Published August 25, 2022
| Version v1
Publication
A machine-learning hybrid-classification method for stratification of multidecadal beach dynamics
Description
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%).
Abstract
Ministerio de Ciencia, Innovación y Universidades RTI2018-096561-A-I00Abstract
Junta de Andalucía. Consejería de Economía y Conocimiento US-1262552Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/136451
- URN
- urn:oai:idus.us.es:11441/136451