Abstracts
Résumé
L’urbanisation rapide et non maîtrisée dans les zones basses de Pikine et Keur Massar, deux départements du Sénégal, a fortement accru la susceptibilité et l’exposition aux inondations récurrentes. Dans ce contexte, la cartographie de la susceptibilité aux inondations constitue un outil stratégique pour la planification urbaine et la réduction des risques. Cette étude vise à élaborer une carte de susceptibilité aux inondations dans les départements de Pikine et de Keur Massar, au Sénégal, en combinant l’analyse multicritère (AHP) avec des outils de Système d’Information Géographique (SIG). Six facteurs déclencheurs ont été intégrés : la pluviométrie, l’élévation, la pente, la densité de drainage, le type de sol et l’occupation du sol. Les pondérations des critères ont été établies selon la méthode de Saaty, et la carte finale a été obtenue par superposition pondérée. Les données satellitaires Landsat-8 et SRTM, analysées via Google Earth Engine, ont permis de cartographier les zones inondées sur la période 2015-2024, subdivisée en trois sous-périodes. L'indice de différence normalisée modifiée de l’eau (MNDWI) a été utilisé pour détecter les inondations, tandis que l’indice de végétation par différence normalisée (NDVI) a permis d’éliminer les faux positifs liés à la végétation. La carte de fréquence des inondations a ensuite servi à valider la carte de susceptibilité. Les résultats indiquent que 56,60 % de la superficie des départements de Keur Massar et Pikine présentent une susceptibilité élevée à très élevée. En croisant ces données avec les infrastructures, il apparaît que 67,46 % des bâtiments et plus de la moitié du réseau routier et ferroviaire sont fortement inondables. Ces vulnérabilités s’expliquent par une urbanisation non planifiée dans des zones basses, jadis réservées à la rétention d’eau. L’étude souligne l’urgence d’une gestion territoriale intégrée et résiliente.
Mots-clés :
- susceptibilité,
- inondations,
- analyse par procédé hiérarchique (AHP),
- Système d’Information Géographique (SIG),
- Google Earth Engine (GEE)
Abstract
Rapid and unplanned urban expansion in low-lying zones of Pikine and Keur Massar has significantly increased flood susceptibility and exposure. In response, flood susceptibility mapping is a strategic tool for urban planning and risk reduction. This study aims to produce a flood susceptibility map for the departments of Pikine and Keur Massar in Senegal, by combining Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS) . Six triggering factors were integrated: rainfall, elevation, slope, drainage density, soil type, and land use. Weighting of the criteria was based on the Saaty method, and the final map was generated through weighted overlay analysis. Landsat-8 and SRTM satellite data, processed via Google Earth Engine, were used to map flooded areas over the period 2015–2024, divided into three sub-periods. The Modified Normalized Difference Water Index (MNDWI) was used to detect flood, while the NDVI helped reduce false detections due to vegetation. The flood frequency map served to validate the AHP-based susceptibility results. Findings show that 56.60% of the study area’s surface exhibits high and very high susceptibility. By overlaying this data with infrastructure layers, the study reveals that 67.46% of buildings and more than half of the road and railway networks are highly flood-prone. This exposure is largely due to unplanned urban expansion into former water retention basins. The results underscore the urgent need for integrated and resilient spatial planning strategies to mitigate the growing flood risks in these densely urbanized areas. The approach provides a replicable framework for flood risk assessment and infrastructure vulnerability analysis in similar contexts.
Keywords:
- susceptibility,
- floods,
- Analytic Hierarchy Process (AHP),
- Geographic Information Systems (GIS),
- Google Earth Engine (GEE)
Appendices
Bibliographie
- Albertini, C., Gioia, A., Iacobellis, V. et Manfreda, S. (2022). Detection of surface water and floods with multispectral satellites. Remote Sensing, 14(23), 6005.
- Alvarez, R. S. et Tan, F. (2024). Combined Fluvial and Pluvial Flooding in an Urban Catchment: A Hydrodynamic Modeling Approach of Davao River, Philippines, in Proceedings of the 8th International Electronic Conference on Water Sciences, 14–16 October 2024, MDPI: Basel, Switzerland.
- Ansarifard, S., Eyvazi, M., kalantari, M., mohseni, B., Ghorbanifard, M., Moghaddam, H. J. et Nouri, M. (2024). Simulation of floods under the influence of effective factors in hydraulic and hydrological models using HEC-RAS and MIKE 21. Discover Water, 4(1), 92.
- ANSD. (2023). 5ème Recencement général de la population et de l’habitat. Agence Nationale de la Statistique et de la Démographie : Dakar, Sénégal, 1-21.
- Ashley, R. M., Balmforth, D. J., Saul, A. J. et Blanskby, J. (2005). Flooding in the future–predicting climate change, risks and responses in urban areas. Water Science and Technology , 52 (5), 265‑273.
- Barbosa, A. E., Fernandes, J. N. et David, L. M. (2012). Key issues for sustainable urban stormwater management. Water research , 46 (20), 6787‑6798.
- Cabrera, J. S. et Lee, H. S. (2019). Flood-prone area assessment using GIS-based multi-criteria analysis: A case study in Davao Oriental, Philippines. Water , 11 (11), 2203.
- Chen, Y.-R., Yeh, C.-H. et Yu, B. (2011). Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Natural hazards , 59 , 1261‑1276.
- Das, A. et Sahoo, S. N. (2025). Impact of land use and climate change on urban flooding: a case study of Bhubaneswar city in India. Natural Hazards, 1‑20.
- Dou, X., Song, J., Wang, L., Tang, B., Xu, S., Kong, F. et Jiang, X. (2018). Flood risk assessment and mapping based on a modified multi-parameter flood hazard index model in the Guanzhong Urban Area, China. Stochastic environmental research and risk assessment, 32, 1131‑1146.
- Douglas, I., Garvin, S., Lawson, N., Richards, J., Tippett, J. et White, I. (2010). Urban pluvial flooding: a qualitative case study of cause, effect and nonstructural mitigation. Journal of Flood Risk Management , 3 (2), 112‑125.
- GRS. (2010). Rapport d'évaluation des besoins post catastrophes ; Inondations urbaines à Dakar : Rapport préparé par le Gouvernement de la République du Sénégal (GRS), avec l'appui de la banque mondiale, du système des Nations Unies de la Commission Européenne, 1-84.
- Gallien, T., Sanders, B. et Flick, R. (2014). Urban coastal flood prediction: Integrating wave overtopping, flood defenses and drainage. Coastal Engineering , 91 , 18‑28.
- GFDRR. urban flood: recovery and reconstruction since 2009: Rapport World Bank’s Global facility for Disater Reduction and Recovery, 1-48.
- Goumrasa, A., Guendouz, M., Guettouche, M. S. et Belaroui, A. (2021). Flood hazard susceptibility assessment in Chiffa wadi watershed and along the first section of Algeria North–South highway using GIS and AHP method. Applied Geomatics, 13(4), 565‑585.
- Green, J., Haigh, I., Quinn, N., Neal, J., Wahl, T., Wood, M., Eilander, D., de Ruiter, M., Ward, P. et Camus, P. (2024). A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions. EGUsphere , 2024 , 1‑108.
- Hénonin, J., Russo, B., Roqueta, D. S., Sanchez-Diezma, R., Domingo, N. D. S., Thomsen, F. et Mark, O. (2010). Urban flood real-time forecasting and modelling: a state-of-the-art review (p. P028). MIKE by DHI conference, Copenhagen, Denmark.
- Hsu, T.-W., Shih, D.-S., Li, C.-Y., Lan, Y.-J. et Lin, Y.-C. (2017). A study on coastal flooding and risk assessment under climate change in the mid-western coast of Taiwan. Water , 9 (6), 390.
- Hughes, A., Vounaki, T., Peach, D., Ireson, A., Jackson, C., Butler, A., Bloomfield, J., Finch, J. et Wheater, H. (2011). Flood risk from groundwater: examples from a Chalk catchment in southern England. Journal of Flood Risk Management , 4 (3), 143‑155.
- Janssen, M. et Van Herwijnen, M. (1992). Multi-objective Decision Support for Environmental Management : Kluwer Academic Publishers, Dordrecht, p. 232.
- Jena, R. et Pradhan, B. (2020). Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment. International Journal of Disaster Risk Reduction , 50 , 101723.
- Komi, K., Neal, J., Trigg, M. A. et Diekkrüger, B. (2017). Modelling of flood hazard extent in data sparse areas: a case study of the Oti River basin, West Africa. Journal of Hydrology: Regional Studies , 10 , 122‑132.
- Lee, M.-J., Kang, J. E. et Kim, G. (2015). Application of fuzzy combination operators to flood vulnerability assessments in Seoul, Korea. Geocarto International , 30 (9), 1052‑1075.
- Mahdavi, S., Salehi, B., Huang, W., Amani, M. et Brisco, B. (2019). A PolSAR change detection index based on neighborhood information for flood mapping. Remote Sensing , 11 (16), 1854.
- Mahmoud, S. H. et Gan, T. Y. (2018). Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production , 196 , 216‑229.
- Moon, H.-T., Kim, J.-S., Chen, J., Yoon, S.-K. et Moon, Y.-I. (2024). Mitigating urban flood Hazards: Hybrid strategy of structural measures. International Journal of Disaster Risk Reduction , 108 , 104542.
- Muis, S., Güneralp, B., Jongman, B., Aerts, J. C. et Ward, P. J. (2015). Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data. Science of the Total Environment , 538 , 445‑457.
- Muneerudeen, A. (2017). Urban and landscape design strategies for flood resilience in Chennai city, [Mémoire de maîtrise].
- Nandi, I., Srivastava, P. K. et Shah, K. (2017). Floodplain mapping through support vector machine and optical/infrared images from Landsat 8 OLI/TIRS sensors: Case study from Varanasi. Water Resources Management , 31 , 1157‑1171.
- Ndiaye, M. L., Pfeifer, H.-R., Niang, S., Dieng, Y., Tonolla, M. et Peduzzi, R. (2010). Impacts de l’utilisation des eaux polluées en agriculture urbaine sur la qualité de la nappe de Dakar (Sénégal). VertigO-la revue électronique en sciences de l’environnement, 10(2).
- Ngong et Ba. (2021). Aux origines de Keur Massar. https://www.seneplus.com/societe/aux-origines-de-keur-massar
- Ozdemir, H., Sampson, C., de Almeida, G. A. et Bates, P. (2013). Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrology and Earth System Sciences, 17(10), 4015‑4030, https://doi.org/10.5194/hess-17-4015-2013.
- Pfeifer, H.-R., Amiguet, A., Brandvold, V., Daouk, S., Gueye-Girardet, A., Hitz, C., Ndiaye, M. L., Niang, S., Okuda, T. et Roberts, J. (2017). Water-related risks in the area of Dakar, Senegal: Coastal aquifers exposed to climate change and rapid urban development. Identifying Emerging Issues in Disaster Risk Reduction, Migration, Climate Change and Sustainable Development: Shaping Debates and Policies , 53‑65.
- Saaty, T. L. (1980). Analytical Hierarchy Process, McGraw Hill-Company: New York .
- Saaty, T. L. (1990a). An exposition of the AHP in reply to the paper “remarks on the analytic hierarchy process”. Management science , 36 (3), 259‑268.
- Saaty, T. L. (1990b). How to make a decision: the analytic hierarchy process. European journal of operational research , 48 (1), 9‑26.
- Samanta, S., Pal, D. K. et Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science , 8 (2), 66.
- Schumann, G., Bates, P. D., Horritt, M. S., Matgen, P. et Pappenberger, F. (2009). Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models. Reviews of Geophysics , 47 (4).
- Seleem, O., Ayzel, G., de Souza, A. C. T., Bronstert, A. et Heistermann, M. (2022). Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany. Geomatics, Natural Hazards and Risk , 13 (1), 1640‑1662.
- Shivhare, V., Kumar, A., Kumar, R., Shashtri, S., Mallick, J. et Singh, C. K. (2024). Flood susceptibility and flood frequency modeling for lower Kosi Basin, India using AHP and Sentinel-1 SAR data in geospatial environment. Natural Hazards, 120(13), 11579‑11610.
- Sy, B. (2019). Approche multidisciplinaire de l’évaluation de l’aléa d’inondation à Yeumbeul Nord, Dakar, Sénégal : la contribution de la science citoyenne.
- Sy, B., Bah, F. B. et Dao, H. (2024). Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event. Water, 16(15), 2201.
- Tehrany, M. S., Pradhan, B., Mansor, S. et Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena , 125 , 91‑101.
- Wang, X., Hou, J., Hu, G., Gao, X. et Shen, R. (2024). A multi-criteria combination approach to determine spatial intervention prioritization of urban flood based on source tracking analysis. Water Resources Management , 38 (3), 893‑914.
- Wang, Y., Chen, A. S., Fu, G., Djordjević, S., Zhang, C. et Savić, D. A. (2018). An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features. Environmental modelling & software , 107 , 85‑95.
- Xu, L., Abbaszadeh, P., Moradkhani, H., Chen, N. et Zhang, X. (2020). Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index. Remote Sensing of Environment , 250 , 112028.
- Yao, L., Chen, L., Wei, W. et Sun, R. (2015). Potential reduction in urban runoff by green spaces in Beijing: A scenario analysis. Urban Forestry & Urban Greening , 14 (2), 300‑308.
- Zhao, G., Pang, B., Xu, Z., Peng, D. et Xu, L. (2019). Assessment of urban flood susceptibility using semi-supervised machine learning model. Science of the Total Environment, 659, 940‑949.

