The University of Southampton
University of Southampton Institutional Repository

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

Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm
Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm
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 (?GA) 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 ?GA technique are able to be used in a potentially temporally predictive manner.

Conclusions:
The ?GA 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.
1439-0108
218-229
Wood, Christopher C.
dff0f5cb-6627-4da4-8bfb-df9e881c54ed
Statham, Peter J.
51458f15-d6e2-4231-8bba-d0567f9e440c
Kelly-Gerreyn, Boris A.
0774749f-e27b-44e9-bad9-6c68391c060e
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d
Wood, Christopher C.
dff0f5cb-6627-4da4-8bfb-df9e881c54ed
Statham, Peter J.
51458f15-d6e2-4231-8bba-d0567f9e440c
Kelly-Gerreyn, Boris A.
0774749f-e27b-44e9-bad9-6c68391c060e
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d

Wood, Christopher C., Statham, Peter J., Kelly-Gerreyn, Boris A. and Martin, Adrian P. (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. (doi:10.1007/s11368-013-0793-0).

Record type: Article

Abstract

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 (?GA) 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 ?GA technique are able to be used in a potentially temporally predictive manner.

Conclusions:
The ?GA 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.

Text
Wood Paper-Final version.docx - Accepted Manuscript
Download (290kB)

More information

e-pub ahead of print date: 29 October 2013
Published date: January 2014
Organisations: Ocean and Earth Science, Marine Biogeochemistry

Identifiers

Local EPrints ID: 362642
URI: http://eprints.soton.ac.uk/id/eprint/362642
ISSN: 1439-0108
PURE UUID: a72897f1-6539-435b-9bdd-99466b6502d2

Catalogue record

Date deposited: 28 Feb 2014 11:30
Last modified: 19 Jul 2019 21:17

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×