Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks
Abstract
:1. Introduction
Background
2. Case Study
3. Methodology
3.1. Methodology Flowchart
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- The data required for the analysis were collected from various sources and pre-processed in GIS.
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- Several flood-influencing factors (FIFs) covering hydrological, geomorphological, environmental, topographical, and meteorological conditions, based on the actual characteristics of the study area, were selected. Input data for each factor were resampled in the GIS environment as raster data with 10 m spatial resolution, resulting in 4,306,822 cells (2194 columns, 1963 rows), and reprojected in the reference system WGS 84/UTM zone 32 N. We classify the FIFs into seven static flood-conditioning factors (FCFs: elevation, slope, topographic wetness index, distance to streams, drainage density, land use/land cover and geology) and one dynamic flood-triggering factor (FTF, October 2015 rainfall). Due to the diverse nature of each factor, all the thematic maps were reclassified on a scale from 1 to 5 (rating score), where 1 refers to a very low (or negligible) level of influence/susceptibility to flooding and 5 to a very high level. The approach of setting a priori the number of susceptibility classes is also employed in similar studies for identifying flood-prone areas [38,42,56,60,70,71,72,73].
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- The AHP technique was employed to determine the relative weights of the flood-influencing factors.
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- The thematic map layers were superposed in GIS using the weights calculated with the AHP technique. A flood susceptibility condition map (FSC) was created by combining the seven FCFs with their weights, and a flood susceptibility assessment map (FSA) was obtained by combining the seven FCFs and the one FTF (rainfall) with their weights. The pixel value of each output map (FSi) was obtained using the following equation (weighted linear combination, WLC, [74]):
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- A sensitivity analysis was applied to both the FSC and FSA maps to evaluate the influence of uncertainties of the input factors’ weights on the derived flood susceptibility maps.
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- To validate the flood susceptibility zonation method, historical flood-related damage sites on the railway infrastructure from the railway company and official flood inundation maps from the competent authorities were used.
3.2. Flood-Influencing Factors (FIFs)
3.2.1. Elevation (E)
3.2.2. Slope Angle (S)
3.2.3. Topographic Wetness Index (TWI)
3.2.4. Distance to Streams (DS)
3.2.5. Drainage Density (DD)
3.2.6. Geology (G)
3.2.7. Land Use Land Cover (LULC)
3.2.8. Cumulative Two-Day Rainfall (C2DR)
3.3. Calculation of Weights
4. Map Interpolation and Sensitivity Analysis
Sensitivity Analysis
5. Results and Discussion
5.1. Spatial Variation of Flood Susceptibility at Basin Level
5.2. Impact of Factors on the Flood Susceptibility Distribution
5.3. Validation of the Methodology
5.4. Limitations and Future Research Directions
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- The inherent subjectivity in expert assessments within the analytic hierarchy process (AHP) can lead to biases in the weighting of criteria, even though pairwise comparisons are used to promote consistency. The influence of this bias can be assessed with sensitivity analysis conducted on weights.
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- The accuracy and resolution of the input data could introduce uncertainties, particularly in regions with complex topography or diverse land cover. Depending on the scope of the susceptibility map, different scales could be required for different applications.
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- The present application considers the rainfall forcing from the 2015 autumn event only. There is the potential to include present climate (rainfalls with different durations and intensity) and future projections where available.
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- The extent and nature of present land use and land cover (LULC) do not account for historical modifications or predict future changes. Future developments of the territory should be included if the corresponding information is available.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Analytical Hierarchy Process (AHP)
Appendix A.1. Development of the Pairwise Comparison Matrix
Intensity of Importance | Values for Reciprocal Scale | Definition |
---|---|---|
1 | 1 | Equal importance |
2 | 1/2 | Equal to moderate importance |
3 | 1/3 | Moderate importance |
4 | 1/4 | Moderate to strong importance |
5 | 1/5 | Strong importance |
6 | 1/6 | Strong to very strong importance |
7 | 1/7 | Very strong importance |
8 | 1/8 | Very to extremely strong importance |
9 | 1/9 | Extreme importance |
Pairwise Comparison Matrix | |||||||
---|---|---|---|---|---|---|---|
Factor | S | DS | TWI | E | DD | G | LULC |
Slope (S) | 1 | 1 | 2 | 2 | 3 | 4 | 5 |
Distance from streams (DS) | 1 | 1 | 2 | 2 | 3 | 4 | 5 |
Topographic wetness index (TWI) | 1/2 | 1/2 | 1 | 1 | 2 | 3 | 4 |
Elevation (E) | 1/2 | 1/2 | 1 | 1 | 2 | 3 | 4 |
Drainage density (DD) | 1/3 | 1/3 | 1/2 | 1/2 | 1 | 2 | 3 |
Geology (G) | 1/4 | 1/4 | 1/3 | 1/3 | 1/2 | 1 | 2 |
Land use land cover (LULC) | 1/5 | 1/5 | 1/4 | 1/4 | 1/3 | 1/2 | 1 |
Column sum | 3.78 | 3.78 | 7.08 | 7.08 | 11.83 | 17.5 | 24 |
Pairwise Comparison Matrix | ||||||||
---|---|---|---|---|---|---|---|---|
Factor | C2DR | S | DS | TWI | E | DD | G | LULC |
Rainfall (C2DR) | 1 | 1 | 1 | 2 | 2 | 4 | 5 | 6 |
Slope (S) | 1 | 1 | 1 | 2 | 2 | 3 | 4 | 5 |
Distance from streams (DS) | 1 | 1 | 1 | 2 | 2 | 3 | 4 | 5 |
Topographic wetness index (TWI) | 1/2 | 1/2 | 1/2 | 1 | 1 | 2 | 3 | 4 |
Elevation (E) | 1/2 | 1/2 | 1/2 | 1 | 1 | 2 | 3 | 4 |
Drainage density (DD) | 1/4 | 1/3 | 1/3 | 1/2 | 1/2 | 1 | 2 | 3 |
Geology (G) | 1/5 | 1/4 | 1/4 | 1/3 | 1/3 | 1/2 | 1 | 2 |
Land use land cover (LULC) | 1/6 | 1/5 | 1/5 | 1/4 | 1/4 | 1/3 | 1/2 | 1 |
Column sum | 4.62 | 4.78 | 4.78 | 9.08 | 9.08 | 15.83 | 22.5 | 30 |
Appendix A.2. Normalized Pairwise Comparison Matrix and Computation of the Criteria Weights
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- divide each element of the matrix A by its column total (obtaining the so-called normalized pairwise comparison matrix),
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- average over each row of the resulting normalized pairwise comparison matrix (i.e., divide the sum of the normalized elements of each row by the number of criteria n), thus obtaining an estimate of the criteria weights.
Normalized Pairwise Comparison Matrix | ||||||||
---|---|---|---|---|---|---|---|---|
Factor | S | DS | TWI | E | DD | G | LULC | Weight |
Slope (S) | 0.26 | 0.26 | 0.28 | 0.28 | 0.25 | 0.23 | 0.21 | 0.25 |
Distance from streams (DS) | 0.26 | 0.26 | 0.28 | 0.28 | 0.25 | 0.23 | 0.21 | 0.25 |
Topographic wetness index (TWI) | 0.13 | 0.13 | 0.14 | 0.14 | 0.17 | 0.17 | 0.17 | 0.15 |
Elevation (E) | 0.13 | 0.13 | 0.14 | 0.14 | 0.17 | 0.17 | 0.17 | 0.15 |
Drainage density (DD) | 0.09 | 0.09 | 0.07 | 0.07 | 0.08 | 0.11 | 0.13 | 0.09 |
Geology (G) | 0.07 | 0.07 | 0.05 | 0.05 | 0.04 | 0.06 | 0.08 | 0.06 |
Land use land cover (LULC) | 0.05 | 0.05 | 0.04 | 0.04 | 0.03 | 0.03 | 0.04 | 0.04 |
Normalized Pairwise Comparison Matrix | |||||||||
---|---|---|---|---|---|---|---|---|---|
Factor | C2DR | S | DS | TWI | E | DD | G | LULC | Weight |
Rainfall (C2DR) | 0.22 | 0.21 | 0.21 | 0.22 | 0.22 | 0.25 | 0.22 | 0.20 | 0.22 |
Slope (S) | 0.22 | 0.21 | 0.21 | 0.22 | 0.22 | 0.19 | 0.18 | 0.17 | 0.20 |
Distance from streams (DS) | 0.22 | 0.21 | 0.21 | 0.22 | 0.22 | 0.19 | 0.18 | 0.17 | 0.20 |
Topographic wetness index (TWI) | 0.11 | 0.10 | 0.10 | 0.11 | 0.11 | 0.13 | 0.13 | 0.13 | 0.12 |
Elevation (E) | 0.11 | 0.10 | 0.10 | 0.11 | 0.11 | 0.13 | 0.13 | 0.13 | 0.12 |
Drainage density (DD) | 0.05 | 0.07 | 0.07 | 0.06 | 0.06 | 0.06 | 0.09 | 0.10 | 0.07 |
Geology (G) | 0.04 | 0.05 | 0.05 | 0.04 | 0.04 | 0.03 | 0.04 | 0.07 | 0.05 |
Land use land cover (LULC) | 0.04 | 0.04 | 0.04 | 0.03 | 0.03 | 0.02 | 0.02 | 0.03 | 0.03 |
Appendix A.3. Consistency Test
n | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|
RI | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
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- computing the product Aw (obtaining the so-called weighted sum vector),
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- dividing the resulting weighted sum vector by w (obtaining the so-called consistency vector),
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- averaging the elements of the consistency vector (i.e., dividing the sum of the elements of the consistency vector by the number of criteria n), thus obtaining an estimate of λmax.
n | λmax | CI | RI | CR | Consistency |
---|---|---|---|---|---|
7 | 7.09 | 0.015 | 1.32 | 0.011 | CR < 0.1 (Yes) |
8 | 8.09 | 0.013 | 1.41 | 0.009 | CR < 0.1 (Yes) |
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Type of Damage | Description of the Damage |
---|---|
Obstruction of crossing structures (culvert/bridge) | At railway–stream intersections, sediment and debris carried by the flow accumulated, leading to the obstruction of crossing structures’ clearances and the failure of drainage facilities with corresponding potential infrastructural damage. |
Instability/collapse of retaining walls—masonry damage | This type of damage affected the area where the railway line passes through the town of Ponte, with buildings flanking the line structures. Instabilities and collapses of retaining walls occurred due to issues related to increased water pressures as well as flow-related erosive phenomena. |
Clogging of drainage ditches | Sediment and debris materials transported by overland flow or coming from the secondary tributaries led to the obstruction of several drainage facilities. |
Failure of embankment caused by erosion | The overflow of the Calore River and tributary creek breached the railway embankment and caused the washing away of ballast. |
Overtopping by water/mud from upstream | Masses of mud and water traveling down the slopes due to overland flow or coming from the secondary tributaries obscured several railway segments. |
Factor [Unit] | Classification | Susceptibility Level | |
---|---|---|---|
Descriptive Form | Score | ||
Elevation (E), [m a.s.l.] | 47–225 | Very high | 5 |
225–415 | High | 4 | |
415–644 | Medium | 3 | |
644–919 | Low | 2 | |
919–1385 | Very low | 1 | |
Slope (S), [°] | 0–6.6 | Very high | 5 |
6.6–12.4 | High | 4 | |
12.4–20 | Medium | 3 | |
20–30.7 | Low | 2 | |
30.7–70.8 | Very low | 1 | |
Rainfall (C2DR), [mm] | 305.1–415.4 | Very high | 5 |
252.7–305.1 | High | 4 | |
214.4–252.7 | Medium | 3 | |
165–214.4 | Low | 2 | |
77.5–165 | Very low | 1 | |
Land use land cover (LULC) | Urban areas | Very high | 5 |
Sparse urban areas | High | 4 | |
Agricultural land | Medium | 3 | |
Grassland and shrub | Low | 2 | |
Forest | Very low | 1 | |
Distance to streams (DS), [m] | 1st–2nd-order streams | ||
0–25 | Very high | 5 | |
25–50 | High | 4 | |
50–100 | Medium | 3 | |
100–150 | Low | 2 | |
>150 | Very low | 1 | |
3rd-order streams | |||
0–50 | Very high | 5 | |
50–100 | High | 4 | |
100–150 | Medium | 3 | |
150–200 | Low | 2 | |
>200 | Very low | 1 | |
4th-order streams | |||
0–100 | Very high | 5 | |
100–200 | High | 4 | |
200–300 | Medium | 3 | |
300–400 | Low | 2 | |
>400 | Very low | 1 | |
5th-order streams | |||
0–100 | Very high | 5 | |
100–300 | High | 4 | |
300–500 | Medium | 3 | |
500–700 | Low | 2 | |
>700 | Very low | 1 | |
Topographic wetness index (TWI), [-] | 12.47–23.62 | Very high | 5 |
9.00–12.47 | High | 4 | |
7.01–9.00 | Medium | 3 | |
5.56–7.01 | Low | 2 | |
1.75–5.56 | Very low | 1 | |
Geology (G) | Clay | Very high | 5 |
Sandstone and arenaceous marl | High | 4 | |
Marl and calcareous marl | Medium | 3 | |
Alluvial | Low | 2 | |
Limestone | Very low | 1 | |
Drainage density (DD), [km/km2] | 2.37–3.64 | Very high | 5 |
1.82–2.37 | High | 4 | |
1.31–1.82 | Medium | 3 | |
0.75–1.31 | Low | 2 | |
0–0.75 | Very low | 1 |
Flood-Influencing Factor | Weights of Factors (Using AHP) | ||
---|---|---|---|
Condition Map | Assessment Map | ||
Triggering factor | Rainfall (C2DR) | - | 0.219 |
Conditioning factor | Slope (S) | 0.255 | 0.201 |
Distance to streams (DS) | 0.255 | 0.201 | |
Topographic wetness index (TWI) | 0.151 | 0.116 | |
Elevation (E) | 0.151 | 0.116 | |
Drainage density (DD) | 0.092 | 0.069 | |
Geology (G) | 0.058 | 0.045 | |
Land use land cover (LULC) | 0.039 | 0.031 |
Flood Susceptibility | FSC (%) | FSCmax (%) | FSCmin (%) | FSA (%) | FSAmax (%) | FSAmin (%) |
---|---|---|---|---|---|---|
Very High | 13.9 | 14.2 | 14.1 | 12.5 | 12.1 | 12.4 |
High | 22.6 | 22.8 | 23.2 | 23.3 | 22.2 | 23 |
Medium | 30 | 29.7 | 29.2 | 29 | 29.5 | 28.8 |
Low | 20 | 20 | 19.9 | 22.6 | 23.6 | 22.3 |
Very Low | 13.6 | 13.2 | 13.6 | 12.5 | 12.7 | 13.7 |
Intersection with | ||||
---|---|---|---|---|
Damage # | Damage Type | Official Flood Inundation Map | Flood Susceptibility Condition (FSC) Map | Flood Susceptibility Assessment (FSA) Map |
1–2 | Obstruction of crossing structures | x | ✓ Very high | ✓ Very high |
3–5 | Obstruction of drainage ditches | x | ✓ Very high | ✓ Very high |
6 | Obstruction of drainage ditches | x | ✓ High | ✓ Very high |
7–10 | Wall collapse/instability | x | ✓ Very high | ✓ Very high |
11–12 | Failure of embankment caused by erosion | x | ✓ Very high | ✓ Very high |
13–18 | Overtopping by water/mud from upstream | x | ✓ Very high | ✓ Very high |
19 | Overtopping by water/mud from upstream | x | ✓ Very high | ✓ High |
20–22 | Overtopping by water/mud from upstream | x | ✓ Very high | ✓ Very high |
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Varra, G.; Della Morte, R.; Tartaglia, M.; Fiduccia, A.; Zammuto, A.; Agostino, I.; Booth, C.A.; Quinn, N.; Lamond, J.E.; Cozzolino, L. Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks. Water 2024, 16, 2592. https://doi.org/10.3390/w16182592
Varra G, Della Morte R, Tartaglia M, Fiduccia A, Zammuto A, Agostino I, Booth CA, Quinn N, Lamond JE, Cozzolino L. Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks. Water. 2024; 16(18):2592. https://doi.org/10.3390/w16182592
Chicago/Turabian StyleVarra, Giada, Renata Della Morte, Mario Tartaglia, Andrea Fiduccia, Alessandra Zammuto, Ivan Agostino, Colin A. Booth, Nevil Quinn, Jessica E. Lamond, and Luca Cozzolino. 2024. "Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks" Water 16, no. 18: 2592. https://doi.org/10.3390/w16182592
APA StyleVarra, G., Della Morte, R., Tartaglia, M., Fiduccia, A., Zammuto, A., Agostino, I., Booth, C. A., Quinn, N., Lamond, J. E., & Cozzolino, L. (2024). Flood Susceptibility Assessment for Improving the Resilience Capacity of Railway Infrastructure Networks. Water, 16(18), 2592. https://doi.org/10.3390/w16182592