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Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model

Author

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  • Sèna Donalde Dolorès Marguerite Deguenon

    (Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 526, Benin
    Centre for Coastal Management, University of Cape Coast, Cape Coast PMB TF0494, Ghana)

  • Castro Gbêmêmali Hounmenou

    (Laboratoire de Biomatématiques et d’Estimations Forestières (LABEF), Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 1525, Benin)

  • Richard Adade

    (Africa Centre of Excellence in Coastal Resilience—Centre for Coastal Management, Department of Fisheries and Aquatic Sciences, School of Biological Sciences, University of Cape Coast, Cape Coast PMB TF0494, Ghana)

  • Oscar Teka

    (Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 526, Benin)

  • Ismaila Imorou Toko

    (Laboratoire de Cartographie (LaCarto), Institut de Géographie, de l’Aménagement du Territoire et de l’Environnement (IGATE), University of Abomey-Calavi, Calavi BP 698, Benin)

  • Denis Worlanyo Aheto

    (Africa Centre of Excellence in Coastal Resilience—Centre for Coastal Management, Department of Fisheries and Aquatic Sciences, School of Biological Sciences, University of Cape Coast, Cape Coast PMB TF0494, Ghana)

  • Brice Sinsin

    (Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 526, Benin)

Abstract

Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.

Suggested Citation

  • Sèna Donalde Dolorès Marguerite Deguenon & Castro Gbêmêmali Hounmenou & Richard Adade & Oscar Teka & Ismaila Imorou Toko & Denis Worlanyo Aheto & Brice Sinsin, 2023. "Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16001-:d:1281387
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    1. Ismail Mondal & SK Ariful Hossain & Jatisankar Bandyopadhyay & Hamad Ahmed Altuwaijri & Anirjita Das & Felix Jose & Nilanjan Saha & Mukhiddin Juliev, 2025. "Potential effects of projected global sea level rise on Sundarbans mangrove wetland ecosystem: insights from SLAMM and hybrid machine learning models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(6), pages 14879-14912, June.

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