Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo
Abstract
:1. Introduction
2. Case Study and Materials
3. Methodology
3.1. Create a Seismic Hazard Map (PGA)
3.2. Landsat Classification
3.3. Identifying Population
3.4. GIS Interpolation Method
3.5. Machine Learning Methods and Evaluation Metrics
3.5.1. Employed Linear and Non-Linear Regression Models
3.5.2. Learning and Testing Process
3.5.3. Performance Evaluation and Best Model Determination
4. Results
4.1. Identifying Historical Urban Area
4.2. Identifying Seismic Hazard Map with Different Methods
4.3. Urban Reas Exposure to Seismic Hazard
4.4. Population Exposure to Seismic Hazard
5. Discussion
- This paper’s findings are in line with earlier research showing that the interpolation kriging method can improve spatial prediction accuracy in different data types [95,97,99], While others show that Inverse Distance Weighted and Kriging are nearly the same [95,96]. Kriging method was the best method. The Kriging method gives superior interpolation for unmeasured quantities [100,101,102].
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Details | Source | |
---|---|---|---|
Landsat | year 1992 | Landsat 4–5 TM, Date Acquired: 3 October | United States Geological Survey, Downloaded from earth explorer tool at https://earthexplorer.usgs.gov/ (accessed on 1 March 2022). Geotechnical data is provided by the General Authority of Educational Buildings, Egypt and Sarhan (2012) |
PGA | year 2005 | Geotechnical boreholes | |
Population | year 1990 | GHS population grid, derived from GPW4, (1990) | European Commission Downloaded from https://ghsl.jrc.ec.europa.eu/ghs_pop.php (accessed on 1 March 2022). [19] |
Class Name | Area (Ha) | Area (%) |
---|---|---|
Desert | 290,843 | 66.63% |
Green | 85,279 | 19.54% |
Urban | 55,085 | 12.62% |
Water | 5287 | 1.21% |
Total | 436,494 | 100% |
Class Value | Desert | Green | Urban | Water | Total | U_Accuracy | Kappa |
---|---|---|---|---|---|---|---|
Desert | 320.00 | 3.00 | 10.00 | 0.00 | 333.00 | 0.96 | 0.00 |
Green | 9.00 | 85.00 | 4.00 | 0.00 | 98.00 | 0.87 | 0.00 |
Urban | 6.00 | 0.00 | 57.00 | 0.00 | 63.00 | 0.90 | 0.00 |
Water | 1.00 | 0.00 | 1.00 | 8.00 | 10.00 | 0.80 | 0.00 |
Total | 336.00 | 88.00 | 72.00 | 8.00 | 504.00 | 0.00 | 0.00 |
P_Accuracy | 0.95 | 0.97 | 0.79 | 1.00 | 0.00 | 0.93 | 0.00 |
Kappa | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.87 |
Accuracy | ML Method | IDW Method | Kriging Method | Natural Method | Spline Method | TopoToR Method | Trend Method |
---|---|---|---|---|---|---|---|
R2 value | 0.96 | 0.93 | 0.95 | 0.88 | 0.82 | 0.95 | 0.19 |
MSE | 72.91 | 106.45 | 82.11 | 179.73 | 288.81 | 89.29 | 1329.97 |
RMSE | 8.54 | 10.32 | 9.06 | 13.41 | 16.99 | 9.45 | 36.47 |
Intensity | PGA (Gal) | Perceived Shaking | Potential Damage |
---|---|---|---|
0 | <0.8 | Not felt | None |
1 | 0.8–2.5 | Very light | None |
2 | 2.5–8 | Light | None |
3 | 8–25 | Weak | None |
4 | 25–80 | Moderate | Very light |
5 | 80–250 | Strong | Light |
6 | 250–400 | Violent | Moderate |
7 | >400 | Extreme | Heavy |
Perceived Shaking | ML Method | GIS Interpolation Methods | Potential Damage | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IDW | Kriging | Natural | Spline | TopoToR | Trend | ||||||||||
Ha | % | Ha | % | Ha | % | Ha | % | Ha | % | Ha | % | Ha | % | ||
Weak | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 1714 | 3.12 | 927 | 1.69 | 0 | 0.00 | None |
Moderate | 3259 | 5.93 | 2975 | 5.41 | 3180 | 5.78 | 1316 | 2.76 | 3264 | 5.94 | 2221 | 4.04 | 1019 | 1.85 | Very Light |
Strong | 50,114 | 91.15 | 50,884 | 92.55 | 50,233 | 91.37 | 45,136 | 94.47 | 48,070 | 87.43 | 50510 | 91.87 | 53,960 | 98.15 | Light |
Violent | 1606 | 2.92 | 1120 | 2.04 | 1567 | 2.85 | 1323 | 2.77 | 1930 | 3.51 | 1321 | 2.40 | 0 | 0.00 | Moderate |
Extreme | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 0 | 0.00 | 1 | 0.002 | 0 | 0.00 | 0 | 0.00 | Heavy |
Urban Area | 54,979 | 100 | 54,979 | 100 | 54,979 | 100 | 47,776 | 100 | 54,979 | 100 | 54,979 | 100 | 54,979 | 100 | Urban Area |
Perceived Shaking | ML Method | GIS Interpolation Methods | Potential Damage | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IDW | Kriging | Natural | Spline | TopoToR | Trend | ||||||||||
1000 Capita | % | 1000 Capita | % | 1000 Capita | % | 1000 Capita | % | 1000 Capita | % | 1000 Capita | % | 1000 Capita | % | ||
Weak | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 150 | 1 | 76 | 1 | 0 | 0 | None |
Moderate | 179 | 1 | 164 | 1 | 179 | 1 | 57 | 0 | 323 | 3 | 104 | 1 | 95 | 1 | Very Light |
Strong | 12,380 | 96 | 12,513 | 97 | 12,412 | 96 | 11,804 | 97 | 12,055 | 93 | 12,438 | 96 | 12,810 | 99 | Light |
Violent | 345 | 3 | 228 | 2 | 313 | 2 | 300 | 2 | 377 | 3 | 287 | 2 | 0 | 0 | Moderate |
Extreme | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Heavy |
Population (1000) | 12,904 | 100 | 12,904 | 100 | 12,904 | 100 | 12160 | 100 | 12,904 | 100 | 12,904 | 100 | 12,904 | 100 | Population (1000) |
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Hamdy, O.; Gaber, H.; Abdalzaher, M.S.; Elhadidy, M. Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo. Sustainability 2022, 14, 10722. https://doi.org/10.3390/su141710722
Hamdy O, Gaber H, Abdalzaher MS, Elhadidy M. Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo. Sustainability. 2022; 14(17):10722. https://doi.org/10.3390/su141710722
Chicago/Turabian StyleHamdy, Omar, Hanan Gaber, Mohamed S. Abdalzaher, and Mahmoud Elhadidy. 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo" Sustainability 14, no. 17: 10722. https://doi.org/10.3390/su141710722