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Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.

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Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. / Young, Peter C.
In: Environmental Modelling and Software, Vol. 13, No. 2, 04.1998, p. 105-122.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Young, PC 1998, 'Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.', Environmental Modelling and Software, vol. 13, no. 2, pp. 105-122. https://doi.org/10.1016/S1364-8152(98)00011-5

APA

Young, P. C. (1998). Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environmental Modelling and Software, 13(2), 105-122. https://doi.org/10.1016/S1364-8152(98)00011-5

Vancouver

Young PC. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environmental Modelling and Software. 1998 Apr;13(2):105-122. doi: 10.1016/S1364-8152(98)00011-5

Author

Young, Peter C. / Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. In: Environmental Modelling and Software. 1998 ; Vol. 13, No. 2. pp. 105-122.

Bibtex

@article{5fff089a0eb1499499946bab249cfdc4,
title = "Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.",
abstract = "Mathematical modelling in the natural and engineering sciences is most often dominated by a philosophy of deterministic reductionism. Moreover, many of the {\textquoteleft}simulation' models that emerge from this approach to modelling are very large and so difficult to identify, estimate (i.e. calibrate) and validate in rigorous statistical terms. In this situation, it seems sensible to consider alternative modelling strategies which overtly acknowledge these data-based modelling difficulties and address the very real problems of calibration and validation associated with the dynamic modelling of complex systems from time series data. This paper outlines a Data-Based Mechanistic (DBM) modelling philosophy which attempts to address some of these problems and illustrates its wide-ranging practical utility through seven examples in areas ranging from the natural environment, through ecology and macro-economics to engineering.",
keywords = "Data-based mechanistic modelling, Natural environment, Ecology, Macro-economics, Engineering",
author = "Young, {Peter C.}",
year = "1998",
month = apr,
doi = "10.1016/S1364-8152(98)00011-5",
language = "English",
volume = "13",
pages = "105--122",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - Data-based mechanistic modelling of environmental, ecological, economic and engineering systems.

AU - Young, Peter C.

PY - 1998/4

Y1 - 1998/4

N2 - Mathematical modelling in the natural and engineering sciences is most often dominated by a philosophy of deterministic reductionism. Moreover, many of the ‘simulation' models that emerge from this approach to modelling are very large and so difficult to identify, estimate (i.e. calibrate) and validate in rigorous statistical terms. In this situation, it seems sensible to consider alternative modelling strategies which overtly acknowledge these data-based modelling difficulties and address the very real problems of calibration and validation associated with the dynamic modelling of complex systems from time series data. This paper outlines a Data-Based Mechanistic (DBM) modelling philosophy which attempts to address some of these problems and illustrates its wide-ranging practical utility through seven examples in areas ranging from the natural environment, through ecology and macro-economics to engineering.

AB - Mathematical modelling in the natural and engineering sciences is most often dominated by a philosophy of deterministic reductionism. Moreover, many of the ‘simulation' models that emerge from this approach to modelling are very large and so difficult to identify, estimate (i.e. calibrate) and validate in rigorous statistical terms. In this situation, it seems sensible to consider alternative modelling strategies which overtly acknowledge these data-based modelling difficulties and address the very real problems of calibration and validation associated with the dynamic modelling of complex systems from time series data. This paper outlines a Data-Based Mechanistic (DBM) modelling philosophy which attempts to address some of these problems and illustrates its wide-ranging practical utility through seven examples in areas ranging from the natural environment, through ecology and macro-economics to engineering.

KW - Data-based mechanistic modelling

KW - Natural environment

KW - Ecology

KW - Macro-economics

KW - Engineering

U2 - 10.1016/S1364-8152(98)00011-5

DO - 10.1016/S1364-8152(98)00011-5

M3 - Journal article

VL - 13

SP - 105

EP - 122

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

IS - 2

ER -