Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm

Wood, Christopher C.; Statham, Peter J.; Kelly-Gerreyn, Boris A.; Martin, Adrian P. ORCID: https://orcid.org/0000-0002-1202-8612. 2014 Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm. Journal of Soils and Sediments, 14 (1). 218-229. https://doi.org/10.1007/s11368-013-0793-0

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Text © Springer Science+Business Media B.V. 2014 This document is the author’s final manuscript version of the journal article, incorporating any revisions agreed during the peer review process. Some differences between this and the publisher’s version remain. You are advised to consult the publisher’s version if you wish to cite from this article. The final publication is available at link.springer.com Wood Paper-Final version.docx - Accepted Version Download (290kB)

Abstract/Summary

Purpose: Diagenetic modelling, the mathematical simulation of the breakdown of sedimentary organic matter and subsequent fate of associated nutrients, has progressed to a point where complex, non-steady state environments can be accurately modelled. A genetic algorithm has never been used in conjunction with an early diagenetic model, and so we aim to discover whether this method is viable to determining a set of realistic model parameters, which itself is often a difficult task. Materials and methods: A range of sensitivity analyses were conducted to establish the parameters for which the model was most sensitive before a micro-genetic algorithm (μGA) was used to fit an output from a previously published diagenetic model (OMEXDIA) to observational data, taken at the North Dogger site from a series of cruises in the North Sea. Profiles of carbon, oxygen, nitrate and ammonia were considered. The method allows a set of parameters to be determined in a manner analogous to natural selection. Each iteration of the genetic algorithm within each experiment decreases the variance between the observed profiles and those calculated by OMEXDIA. Results and discussion: Despite some of the observed profiles, particularly for carbon, showing unusual patterns, the genetic algorithm was able to generate a set of parameters which was able to fit the observations. The genetic algorithm can therefore help to determine the values of other parameters used in the model, for which observational values are difficult to measure (e.g. the flux of organic matter to the sediment from the overlying water column and the rates of degradation of organic matter). We also show that the values of the parameters determined by the μGA technique are able to be used in a potentially temporally predictive manner. Conclusions: The μGA used is a viable method to fit carbon and nutrient sedimentary profiles observed in complex, dynamic shelf sea systems, despite OMEXDIA originally being designed for a different sedimentary environment. The results therefore show that this novel use of a genetic algorithm is a suitable method for both model calibration and validation and that the technique may help in explaining processes which are poorly understood.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s11368-013-0793-0
ISSN:	1439-0108
Date made live:	28 Feb 2014 11:34 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/505062

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