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Simplicity out of complexity in environmental modelling: Occam's razor revisited.

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Simplicity out of complexity in environmental modelling: Occam's razor revisited. / Young, Peter C.; Parkinson, Stuart; Lees, Matthew.

In: Journal of Applied Statistics, Vol. 23, No. 2-3, 1996, p. 165-210.

Research output: Contribution to Journal/MagazineJournal article

Harvard

Young, PC, Parkinson, S & Lees, M 1996, 'Simplicity out of complexity in environmental modelling: Occam's razor revisited.', Journal of Applied Statistics, vol. 23, no. 2-3, pp. 165-210. https://doi.org/10.1080/02664769624206

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Author

Young, Peter C. ; Parkinson, Stuart ; Lees, Matthew. / Simplicity out of complexity in environmental modelling: Occam's razor revisited. In: Journal of Applied Statistics. 1996 ; Vol. 23, No. 2-3. pp. 165-210.

Bibtex

@article{84cca2165f494b01ada6fb6f7cc14bb1,
title = "Simplicity out of complexity in environmental modelling: Occam's razor revisited.",
abstract = "While large models based on a deterministic-reductionist philosophy have an important part to play in environmental research, it is advantageous to consider alternative modelling methodologies which overtly acknowledge the poorly defined and uncertain nature of most environmental systems. The paper discusses this topic and presents an integrated statistical modelling procedure which involves three main methodological tools: uncertainty and sensitivity studies based on Monte Carlo simulation techniques; dominant mode analysis using a new method of combined linearization and model-order reduction; and data-based mechanistic modelling. This novel approach is illustrated by two practical examples: modelling the global carbon cycle in relation to possible climate change; and modelling a horticultural glasshouse for the purposes of automatic climate control system design.",
author = "Young, {Peter C.} and Stuart Parkinson and Matthew Lees",
year = "1996",
doi = "10.1080/02664769624206",
language = "English",
volume = "23",
pages = "165--210",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "2-3",

}

RIS

TY - JOUR

T1 - Simplicity out of complexity in environmental modelling: Occam's razor revisited.

AU - Young, Peter C.

AU - Parkinson, Stuart

AU - Lees, Matthew

PY - 1996

Y1 - 1996

N2 - While large models based on a deterministic-reductionist philosophy have an important part to play in environmental research, it is advantageous to consider alternative modelling methodologies which overtly acknowledge the poorly defined and uncertain nature of most environmental systems. The paper discusses this topic and presents an integrated statistical modelling procedure which involves three main methodological tools: uncertainty and sensitivity studies based on Monte Carlo simulation techniques; dominant mode analysis using a new method of combined linearization and model-order reduction; and data-based mechanistic modelling. This novel approach is illustrated by two practical examples: modelling the global carbon cycle in relation to possible climate change; and modelling a horticultural glasshouse for the purposes of automatic climate control system design.

AB - While large models based on a deterministic-reductionist philosophy have an important part to play in environmental research, it is advantageous to consider alternative modelling methodologies which overtly acknowledge the poorly defined and uncertain nature of most environmental systems. The paper discusses this topic and presents an integrated statistical modelling procedure which involves three main methodological tools: uncertainty and sensitivity studies based on Monte Carlo simulation techniques; dominant mode analysis using a new method of combined linearization and model-order reduction; and data-based mechanistic modelling. This novel approach is illustrated by two practical examples: modelling the global carbon cycle in relation to possible climate change; and modelling a horticultural glasshouse for the purposes of automatic climate control system design.

U2 - 10.1080/02664769624206

DO - 10.1080/02664769624206

M3 - Journal article

VL - 23

SP - 165

EP - 210

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 2-3

ER -