Articles | Volume 8, issue 3
Earth Surf. Dynam., 8, 809–824, 2020
Earth Surf. Dynam., 8, 809–824, 2020

Research article 25 Sep 2020

Research article | 25 Sep 2020

Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct

Mariela Perignon et al.

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Cited articles

Agarwal, P. and Skupin, A.: Self-organising maps: Applications in geographic information science, John Wiley & Sons Ltd, 2008. a
Alam, M.: Sea-Level Rise and Coastal Subsidence: Causes, Consequences, and Strategies, Springer Netherlands, Dordrecht,, 1996. a
Allison, M. A.: Historical Changes in the Ganges–Brahmaputra Delta Front, J. Coast. Res., 14, 1269–1275, 1998. a
Allison, M. A.: Geologic framework and environmental status of the Ganges-Brahmaputra Delta, J. Coast. Res., 14, 827–836, 1998. a
Allison, M. A., Khan, S., Goodbred, S. L., and Kuehl, S. A.: Stratigraphic evolution of the late Holocene Ganges-Brahmaputra lower delta plain, Sediment. Geol., 155, 317–342, 2003. a, b
Short summary
We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.