Forecasting Interventions on Spanish Beaches through Decision Trees
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- Ansorena (2024) (Español (España)) 37
- Ansorena (2024) - Material complementario (Español (España)) 28
DOI
https://doi.org/10.25267/Costas.2024.v6.i1.0503Info
Authors
- Iñigo López Ansorena (ES) Universidad Nebrija
Abstract
While large-scale data utilization can be a crucial element in coastal studies, few works have addressed this issue. This paper utilizes a refined database from the beach catalogue (Ministry for Ecological Transition and Demographic Challenge of Spain) to develop a Decision Tree that sheds light on the criteria guiding actions on Spanish beaches. The Decision Tree is constructed through a supervised machine learning technique that learns from 40 features and over 100,000 descriptive data points from the 3,554 Spanish beaches. This work reveals the importance of each variable when making a decision (to act or not) on a specific beach. The model allows for a better understanding of the criteria used by the Ministry to make the decision with statistically significant levels of certainty. Early knowledge of this critical decision can be used by all social, economic, and political agents to make contributions that complement the action proposed by the Coastal Directorate.
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Copyright (c) 2024 Iñigo López Ansorena
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Ansorena, I.L. (2020) ‘Managing uncertainty in ferry terminals: a machine learning approach’, Int. J. Business Information Systems, Vol. 33, No. 2, pp.285–297.
Ansorena, I.L. (2023) ‘K-means analysis of construction projects in port waterfronts’, Int. J. Applied Decision Sciences, Vol. 16, No. 5, pp.525–544
Bae, H. K., Olson, B. H., Hsu, K. L., & Sorooshian, S. (2010) ‘Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area’, Water science and technology, Vol.61, No.2, pp.545-553.
Braud D.H. and Hill J.M. (1988) ‘A decision tree for Coastal Management permit guidelines’, Environmental Management, Vol. 12, No.1, pp.29–36
Breiman, L. and Cutler, A. (2004) Random Forests. (Accessed 16 July 2016). http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#giniimp.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984) Classification and regression trees, Wadsworth Int. Group, Belmont, California.
Carretero, S. C., Capítulo, L. R., & Kruse, E. E. (2019) ‘Decision tree as a tool for the management of coastal aquifers of limited saturated thickness’, Quarterly Journal of Engineering Geology and Hydrogeology, qjegh2018-136.
Frontur (2017) Estadística de Movimientos Turísticos en Fronteras.
Goldstein, E.B., Coco, G. and Plant N.G. (2019) ‘A review of machine learning applications to coastal sediment transport and morphodynamics’, Earth-Science Reviews, Vol.(194), July, pp. 97-108.
Hunt, E.B., Marin, J., Stone, P.J. (1966) Experiments in induction. Ac. Press, New York.
LEY DE COSTAS (2013) Ley 2/2013, de 29 de mayo, de protección y uso sostenible del litoral y de modificación de la Ley 22/1988, de 28 de julio, de Costas. Publicado en: «BOE» núm. 129, de 30/05/2013. BOE-A-2013-5670.
Losada, I. J., Toimil, A., Muñoz, A., García-Fletcher, A. P., & Díaz-Simal, P. (2019) ‘A planning strategy for the adaptation of coastal areas to climate change: The Spanish case’, Ocean & Coastal Management, 182, 104983.
Massey, J., Lynch, K. and de Lucia, A. (2013) ‘Decision Making and Coastal Risks: A Good Practice Guide’. Atlantic Network for Coastal Risk Management Project.
Mehta, M., Agrawal, R., Rissanen, J. (1996) ‘SLIQ: A fast scalable classifier for data mining’ in EDBT 1996: Advances in Database Technology, Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp.18-32.
MITECO (2018) The Beaches Guide. Guía de playas de España. Documentación asociada a la Guía de playas. Ed.: Ministerio para la Transición Ecológica (MITECO), Madrid, España. https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/costas-medio-marino/guia-playas-descargas.aspx
Reis, J.P., Pereira, A., and Reis, L.P. (2012) Coastal ecosystems simulation: a decision tree analysis for Bivalve's growth conditions. In Proceedings 26th European Conference on Modelling and Simulation ECMS May 29th - June 1st, 2012. Koblenz, Germany.
Simm, J., and Cruickshank, I. (1998) Construction risk in coastal engineering. Thomas Telford. London, UK.
The earth summit of Río de Janeiro (Chapter 17 in the proceedings of Agenda 21). United Nations Conference on Environment & Development, Rio de Janerio, Brazil, 3 to 14 June 1992, AGENDA 21. http://www.un.org/esa/sustdev/agenda21.htm.
Triki, C., Zekri, S., Al-Maktoumi, A., & Fallahnia, M. (2017) ‘An artificial intelligence approach for the stochastic management of coastal aquifers’, Water resources management, Vol.31, No.15, pp.4925-4939.
Virginia Institute of Marine Science (2010) ‘Integrated Shoreline Management Decision Tree for Untreated Shorelines’, Rivers & Coast v.5, no.2. Center for Coastal Resources Management.
Wu, X. and Kumar, V. (2009) The top 10 algorithms in data mining. Ed. Taylor & Francis Group, LLC, London.