Evaluation Factors
- Step 1. The opportunities and risks and then the strengths and vulnerabilities of the city can be assessed by recognizing the external and internal variables.
- Step 2. Solicit experts’ opinions through a questionnaire, with a weighted coefficient (between 0 to 1) being assigned to each factor in a way that the total of the assigned weighted coefficients equals one.
- Step 3. Based on the analysis of opportunities, threats and strengths and weaknesses, a score of 1 to 4 is allocated for each of these factors for the city. The number 4 means that the reaction was perfect, and 1 shows that the reaction was very weak. The interpretation of each of these scores can be as follows: 4 (excellent reaction), 3 (good reaction), 2 (bad and negative reaction), 1 (very bad reaction).
- Step 4. The factor weight is multiplied by its score of efficiency to get the final value for each factor. Once the cumulative score of each element is determined, they are summarized to determine the total weighted IFE and EFEE scores.
- Step 5. The total weighted score is calculated which is at least 1 and at the most 4. The average score for the cities is 2.5. For IFE, if this score value is below 2.5, it indicates that the strengths were not greater than the weaknesses. If it was more than 2.5, the strengths overshadowed the weaknesses [86]. On the other hand, for EFE, if this value is below 2.5, it means that the opportunities were not greater than the threats; if it was above 2.5, then the opportunities overshadowed the threats [87].
Developing Strategies Using SWOT
- Aggressive strategies: capitalizing the most on using environmental opportunities by harnessing the strengths of the city.
- Competitive strategies: using the strengths of the city for avoiding threats.
- Conservative strategies: for using the potential advantages which are hidden in environmental opportunities in order to compensate for the weaknesses of the city.
- Defensive strategies: for minimizing the losses from threats and weaknesses.
2.5.2. The Strategic Planning Matrix
- Step 1. In the left column in the matrix of the QSPM, the external factor opportunities and risks and inner strength factors and weaknesses of the city are identified. This information is collated directly from the IFE and EFE matrices.
- Step 2. A score is allocated for each critical factor. These scores are related to the IFE and EFE matrices and are added to the second column, next to the critical success factors.
- Step 3. By considering the second stage of the formulation (i.e., integration and combination), the possible and applicable strategies are considered and added to the row at the top of the QSPM matrix.
- Step 4. Finally, the attraction score (AS) is determined. It is defined as the numerical value which captures the relative attraction of each strategy. By simultaneously considering critical success factors and the attraction scoring, the following central question needs to be answered: Does this factor have any effect on choosing any of the four strategy types mentioned above? If the answer to this question is positive, then this strategy is compared with this key factor. Strategies should then be evaluated considering their relative attraction scores as being either one of the following:
- Step 5. The total score of attraction is calculated. To evaluate the relative intensity of that solution, the attraction score of each element in each row is multiplied. A high score indicates a high attraction for that strategy [95].
3. Results
3.1. Earthquake Vulnerability Map (EVM) Benerated by Adopting the SOM Method
3.2. Earthquake Vulnerability Map (EVM) Generated by the MLP Method
3.3. Validation
Hazard Mitigation Strategies for Zone One
3.4. SWOT Analysis
3.4.1. External Factor Evaluation (EFE)
3.4.2. Internal Factor Evaluation (IFE)
3.4.3. Internal and External Matrices (IE)
3.5. Developing an Earthquake Hazard Mitigation Plan
3.5.1. SWOT Plans
3.5.2. QSPM Strategies
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ANN | Artificial Analytic Network |
SWOT | Strength Weaknesses Opportunities Threats |
QSPM | Quantitative Strategic Planning Matrix |
GIS | Geography Information System |
EVMs | Earthquake Vulnerability Maps |
SOM | Self-Organizing Map |
MLP | Multilayer Perceptron |
RBV | Residential Building Vulnerability |
PV | Population Vulnerability |
NTF | North Tabriz Fault |
DEN | Density |
DIS | Distance |
PER | Percent |
IFE | Internal Factor Evaluation |
EFE | External Factor Evaluation |
SO | Strength–Opportunities |
ST | Strength–Threats |
WT | Weaknesses–Threats |
WO | Weaknesses–Opportunities |
TAS | Total Attraction Score |
AS | Attraction Score |
ERA | Earthquake Risk Assessment |
Appendix A. Relative Importance Index of Urban Earthquake Vulnerability Indicators
Indicators |
1. Percent of population under 6 years old |
2. Residential building density |
3. Buildings floor density |
4. Percent of population with telephone access |
5. Distance to police station |
6. Average acceleration value for medium magnitude earthquake |
7. Percent female participating in labour force |
8. Percent of population over 65 years old |
9. Distance to open space |
10. Aspect |
11. Distance to relief centers |
12. Commercial buildings density |
13. Percent of population with disability |
14. Household density |
15. Room area per person |
16. Employee people density |
17. Percent of homeownership |
18. Percent of population with the health insurance coverage |
19. Percent of population who are migrants |
20. Features of geology |
21. Population density |
22. Percent of housing unit with kitchen |
23. Buildings density |
24. Size of buildings density |
25. Percent of slope |
26. Ratio of widows in female population |
27. Per capita household income |
28. Unemployed people density |
29. Drainage |
30. Distance to fault |
31. Quality of buildings density |
32. Age of buildings density |
33. Percent of housing unit with bathroom |
34. Distance to road network |
35. Degree of occupancy per room |
36. Distance to danger centers |
37. Private residence with more than five rooms |
38. Dentists per 100,000 population |
39. Specialist physicians per 100,000 population |
40. Literate people density |
41. Hospital beds per 100,000 population |
42. Dwelling population density on census unit |
43. Buildings materials density |
44. Women with many children |
Appendix B. Selected Indicators and Omitted Indicators Highlighted in Red
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Group | Parameter | Default Value |
---|---|---|
Sampling in band images | Column interval | 3 |
Row interval | 7 | |
Network parameters | Output layer neuron | 45 × 45 = 2025 |
Initial neighborhood radius | 64 × 64 | |
Min. learning rate | 0.5 | |
Max. learning rate | 1 | |
Fine-tuned parameters | Min. gain term | 0.1 |
Max. gain term | 0.8 | |
Fine-tuned rule | LVQ2 | |
Fine-tunined epochs | 80 | |
Classification specification | Output hard classification map | Yes |
Display feature map | Yes | |
Algorithm for unknown pixels | Min mean distance |
Group | Parameter | Default Value |
---|---|---|
Input specifications | Avg. training pixels per class | 500 |
Avg. test pixels per class | 500 | |
Network topology | Hidden layers | 1 |
Training parameters | Automatic training | Yes |
Dynamic learning rate | Yes | |
Learning rate | 0.0006 | |
End learning rate | 0.000625 | |
Momentum factor | 0.5 | |
Sigmoid constant “a” | 1.0 | |
Stopping criteria | RMS | 0.001 |
Iterations | 10,000 | |
Accuracy rate | 90.00 |
Perspective | Opportunities (O) | Threats (T) |
---|---|---|
Strengths (S) | (Area 1) Aggressive strategies: Harnessing opportunities by using strengths (SO). | (Area 3) Conservative strategies: Using the potential advantages which are hidden in environmental opportunities to compensate for the weaknesses of the city (WO). |
Weaknesses (W) | (Area 2) Competitive strategies: Capitalising on the strengths for preventing the threats (ST). | (Area 4) Defensive strategies: Minimizing the losses from threats and weaknesses (WT). |
Vulnerability | Very High | High | Moderate | Low | Very Low | % |
---|---|---|---|---|---|---|
Zone one | 6.96 | 12.40 | 30.16 | 48.75 | 1.72 | 100.00 |
Zone two | 1.64 | 10.25 | 18.67 | 52.95 | 16.49 | 100.00 |
Zone three | 0.17 | 5.77 | 16.04 | 66.42 | 11.60 | 100.00 |
Zone four | 1.37 | 27.65 | 39.71 | 27.65 | 0.00 | 100.00 |
Zone five | 17.56 | 16.74 | 23.26 | 42.41 | 0.02 | 100.00 |
Zone six | 0.00 | 0.00 | 75.14 | 24.86 | 0.00 | 100.00 |
Zone seven | 0.00 | 0.00 | 24.7 | 75.40 | 0.00 | 100.00 |
Zone eight | 1.05 | 0.00 | 39.68 | 56.08 | 3.19 | 100.00 |
Zone nine | 0.00 | 0.00 | 76.10 | 23.75 | 0.15 | 100.00 |
Vulnerability | Very High | High | Moderate | Low | Very Low | Percent |
---|---|---|---|---|---|---|
Zone one | 23.77 | 27.08 | 24.17 | 24.24 | 0.74 | 100.00 |
Zone two | 9.28 | 32.42 | 26.58 | 16.91 | 14.82 | 100.00 |
Zone three | 1.04 | 22.24 | 34.67 | 34.41 | 7.64 | 100.00 |
Zone four | 13.62 | 41.57 | 30.42 | 14.38 | 0.00 | 100.00 |
Zone five | 37.73 | 31.40 | 14.31 | 16.55 | 0.00 | 100.00 |
Zone six | 0.00 | 0.00 | 89.10 | 10.90 | 0.00 | 100.00 |
Zone seven | 0.00 | 0.00 | 0.72 | 99.62 | 0.00 | 100.00 |
Zone eight | 0.00 | 0.00 | 78.14 | 21.86 | 0.00 | 100.00 |
Zone nine | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 100.00 |
Vulnerability | Very High | High | Moderate | Low | Very Low | Percent | Number of Buildings |
---|---|---|---|---|---|---|---|
Zone one | 13.61 | 21.80 | 34.05 | 29.40 | 1.12 | 100.00 | 43,790 |
Zone two | 5.46 | 30.96 | 34.77 | 14.44 | 14.37 | 100.00 | 20,563 |
Zone three | 0.05 | 1.60 | 4.00 | 3.50 | 0.84 | 100.00 | 21,900 |
Zone four | 3.24 | 38.04 | 47.70 | 11.02 | 0.00 | 100.00 | 33,766 |
Zone five | 35.91 | 26.56 | 19.06 | 18.46 | 0.00 | 100.00 | 5543 |
Zone six | 0.00 | 0.00 | 86.97 | 13.04 | 0.00 | 100.00 | 3765 |
Zone seven | 0.00 | 0.00 | 8.40 | 91.60 | 0.00 | 100.00 | 1363 |
Zone eight | 0.00 | 14.40 | 34.12 | 49.01 | 2.46 | 100.00 | 505 |
Zone nine | 0.00 | 0.00 | 78.96 | 21.04 | 0.01 | 100.00 | 1793 |
Vulnerability | Area (m2) | Hectares | % |
---|---|---|---|
Very High | 3,053,543 | 306 | 2 |
High | 14,330,487 | 1432 | 6 |
Medium | 87,294,910 | 8730 | 34 |
Low | 134,963,682 | 13,497 | 53 |
Very Low | 16,251,353 | 1626 | 5 |
SUM | 255,893,975 | 25,591 | 100 |
Vulnerability | Very High | High | Moderate | Low | Very Low | Percent |
---|---|---|---|---|---|---|
Zone one | 5.74 | 9.80 | 23.23 | 57.94 | 3.29 | 100.00 |
Zone two | 0.44 | 5.80 | 15.32 | 68.30 | 10.14 | 100.00 |
Zone three | 0.14 | 3.29 | 16.28 | 58.20 | 22.10 | 100.00 |
Zone four | 1.08 | 25.57 | 35.73 | 35.94 | 1.69 | 100.00 |
Zone five | 16.14 | 14.26 | 27.00 | 42.54 | 0.06 | 100.00 |
Zone six | 0.00 | 2.56 | 41.41 | 54.90 | 1.13 | 100.00 |
Zone seven | 0.00 | 0.00 | 28.80 | 67.55 | 3.65 | 100.00 |
Zone eight | 0.00 | 0.00 | 0.00 | 53.04 | 3.28 | 100.00 |
Zone nine | 0.00 | 0.00 | 72.86 | 27.08 | 0.05 | 100.00 |
Vulnerability | Very High | High | Moderate | Low | Very Low | Percent |
---|---|---|---|---|---|---|
Zone one | 19.97 | 23.61 | 25.28 | 30.51 | 0.64 | 100.00 |
Zone two | 2.83 | 16.73 | 13.37 | 52.99 | 14.08 | 100.00 |
Zone three | 0.72 | 12.50 | 17.76 | 53.63 | 15.39 | 100.00 |
Zone four | 3.30 | 58.06 | 21.75 | 16.29 | 0.59 | 100.00 |
Zone five | 35.99 | 26.05 | 22.74 | 15.22 | 0.00 | 100.00 |
Zone six | 0.00 | 03.82 | 69.84 | 26.35 | 0.00 | 100.00 |
Zone seven | 0.00 | 0.00 | 8.58 | 75.58 | 15.84 | 100.00 |
Zone eight | 0.00 | 0.00 | 66.67 | 19.81 | 12.16 | 100.00 |
Zone nine | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 100.00 |
Vulnerability | Very High | High | Moderate | Low | Very Low | Percent | Number of Buildings |
---|---|---|---|---|---|---|---|
Zone one | 11.33 | 17.80 | 31.01 | 38.96 | 0.90 | 100.00 | 43,200 |
Zone two | 01.49 | 19.16 | 34.88 | 39.21 | 5.26 | 100.00 | 20,491 |
Zone three | 0.43 | 21.47 | 51.85 | 14.54 | 11.71 | 100.00 | 22,200 |
Zone four | 2.67 | 44.18 | 40.03 | 12.54 | 0.59 | 100.00 | 34,567 |
Zone five | 33.32 | 24.71 | 26.74 | 15.22 | 0.01 | 100.00 | 5564 |
Zone six | 0.00 | 4.85 | 55.42 | 39.58 | 0.16 | 100.00 | 3476 |
Zone seven | 0.00 | 0.00 | 4.97 | 90.56 | 4.47 | 100.00 | 1364 |
Zone eight | 0.00 | 0.00 | 48.9 | 51.10 | 0.00 | 100.00 | 408 |
Zone nine | 0.00 | 0.00 | 65.4 | 34.60 | 0.00 | 100.00 | 1790 |
SOM | MLP | ||
---|---|---|---|
SOM | Pearson Correlation | 1 | 0.997 |
Sig. (2-tailed) | 0.00 | ||
N | 6 | 6 | |
MLP | Pearson Correlation | 0.997 | 1 |
Sig. (2-tailed) | 0.00 | ||
N | 6 | 6 |
SOM | MLP | ||
---|---|---|---|
SOM | Pearson correlation | 1 | 0.921 |
Sig. (two-tailed) | 0.026 | ||
N | 5 | 5 | |
MLP | Pearson correlation | 0.921 | 1 |
Sig. (two-tailed) | 0.026 | ||
N | 5 | 6 |
Least Vulnerable | Most Vulnerable | |
---|---|---|
Q-average | 0.89 | 0.95 |
Zone & Regions | Population in (2012) (Person) | Residential Area (Hectare) | Urban Constructed Area (Hectare) | Total Area (Hectare) | Net Population Density (Person/Hectare) | Gross Population Density (Person/Hectare) | |
---|---|---|---|---|---|---|---|
Constructed Space | Total Area | ||||||
1 | 2,011,302.1 | 552.4 | 1546.9 | 1546.9 | 383.2 | 137.3 | |
1-1 | 78,698.3 | 171.9 | 333.2 | 333.2 | 458.3 | 236.2 | |
1-2 | 59,392.2 | 148.8 | 573.8 | 573.8 | 399.1 | 104.4 | |
1-3 | 73,212.4 | 231.6 | 639.8 | 639.8 | 316.2 | 114.1 |
Opportunity | Weight | Effectiveness Score | Final Score | |
1 | Large number of young demographics in the population, particularly men, and using their potentials | 0.052 | 3 | 0.156 |
2 | Presence of urban management | 0.052 | 3 | 0.156 |
3 | Forming strengthening committees in East Azerbaijan Province and conducting studies on a number of public buildings and schools | 0.078 | 4 | 0.312 |
4 | Granting of title deeds and official tenure right | 0.052 | 3 | 0.156 |
5 | Granting of renewal incentive rules | 0.052 | 3 | 0.156 |
6 | Feasibility of transforming abandoned buildings and empty spaces into needed land use types in the area such as green spaces | 0.052 | 3 | 0.156 |
7 | Role of religious places in social interactions, training and communication | 0.026 | 3 | 0.078 |
Threats | Weight | Effectiveness Score | Final Score | |
1 | Fine-grained lots | 0.105 | 1 | 0.105 |
2 | Located on lands with a gradient of more than 5% | 0.105 | 1 | 0.105 |
3 | This zone is located on fault lines | 0.131 | 1 | 0.131 |
4 | Population density around gas stations of this zone | 0.078 | 1 | 0.078 |
5 | Narrowness of thoroughfares | 0.052 | 2 | 0.104 |
6 | Low distance to service centers of Tabriz (gas post, water reserves, petrol and gas station) | 0.105 | 1 | 0.105 |
7 | Neglect of old buildings and probability of their destruction and leaving of large debris and blocking of thoroughfares | 0.052 | 2 | 0.104 |
Total | 1 | 1.902 |
Strengths | Weight | Effectiveness Score | Final Score | |
1 | High percentage of employees and low unemployment | 0.0454 | 3 | 0.1362 |
2 | Proper access to relief facilities such as fire station and hospital | 0.09 | 4 | 0.36 |
3 | High access to urban open space | 0.068 | 4 | 0.272 |
4 | Organizing specialized earthquake committee in the city of Tabriz and holding sessions every two months | 0.068 | 3 | 0.204 |
5 | Holding earthquake and safety maneuver at schools | 0.068 | 3 | 0.204 |
6 | People tendency toward housing renovation | 0.0227 | 3 | 0.681 |
7 | Allocating constructional budget to renovate infrastructure and development and renovation of drinking water network for various zones | 0.0454 | 3 | 0.1362 |
Weaknesses | Weight | Effectiveness Score | Final Score | |
1 | High population and building density | 0.09 | 1 | 0.09 |
2 | High density of household | 0.068 | 1 | 0.068 |
3 | Structural degradation of buildings due to their old ages | 0.09 | 1 | 0.09 |
4 | Low quality of buildings in terms of materials | 0.09 | 1 | 0.09 |
5 | High density of residential building and number of floors | 0.068 | 1 | 0.068 |
6 | Poor access to the city center | 0.0454 | 2 | 0.0908 |
7 | Low renewal rates in residential building | 0.068 | 2 | 0.136 |
8 | Delay in organizing and enabling as well as failure in planning in decision makings relevant to urban problems | 0.068 | 2 | 0.136 |
Total | 1 | 2.7622 |
Strengths (S) | Weaknesses (W) | |
|
| |
Opportunities (O) | (SO) | (WO) |
|
|
|
Threat (T) | (ST) | (WT) |
|
|
|
Strategies | |||||||||
---|---|---|---|---|---|---|---|---|---|
ST1 | ST2 | ST3 | ST4 | ||||||
Factors | Weight | AS | TAS | AS | TAS | AS | TAS | AS | TAS |
Strength | |||||||||
1 | 0.0454 | 2 | 0.0908 | 2 | 0.0908 | 1 | 0.0454 | 2 | 0.0908 |
2 | 0.0900 | 4 | 0.36 | 1 | 0.0900 | 1 | 0.0900 | 4 | 0.36 |
3 | 0.0680 | 2 | 0.136 | 1 | 0.0680 | 4 | 0.2720 | 3 | 0.204 |
4 | 0.0680 | 3 | 0.204 | 3 | 0.2040 | 3 | 0.2040 | 3 | 0.204 |
5 | 0.0680 | 3 | 0.204 | 1 | 0.0680 | 1 | 0.0680 | 1 | 0.0680 |
6 | 0.0227 | 2 | 0.0454 | 3 | 0.0681 | 3 | 0.0681 | 2 | 0.0454 |
7 | 0.0454 | 3 | 0.1362 | 2 | 0.0908 | 1 | 0.0454 | 1 | 0.0454 |
Weakness | |||||||||
1 | 0.0900 | 1 | 0.0900 | 1 | 0.0900 | 2 | 0.18 | 1 | 0.0900 |
2 | 0.0680 | 1 | 0.0680 | 1 | 0.0680 | 2 | 0.136 | 1 | 0.0680 |
3 | 0.0900 | 2 | 0.1800 | 2 | 0.1800 | 2 | 0.18 | 1 | 0.0900 |
4 | 0.0900 | 2 | 0.1800 | 2 | 0.1800 | 2 | 0.18 | 1 | 0.0900 |
5 | 0.0680 | 1 | 0.0680 | 1 | 0.0680 | 2 | 0.136 | 1 | 0.0680 |
6 | 0.0454 | 2 | 0.0908 | 1 | 0.0454 | 3 | 0.1362 | 1 | 0.0454 |
7 | 0.0680 | 1 | 0.0680 | 2 | 0.1360 | 2 | 0.136 | 2 | 0.1360 |
8 | 0.0680 | 2 | 0.1360 | 2 | 0.1360 | 3 | 0.204 | 3 | 0.2040 |
Opportunity | |||||||||
1 | 0.052 | 2 | 0.104 | 2 | 0.104 | 2 | 0.104 | 1 | 0.052 |
2 | 0.052 | 3 | 0.156 | 3 | 0.156 | 3 | 0.156 | 3 | 0.156 |
3 | 0.078 | 2 | 0.156 | 4 | 0.312 | 3 | 0.234 | 2 | 0.156 |
4 | 0.052 | 2 | 0.104 | 1 | 0.052 | 2 | 0.104 | 2 | 0.104 |
5 | 0.052 | 2 | 0.104 | 2 | 0.104 | 1 | 0.052 | 1 | 0.052 |
6 | 0.052 | 2 | 0.104 | 2 | 0.104 | 3 | 0.156 | 1 | 0.052 |
7 | 0.026 | 2 | 0.052 | 1 | 0.026 | 1 | 0.026 | 1 | 0.026 |
Threat | |||||||||
1 | 0.105 | 1 | 0.105 | 1 | 0.105 | 4 | 0.420 | 1 | 0.105 |
2 | 0.105 | 1 | 0.105 | 2 | 0.210 | 1 | 0.105 | 4 | 0.420 |
3 | 0.131 | 4 | 0.524 | 3 | 0.393 | 2 | 0.262 | 4 | 0.524 |
4 | 0.078 | 2 | 0.156 | 1 | 0.078 | 2 | 0.156 | 1 | 0.078 |
5 | 0.052 | 2 | 0.104 | 1 | 0.052 | 3 | 0.156 | 2 | 0.104 |
6 | 0.105 | 2 | 0.210 | 2 | 0.210 | 3 | 0.315 | 1 | 0.105 |
7 | 0.052 | 3 | 0.156 | 4 | 0.208 | 2 | 0.104 | 2 | 0.104 |
Total | 4.190 | 3.690 | 4.430 | 3.480 |
Strategies | Total Attractiveness Score |
---|---|
ST3—Optimal consideration of environmental phenomena in designing and locating of sites | 4.43 |
ST1—Reinforcement of the crisis confrontation subsystems in the city | 4.19 |
ST2—Revitalization and renewal of land uses in historic areas | 3.69 |
ST4—Proper design of open spaces inside urban areas of the zone by creating hierarchy in their design | 3.48 |
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Alizadeh, M.; Zabihi, H.; Rezaie, F.; Asadzadeh, A.; Wolf, I.D.; Langat, P.K.; Khosravi, I.; Beiranvand Pour, A.; Mohammad Nataj, M.; Pradhan, B. Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. Remote Sens. 2021, 13, 4519. https://doi.org/10.3390/rs13224519
Alizadeh M, Zabihi H, Rezaie F, Asadzadeh A, Wolf ID, Langat PK, Khosravi I, Beiranvand Pour A, Mohammad Nataj M, Pradhan B. Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. Remote Sensing. 2021; 13(22):4519. https://doi.org/10.3390/rs13224519
Chicago/Turabian StyleAlizadeh, Mohsen, Hasan Zabihi, Fatemeh Rezaie, Asad Asadzadeh, Isabelle D. Wolf, Philip K Langat, Iman Khosravi, Amin Beiranvand Pour, Milad Mohammad Nataj, and Biswajeet Pradhan. 2021. "Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model" Remote Sensing 13, no. 22: 4519. https://doi.org/10.3390/rs13224519
APA StyleAlizadeh, M., Zabihi, H., Rezaie, F., Asadzadeh, A., Wolf, I. D., Langat, P. K., Khosravi, I., Beiranvand Pour, A., Mohammad Nataj, M., & Pradhan, B. (2021). Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model. Remote Sensing, 13(22), 4519. https://doi.org/10.3390/rs13224519