IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i4p975-d205795.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/4/975/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/4/975/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    2. N. Sudha Rani & A. Satyanarayana & Prasad Bhaskaran, 2015. "Coastal vulnerability assessment studies over India: a review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(1), pages 405-428, May.
    3. Barbara Neumann & Athanasios T Vafeidis & Juliane Zimmermann & Robert J Nicholls, 2015. "Future Coastal Population Growth and Exposure to Sea-Level Rise and Coastal Flooding - A Global Assessment," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-34, March.
    4. Susan Hanson & Robert Nicholls & N. Ranger & S. Hallegatte & J. Corfee-Morlot & C. Herweijer & J. Chateau, 2011. "A global ranking of port cities with high exposure to climate extremes," Climatic Change, Springer, vol. 104(1), pages 89-111, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. D. J. Rasmussen & Scott Kulp & Robert E. Kopp & Michael Oppenheimer & Benjamin H. Strauss, 2022. "Popular extreme sea level metrics can better communicate impacts," Climatic Change, Springer, vol. 170(3), pages 1-17, February.
    2. Tian Liu & Peijun Shi & Jian Fang, 2022. "Spatiotemporal variation in global floods with different affected areas and the contribution of influencing factors to flood-induced mortality (1985–2019)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2601-2625, April.
    3. M. Dinesh Kumar & Shubham Tandon & Nitin Bassi & Pradipta Kumar Mohanty & Saurabh Kumar & Manish Mohandas, 2022. "A framework for risk-based assessment of urban floods in coastal cities," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 2035-2057, February.
    4. Xuchao Yang & Lin Lin & Yizhe Zhang & Tingting Ye & Qian Chen & Cheng Jin & Guanqiong Ye, 2019. "Spatially Explicit Assessment of Social Vulnerability in Coastal China," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    5. Anamaria Bukvic & Guillaume Rohat & Alex Apotsos & Alex de Sherbinin, 2020. "A Systematic Review of Coastal Vulnerability Mapping," Sustainability, MDPI, vol. 12(7), pages 1-26, April.
    6. Francesca Dal Cin & Martin Fleischmann & Ombretta Romice & João Pedro Costa, 2020. "Climate Adaptation Plans in the Context of Coastal Settlements: The Case of Portugal," Sustainability, MDPI, vol. 12(20), pages 1-19, October.
    7. Thomas David Pol & Jochen Hinkel, 2019. "Uncertainty representations of mean sea-level change: a telephone game?," Climatic Change, Springer, vol. 152(3), pages 393-411, March.
    8. Nicholas R. Golledge, 2020. "Long‐term projections of sea‐level rise from ice sheets," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(2), March.
    9. Jan-Ludolf Merkens & Daniel Lincke & Jochen Hinkel & Sally Brown & Athanasios Thomas Vafeidis, 2018. "Regionalisation of population growth projections in coastal exposure analysis," Climatic Change, Springer, vol. 151(3), pages 413-426, December.
    10. Julia Caon Araujo & Fabio Ferreira Dias, 2021. "Multicriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 53-72, May.
    11. Mary O. Oloyede & Akan B. Williams & Godwin O. Ode & Nsikak U. Benson, 2022. "Coastal Vulnerability Assessment: A Case Study of the Nigerian Coastline," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    12. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    13. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    14. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    15. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    16. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    17. Sem J. Duijndam & W. J. Wouter Botzen & Liselotte C. Hagedoorn & Philip Bubeck & Toon Haer & My Pham & Jeroen C. J. H. Aerts, 2023. "Drivers of migration intentions in coastal Vietnam under increased flood risk from sea level rise," Climatic Change, Springer, vol. 176(2), pages 1-22, February.
    18. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    19. Jidong Wu & Ying Li & Ning Li & Peijun Shi, 2018. "Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 17-30, January.
    20. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:975-:d:205795. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.