A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment
Abstract
:1. Introduction
2. Related Research
3. Materials and Methods
3.1. Study Area Characteristic
3.2. Data Acquisition, Classification and Standardization
- W: Weight given by respondents to each factor and range between 1 and 5,
- A: Maximum weight, in this case, A = 5,
- N: Number of respondents.
3.2.1. Description of the Selected Indicators
Environmental Indicators
Social Indicators
Economic Indicators
Physical Indicators
3.3. Transferring Layers to IDRISI Software
3.4. Analytic Network Process (ANP) Approach
3.4.1. Step A: ANP Model Construction and Problem Structuring
3.4.2. Step B: Paired Comparisons
3.4.3. Step C: Super Matrix Calculation
3.4.4. Step D: Selection
3.5. Artificial Neural Network
4. Results
4.1. Applying ANP for Training Site
4.2. Calculating Vulnerability Index Score for Training Site
4.3. Applying Multi-Layer Perceptron (MLP) Network for Earthquake Vulnerability Map (EVM)
4.4. Investigating the Accuracy of the Obtained Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Number | Year | Fatalities | Source | Description |
---|---|---|---|---|
1 | 634 | - | B | |
2 | 694 | - | B | |
3 | 746 | - | B | |
4 | 838 | - | B | |
5 | 849 | - | B | |
6 | 858 | - | B-A | Half of the town was destroyed |
7 | 868 | - | B | |
8 | 949 | - | B | |
9 | 1020 | - | B | |
10 | 1040 | - | B | |
11 | 1042 | 40,000 | A-B | Most of the important structures were destroyed. |
12 | 1272 | - | B | Many houses were destroyed in Tabriz. |
13 | 1273 | 250 | A | 18 aftershocks occurred in the first 24 h. Aftershocks continued for 4 months. |
14 | 1314 | - | B | |
15 | 1345 | - | A | No destruction occurred. |
16 | 1441 | - | B | This event was probably related to Wan-Nimroud earthquake that had been associated with volcanic activity. |
17 | 1527 | - | B | |
18 | 1633 | - | B | |
19 | 1640 | - | B | |
20 | 1641 | 1200 | A-B | The earthquake happened between Tabriz and Urmia Lake. Osko, Khosrowshah, and Dehkharghan were destroyed. |
21 | 1668 | - | B | Tabriz and some parts of Caucasus were destroyed. |
22 | 1717 | 700 | A | Midnight earthquake destroyed more than 4000 houses. |
23 | 1721 | - | A-B | Many houses and monuments were destroyed. Rapture length reached to more than 55 km (between Tykmehdash and Tabriz) A lot of damages occurred in the area between Shebli and Gharebaba. |
24 | 1727 | 70,000 | A-B | |
25 | 1779 | 100,000 | B | |
28 | 1843 | - | A-B | A series of earthquakes of different intensities have been recorded by Khanikov. |
29 | 1870 | - | B | An earthquake shook Tabriz City. |
30 | 1896 | - | B | An earthquake was felt in Tabriz. |
31 | 1896 | - | B | An earthquake was felt in Tabriz. |
Criteria | Indicators- Description | Scholars |
---|---|---|
Physical | 1. Building Density 2. Residential Density 3. Distance to road network 4. Distance to open space 5. Distance to police stations 6. Size of building block density 7. Building’s floor density 8. Quality of buildings density 9. Distance to relief centers 10. Distance to Danger centers 11. Buildings’ Materials density 12. Age of building density 13. Commercial building density | [72,73,74,75,76,77,78,79,80,81,82] |
Environmental | 14. Percent of Slope 15. Features of geology 16. Average acceleration values for medium magnitude earthquake 17. Aspect 18. Drainage 19. Distance to fault | [82,83,84,85] |
Social | 20. Population density 21. Percent of population under 6 years old 22. Household density 23. Percent of population over 65 years old 24. Literate People density 25. Ratio of widows in female population 26. Women with many children 27. Percent population with health insurance coverage 28. Percent of the population with telephone access 29. Percent females participating in labor force 30. Percent of housing units with bathroom 31. Percent of housing units with kitchen 32. Percent of population with disability 33. Percent of population who are migrants 34. Dentist per 100,000 population 35. Specialist physician per 100,000 population 36. Hospital beds per 100,000 population 37. Dwelling population density on census unit | [86,87,88,89,90,91,92,93] |
Economic | 38. Employed People density 39. Unemployed people density 40. Degree of occupancy per room 41. Room area per person 42. private residences with more than 5 rooms 43. Percent of homeownership 44. Per capita household income | [93,94,95] |
Criteria | Indicators-Description | Abbreviation | Scale | Source | Scholars |
---|---|---|---|---|---|
Physical | Building Density | BD | 1.2500 | 4 | [96,97,98,99,100,101,102,103,104,105] |
Residential Density | RD | 1.2500 | 4 | ||
Distance to the road network | DRN | 1.2500 | 1 | ||
Distance to open space | DOS | 1.2500 | 1 | ||
Size of building block density | 4 | ||||
Building’s floor density | SBBD | 1.2500 | 4 | ||
Quality of buildings density | BFD | 1.2500 | 5 | ||
Distance to relief centers | QBD | 1.2500 | 1 | ||
Distance to Danger centers | DRC | 1.2500 | 1 | ||
Buildings’ Materials density | DDC | 1.2500 | 4 | ||
Age of building density | BMD | 1.2500 | 4 | ||
Commercial building density | ABD | 1.2500 | 4 | ||
CBD | 1.2500 | 4 | |||
Environmental | Percent of Slope | PS | - | 6 | [105,106] |
Features of geology | PG | 1.100,000 | 2 | ||
Distance to fault | DF | 1.100,000 | 1 | ||
Social | Population density | PD | 1.10,000 | 3 | [107,108,109,110] |
Household density | HD | 1.10,000 | 3 | ||
Literate People density | LPD | 1.10,000 | 3 | ||
Economic | Employed People density | EPD | 1.2500 | 3 | [108,109,110,111,112] |
Unemployed people density | UPD | 1.2500 | 3 |
Criteria | Environmental | Physical | Social | Economic | [W21] |
---|---|---|---|---|---|
Environmental | 1 | 0.89 | 1.38 | 1.15 | 0.24 |
Physical | 1.11 | 1 | 1.55 | 1.29 | 0.61 |
Social | 0.72 | 0.64 | 1 | 0.83 | 0.08 |
Economic | 0.86 | 0.77 | 1.19 | 1 | 0.07 |
Vulnerability Dimension | Indicators | Ideal | Normalized |
---|---|---|---|
Social | PD | 0.496 | 0.0511 |
HHD | 0.337 | 0.0347 | |
LPD | 0.088 | 0.0091 | |
Economic | EMD | 0.373 | 0.0384 |
UEPD | 0.120 | 0.0124 | |
CBD | 0.040 | 0.0042 | |
Physical | DRN | 0.443 | 0.0456 |
DDC | 0.494 | 0.0509 | |
DRC | 0.380 | 0.0392 | |
DOS | 0.494 | 0.0509 | |
RD | 0.842 | 0.0867 | |
BD | 0.710 | 0.0731 | |
ABD | 0.368 | 0.0379 | |
BMD QBD | 0.781 | 0.0804 | |
SBBD | 0.295 | 0.0304 | |
B | 0.390 | 0.0402 | |
BFD | 0.661 | 0.0681 | |
Environmental | DF | 1 | 0.1029 |
FG | 0.797 | 0.0821 | |
PS | 0.598 | 0.0616 |
Group | Parameter | Value |
---|---|---|
Input specifications | Avrg. training pixels per class | 500 |
Avrg. testing pixels per class | 500 | |
Network topology | Hidden layers | 1 |
Nodes | 10 | |
Input Layers Node | 20 | |
Output Layer Nodes | 5 | |
Training parameter | Automatic training | Yes |
Dynamic learning rate | Yes | |
Start Learning rate | 0.001 | |
End learning rate | 0.0006 | |
Momentum factor | 0.5 | |
Stopping criteria | RMS | 0.1534 |
Iterations | 10000 | |
Accuracy rate | 90.00 |
Vulnerability | Area (km2) | Hectares | Percentage (%) |
---|---|---|---|
Very High | 3.05 | 305.35 | 1.19 |
High | 14.33 | 1433.05 | 5.60 |
Moderate | 87.29 | 8729.49 | 34.11 |
Low | 134.96 | 13,496.37 | 52.74 |
Very Low | 16.25 | 1625.14 | 6.35 |
Total | 255.894 | 25,589.40 | 100.00 |
Zone | Vulnerability | Area (km2) | Hectares | Percentage (%) | Zone | Vulnerability | Area (km2) | Hectares | Percentage (%) |
1 | Very High | 1.177 | 177.99 | 5.74 | 2 | Very High | 0.167 | 16.79 | 0.44 |
1 | High | 3.04 | 304.13 | 9.80 | 2 | High | 2.22 | 222.38 | 5.80 |
1 | Moderate | 7.20 | 720.69 | 23.23 | 2 | Moderate | 5.87 | 587.55 | 15.32 |
1 | Low | 17.97 | 1797.46 | 57.94 | 2 | Low | 26.19 | 2619.24 | 68.30 |
1 | Very Low | 1.01 | 101.99 | 3.29 | 2 | Very Low | 3.88 | 388.92 | 10.14 |
Sum | 31.02 | 3102.26 | 100.00 | Sum | 38.34 | 3834.88 | 100.00 | ||
Zone | Vulnerability | Area (km2) | Hectares | Percentage | Zone | Vulnerability | Area (km2) | Hectares | Percentage |
3 | Very High | 0.05 | 5.15 | 0.14 | 4 | Very High | 0.29 | 29.72 | 1.08 |
3 | High | 1.22 | 122.99 | 3.29 | 4 | High | 7.06 | 706.21 | 25.57 |
3 | Moderate | 6.09 | 609.03 | 16.28 | 4 | Moderate | 9.86 | 986.83 | 35.73 |
3 | Low | 21.77 | 2177.86 | 58.20 | 4 | Low | 9.92 | 992.85 | 35.94 |
3 | Very Low | 8.26 | 826.83 | 22.10 | 4 | Very Low | 0.46 | 46.59 | 1.69 |
Sum | 37.41 | 3741.85 | 100.00 | Sum | 27.62 | 2762.20 | 100.00 | ||
Zone | Vulnerability | Area (km2) | Hectares | Percentage | Zone | Vulnerability | Area (km2) | Hectares | Percentage |
5 | Very High | 0.75 | 75.70 | 16.14 | 6 | High | 0.10 | 10.48 | 2.56 |
5 | High | 0.66 | 66.87 | 14.26 | 6 | Moderate | 1.69 | 169.41 | 41.41 |
5 | Moderate | 1.26 | 126.62 | 27.00 | 6 | Low | 2.24 | 224.57 | 54.90 |
5 | Low | 1.99 | 199.52 | 42.54 | 6 | Very Low | 0.04 | 4.61 | 1.13 |
5 | Very Low | 0.002 | 0.27 | 0.06 | Sum | 4.09 | 409.07 | 100.00 | |
Sum | 4.68 | 468.97 | 100.00 | ||||||
Zone | Vulnerability | Area (km2) | Hectares | Percentage | Zone | Vulnerability | Area (km2) | Hectares | Percentage |
7 | Moderate | 10.88 | 1088.19 | 28.80 | 8 | Moderate | 15.21 | 1521.90 | 43.68 |
7 | Low | 25.52 | 2552.61 | 67.55 | 8 | Low | 18.47 | 1847.70 | 53.04 |
7 | Very Low | 1.37 | 137.96 | 3.65 | 8 | Very Low | 1.14 | 114.30 | 3.28 |
Sum | 37.78 | 3778.76 | 100.00 | Sum | 34.83 | 3483.91 | 100.00 | ||
Zone | Vulnerability | Area (km2) | Hectares | Percentage | |||||
9 | Moderate | 29.17 | 2917.45 | 72.86 | |||||
9 | Low | 10.84 | 1084.37 | 27.08 | |||||
9 | Very Low | 0.02 | 2.13 | 0.05 | |||||
Sum | 40.03 | 4003.95 | 100.00 |
Zone | Vulnerability | Households | Population | Percentage | Zone | Vulnerability | Households | Population | Percentage |
1 | Very High | 18,409 | 73,291 | 19.97 | 2 | Very High | 2130 | 8524 | 2.83 |
1 | High | 23,054 | 86,647 | 23.61 | 2 | High | 13,642 | 50,333 | 16.73 |
1 | Moderate | 25,874 | 92,800 | 25.28 | 2 | Moderate | 6884 | 40,226 | 13.37 |
1 | Low | 30,862 | 111,987 | 30.51 | 2 | Low | 52,006 | 159,478 | 52.99 |
1 | Very Low | 640 | 2333 | 0.64 | 2 | Very Low | 7548 | 42,382 | 14.08 |
SUM | 98,839 | 367,058 | 100.00 | SUM | 82,210 | 300,943 | 100.00 | ||
Zone | Vulnerability | Households | Population | Percentage | Zone | Vulnerability | Households | Population | Percentage |
3 | Very High | 470 | 1929 | 0.72 | 4 | Very High | 2647 | 10,553 | 3.30 |
3 | High | 9583 | 33,376 | 12.50 | 4 | High | 51,908 | 185,501 | 58.06 |
3 | Moderate | 14,695 | 47,415 | 17.76 | 4 | Moderate | 17,562 | 69,485 | 21.75 |
3 | Low | 48,256 | 143,159 | 53.63 | 4 | Low | 13,416 | 52,047 | 16.29 |
3 | Very Low | 1408 | 41,080 | 15.39 | 4 | Very Low | 508 | 1892 | 0.59 |
SUM | 74,376 | 266,959 | 100.00 | SUM | 86,041 | 319,478 | 100.00 | ||
Zone | Vulnerability | Households | Population | Percentage | Zone | Vulnerability | Households | Population | Percentage |
5 | Very High | 7436 | 31,441 | 35.99 | 6 | High | 358 | 1132 | 3.82 |
5 | High | 5351 | 22,755 | 26.05 | 6 | Moderate | 6391 | 20,718 | 69.84 |
5 | Moderate | 4768 | 19,860 | 22.74 | 6 | Low | 2233 | 7816 | 26.35 |
5 | Low | 3298 | 13,294 | 15.22 | SUM | 8982 | 29,666 | 100.00 | |
SUM | 20,853 | 87,350 | 100.00 | 82,210 | 300,943 | ||||
Zone | Vulnerability | Households | Population | Percentage | Zone | Vulnerability | Households | Population | Percentage |
7 | Moderate | 132 | 1570 | 8.58 | 8 | Moderate | 90 | 488 | 66.67 |
7 | Low | 4803 | 13,822 | 75.58 | 8 | Low | 32 | 145 | 19.81 |
7 | Very Low | 62 | 2896 | 15.84 | 8 | Very Low | 18 | 89 | 12.16 |
SUM | 4997 | 18,288 | 100.00 | SUM | 140 | 732 | 100.00 | ||
Zone | Vulnerability | Households | Population | Percentage | |||||
9 | Moderate | 2022 | 7586 | 100.00 | |||||
SUM | 2022 | 7586 | 100.00 |
VAR00002 | VAR00003 | ||
---|---|---|---|
VAR00002 | Pearson Correlation | 1 | 0.976 ** |
Sig. (2-tailed) | 0.001 | ||
N | 6 | 6 | |
VAR00003 | Pearson Correlation | 0.976 ** | 1 |
Sig. (2-tailed) | 0.001 | ||
N | 6 | 6 |
VAR00001 | VAR00002 | ||
---|---|---|---|
VAR00001 | Pearson Correlation | 1 | 0.940 ** |
Sig. (2-tailed) | 0.005 | ||
N | 6 | 6 | |
VAR00002 | Pearson Correlation | 0.940 ** | 1 |
Sig. (2-tailed) | 0.005 | ||
N | 6 | 6 |
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Share and Cite
Alizadeh, M.; Ngah, I.; Hashim, M.; Pradhan, B.; Pour, A.B. A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment. Remote Sens. 2018, 10, 975. https://doi.org/10.3390/rs10060975
Alizadeh M, Ngah I, Hashim M, Pradhan B, Pour AB. A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment. Remote Sensing. 2018; 10(6):975. https://doi.org/10.3390/rs10060975
Chicago/Turabian StyleAlizadeh, Mohsen, Ibrahim Ngah, Mazlan Hashim, Biswajeet Pradhan, and Amin Beiranvand Pour. 2018. "A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment" Remote Sensing 10, no. 6: 975. https://doi.org/10.3390/rs10060975