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Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling

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  • Angeliki Peponi

    (Geomodlab, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal
    Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha-Suchdol 16500, Czechia
    Centre of Geographical Studies, Universidade de Lisboa; Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal)

  • Paulo Morgado

    (Geomodlab, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal
    Centre of Geographical Studies, Universidade de Lisboa; Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal)

  • Jorge Trindade

    (Centre of Geographical Studies, Universidade de Lisboa; Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal
    Department of Sciences and Technology, Universidade Aberta, 1269-001 Lisboa, Portugal)

Abstract

The complexities of coupled environmental and human systems across the space and time of fragile systems challenge new data-driven methodologies. Combining geographic information systems (GIS) and artificial neural networks (ANN) allows us to design a model that forecasts the erosion changes in Costa da Caparica, Lisbon, Portugal, for 2021, with a high accuracy level. The GIS–ANN model proves to be a powerful tool, as it analyzes and provides the “where” and the “why” dynamics that have happened or will happen in the future. According to the literature, ANNs present noteworthy advantages compared to the other methods that are used for prediction and decision making in urban coastal areas. In order to conduct a sensitivity analysis on natural and social forces, as well as dynamic relations in the dune–beach system of the study area, two types of ANNs were tested on a GIS environment: radial basis function (RBF) and multilayer perceptron (MLP). The GIS–ANN model helps to understand the factors that impact coastal erosion changes, and the importance of having an intelligent environmental decision support system to address these risks. This quantitative knowledge of the erosion changes and the analytical map-based frame are essential for an integrated management of the area and the establishment of pro-sustainability policies.

Suggested Citation

  • Angeliki Peponi & Paulo Morgado & Jorge Trindade, 2019. "Combining Artificial Neural Networks and GIS Fundamentals for Coastal Erosion Prediction Modeling," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:975-:d:205795
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    References listed on IDEAS

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    1. Constantinos A. Balaras & Andreas I. Theodoropoulos & Elena G. Dascalaki, 2023. "Geographic Information Systems for Facilitating Audits of the Urban Built Environment," Energies, MDPI, vol. 16(11), pages 1-26, May.
    2. Juliana Mio de Souza & Paulo Morgado & Eduarda Marques da Costa & Luiz Fernando de Novaes Vianna, 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    3. Daniel Ogaro Atambo & Mohammad Najafi & Vinayak Kaushal, 2022. "Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network," Sustainability, MDPI, vol. 14(9), pages 1-20, May.
    4. Marcell Kupi & Eszter Szemerédi, 2021. "Impact of the COVID-19 on the Destination Choices of Hungarian Tourists: A Comparative Analysis," Sustainability, MDPI, vol. 13(24), pages 1-17, December.

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