Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Used
2.3. Method
2.3.1. LULC Classification
2.3.2. Urban Growth Modeling
Selection of Driving Variables
Transition Potential Modeling
Scenario Setting for Future Urban Growth Simulation
Model Validation
2.3.3. Assessment of Potential Exposure to Urban Flooding
3. Results and Discussion
3.1. Accuracy of LULC Map Classification
3.2. Model Validation and Prediction of Future Urban Growth
3.3. Evaluation of Urban Exposure to Flooding
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Format | Data Source | Year | Resolution |
---|---|---|---|---|
DEM | Raster | BD ALTI® https://geoservices.ign.fr/bdalti, accessed on 23 February 2023 | 2022 | 25 m |
Road network | Vector | ROUTE 500® https://geoservices.ign.fr/route500, accessed on 3 March 2023 | 2021 | N/A |
Coastal boundary | Vector | LIMITE TERRE-MER https://geoservices.ign.fr/limite-terre-mer, accessed on 3 March 2023 | 2021 | N/A |
Protected zones | Vector | INPN—Données du programme ‘Espaces Protégés’ https://www.data.gouv.fr/fr/datasets/inpn-donnees-du-programme-espaces-proteges/#/resources, accessed on 10 March 2023 | 2022 | N/A |
Natural Risk Prevention Plan | Vector | Plans de Préventions des Risques naturels https://www.georisques.gouv.fr/plans-de-prevention-des-risques-naturels, accessed on 10 March 2023 | 2023 | N/A |
High-risk flood areas | Vector | Territoires à Risques important d’Inondation (TRI)—version 2 https://www.georisques.gouv.fr/donnees/bases-de-donnees/zonages-inondation-rapportage-2020, accessed on 10 March 2023 | 2020 | N/A |
Future sea level projection | CSV formatted | This data produced by the IPCC authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) https://catalogue.ceda.ac.uk/uuid/98af2184e13e4b91893ab72f301790db, accessed on 10 March 2023 | 2060/ 2100 | N/A |
Modeling Variables | Urban Growth Scenarios | ||
---|---|---|---|
Business as Usual | Environmental Protection | Strategic Urban Planning | |
Drivers of urban growth | Digital elevation model | Digital elevation model | Digital elevation model |
Slope | Slope | Slope | |
Distance to major roads | Distance to major roads | Distance to major roads | |
Distance to coast | Distance to coast | Distance to coast | |
Distance to existing urban area | Distance to existing urban area | Distance to existing urban area | |
Evidence likelihood | Evidence likelihood | Evidence likelihood | |
Constraints | Existing urban areas | Existing urban areas | Existing urban areas |
Water bodies | Water bodies | Water bodies | |
Protected natural areas | Protected natural areas | ||
Restricted urbanization zones |
Accuracy Measure | Definition | Range | Expected Minimum Threshold |
---|---|---|---|
Kno | Kno measures the overall accuracy of the simulated map by comparing the proportion of correctly predicted pixels with the anticipated proportion [59,60,61]. | −1 to 1 | >0.8 |
Kappa Kstandard | Kstandard assesses the map’s ability to achieve perfect classification [59,60,61]. | −1 to 1 | >0.8 |
Klocation | Klocation assesses the simulation’s accuracy based solely on location [59,60,61]. | −1 to 1 | >0.8 |
Quantity disagreement | Quantity disagreement represents the degree to which the predicted map fails to reflect the precise quantity of each LULC class, when compared to the reference map, not taking into account location [62]. | 0 to 100% | Overall disagreement (Quantity disagreement + Allocation disagreement) < 20% |
Allocation disagreement | Allocation disagreement represents the degree to which the predicted map fails to reflect the precise position of each LULC class [62]. | 0 to 100% | Overall disagreement (Quantity disagreement + Allocation disagreement) < 20% |
Overall agreement | Overall agreement is determined by omitting both quantity and allocation disagreement [62]. | 0 to 100% | >80% |
Area under the curve (AUC) | Area under the curve is a quantitative measure derived from the Relative Operating Characteristic curve that compares the probability of a class occurring to its actual location [63,64]. | 0 to 1 | >0.5 |
Accuracy | LULC 2006 | LULC 2019 | LULC 2022 |
---|---|---|---|
Overall accuracy (%) | 93.26 | 95.11 | 94.04 |
Kappa coefficient (K) | 0.91 | 0.93 | 0.92 |
Year | Classified Data | Reference Data | ||||
---|---|---|---|---|---|---|
Water | Forest | Other Land | Agriculture | Urban | ||
2022 | Water | 212 | 6 | 6 | 4 | 0 |
Forest | 0 | 204 | 0 | 5 | 0 | |
Other land | 3 | 0 | 207 | 2 | 8 | |
Agriculture | 1 | 1 | 0 | 400 | 13 | |
Urban | 0 | 0 | 6 | 22 | 193 | |
2019 | Water | 229 | 4 | 2 | 0 | 0 |
Forest | 3 | 224 | 0 | 3 | 0 | |
Other land | 1 | 0 | 202 | 1 | 3 | |
Agriculture | 1 | 3 | 0 | 391 | 26 | |
Urban | 0 | 1 | 4 | 11 | 180 | |
2006 | Water | 203 | 1 | 0 | 5 | 0 |
Forest | 0 | 203 | 0 | 2 | 0 | |
Other land | 0 | 0 | 217 | 3 | 7 | |
Agriculture | 1 | 2 | 0 | 365 | 26 | |
Urban | 0 | 0 | 9 | 29 | 190 |
Assessment Metrics | Urban Growth Scenarios | ||
---|---|---|---|
Business as Usual | Environmental Protection | Strategic Urban Planning | |
Kno | 89.71 | 89.72 | 89.72 |
Kappa Kstandard | 85.35 | 85.36 | 85.37 |
Klocation | 88.63 | 88.64 | 88.65 |
Quantity disagreement | 2.16 | 2.16 | 2.16 |
Allocation disagreement | 6.41 | 6.41 | 6.40 |
Overall agreement | 91.43 | 91.43 | 91.44 |
AUC | 61.77 | 62.80 | 63.78 |
Year | Total Urban Area (km2) | Urban Growth Scenario | Urban Areas Susceptible to Flooding Due to Extreme Storms (km2) | Urban Areas Susceptible to Flooding Due to Extreme Storms and Sea Level Rise (km2) |
---|---|---|---|---|
2060 | 514.45 | Business as usual | 91.8592 | 94.4719 |
Environmental protection | 78.139 | 79.6219 | ||
Strategic urban planning | 58.3806 | 59.6068 | ||
2100 | 679.87 | Business as usual | 129.434 | 225.97 |
Environmental protection | 98.8963 | 165.263 | ||
Strategic urban planning | 68.4748 | 117.047 |
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Frifra, A.; Maanan, M.; Maanan, M.; Rhinane, H. Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model. J. Mar. Sci. Eng. 2024, 12, 800. https://doi.org/10.3390/jmse12050800
Frifra A, Maanan M, Maanan M, Rhinane H. Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model. Journal of Marine Science and Engineering. 2024; 12(5):800. https://doi.org/10.3390/jmse12050800
Chicago/Turabian StyleFrifra, Ayyoub, Mohamed Maanan, Mehdi Maanan, and Hassan Rhinane. 2024. "Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model" Journal of Marine Science and Engineering 12, no. 5: 800. https://doi.org/10.3390/jmse12050800