A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps

Abstract

Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring.

Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques

“…SOM’s utility lies in the holistic visual interpretation of the output rather than in understanding the underlying processes [ 63 ]. Roughly speaking, the output layer (i.e., the self-organising map itself) contains neurons organised in a rectangular or hexagonal lattice to represent the entire dataset [ 58 ].…”

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“…For each layer, a similarity level is calculated, and the individual sim are combined into a single value which is used to determine the winner node. SOM's utility lies in the holistic visual interpretation of the output rather than in understanding the underlying processes [63]. Roughly speaking, the output layer (i.e., the self-organising map itself) contains neurons organised in a rectangular or hexagonal lattice to represent the entire dataset [58].…”

Phase II MS4 challenges: moving toward effective stormwater management for small municipalities

“…Promising options for low-cost monitoring programs may lie in remote sensing (Matejicev et al 2003;Heasley et al 2020), citizen science (Cartwright et al 2015;Shupe 2017;Kielstra et al 2019), inter-departmental collaboration within municipal governments, or collaboration with higher educational institutions. Using technology and stakeholder/citizen programs to gather information can lower costs and improve efficiency of data collection programs.…”

Fluvial Geomorphology and River Management

“…The behaviour of Mediterranean fluvial systems was stressed with three distinct approaches by Bentivenga et al [4] and Filocamo et al [5] in southern Italy, and by Sánchez-Donoso et al [6] in southern Spain. Concerning humid continental climatic zone areas, the papers of Heasley et al [7], Zingaro et al [8], and Heritage and Entwistle [9] analyse the fluvial systems of the UK under different point of views, whereas those of Szabó et al [10] and Bihałowicz and Wierzbicki [11] address fluvial systems of central Europe in Hungary and Poland, respectively. The fluvial system in the humid tropical climate of Brazil was analysed by Marinho et al [12], and that in the arid climate of Northern Ethiopia by Annys et al [13].…”