A machine-learning hybrid-classification method for stratification of multidecadal beach dynamics
Coastal areas are one of the most threatened natural systems in the world. Environmental beach indicators, such as erosion and deposition rates of exposed beaches in Andalusia (640 km), were calculated using the upper limit of the active beach profile and detailed orthophotos (1:2500) for the period...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/136451 |
| Acesso em linha: | https://hdl.handle.net/11441/136451 https://doi.org/10.1080/10106049.2022.2110616 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Erosion rate Andalusia Coast Artificial intelligence Regression tree |
| Resumo: | Coastal areas are one of the most threatened natural systems in the world. Environmental beach indicators, such as erosion and deposition rates of exposed beaches in Andalusia (640 km), were calculated using the upper limit of the active beach profile and detailed orthophotos (1:2500) for the periods 1956–1977, 1977–2001 and 2001–2011. A hybrid classification method, both supervised and unsupervised, based on machine-learning (ML) techniques was then applied to model beach response and dynamics for this 55-year period. The use of a K-means technique allowed stratification into four beach groups that have responded similarly in terms of coastline mobility and erosion/deposition patterns. Furthermore, the application of a classification and regression tree (CART) based on the K-means results helped to identify the threshold values for erosional and depositional rates and the period that characterises each cluster or stratum, enabling correct classification of 1415 out of 1509 beaches (93.77%). |
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