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Probabilistic programming for nitrate pollution control: Comparing different probabilistic constraint approximations

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Title: Probabilistic programming for nitrate pollution control: Comparing different probabilistic constraint approximations
Authors: Kampas, Athanasios1 tkampas@aias.gr, White, Ben2 bwhite@agric.uwa.edu.au
Source: European Journal of Operational Research. 5/16/2003, Vol. 147 Issue 1, p217. 12p.
Subjects: Stochastic programming, Emissions (Air pollution), Nitrates
Abstract: Agricultural nitrate emissions within a river catchment are, due to rainfall and other sources of natural variation, uncertain. A regulator aiming to reduce nitrate emissions into surface and groundwater faces a trade-off between reliability in achieving emission standards and the cost of compliance to agriculture. This paper explores this trade-off by comparing different assumptions about the probability distribution of nitrate emissions and thus the probabilistic constraint included in the catchment model. Three categories of probabilistic constraints are considered: (1) non-parametric, (2) normal and (3) lognormal. The results indicate that the restrictiveness of the non-parametric assumption could lead to a significant reduction in profit relative to the normal and lognormal. The lognormal assumption, although it is theoretically correct, cannot be generalised to the case of correlated emissions. However, ignoring the dependence between different sources of nitrate emissions introduces more bias than mis-specifying their distribution. Therefore a probabilistic constraint based on a correlated normal distribution of emissions gives the best approximation for nitrate emissions in this study. [Copyright &y& Elsevier]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 8997902
AccessLevel: 6
PubType: Academic Journal
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  Data: Probabilistic programming for nitrate pollution control: Comparing different probabilistic constraint approximations
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  Data: <searchLink fieldCode="AR" term="%22Kampas%2C+Athanasios%22">Kampas, Athanasios</searchLink><relatesTo>1</relatesTo><i> tkampas@aias.gr</i><br /><searchLink fieldCode="AR" term="%22White%2C+Ben%22">White, Ben</searchLink><relatesTo>2</relatesTo><i> bwhite@agric.uwa.edu.au</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. 5/16/2003, Vol. 147 Issue 1, p217. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Emissions+%28Air+pollution%29%22">Emissions (Air pollution)</searchLink><br /><searchLink fieldCode="DE" term="%22Nitrates%22">Nitrates</searchLink>
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  Data: Agricultural nitrate emissions within a river catchment are, due to rainfall and other sources of natural variation, uncertain. A regulator aiming to reduce nitrate emissions into surface and groundwater faces a trade-off between reliability in achieving emission standards and the cost of compliance to agriculture. This paper explores this trade-off by comparing different assumptions about the probability distribution of nitrate emissions and thus the probabilistic constraint included in the catchment model. Three categories of probabilistic constraints are considered: (1) non-parametric, (2) normal and (3) lognormal. The results indicate that the restrictiveness of the non-parametric assumption could lead to a significant reduction in profit relative to the normal and lognormal. The lognormal assumption, although it is theoretically correct, cannot be generalised to the case of correlated emissions. However, ignoring the dependence between different sources of nitrate emissions introduces more bias than mis-specifying their distribution. Therefore a probabilistic constraint based on a correlated normal distribution of emissions gives the best approximation for nitrate emissions in this study. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/S0377-2217(02)00254-0
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 217
    Subjects:
      – SubjectFull: Stochastic programming
        Type: general
      – SubjectFull: Emissions (Air pollution)
        Type: general
      – SubjectFull: Nitrates
        Type: general
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      – TitleFull: Probabilistic programming for nitrate pollution control: Comparing different probabilistic constraint approximations
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            NameFull: Kampas, Athanasios
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            NameFull: White, Ben
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              Text: 5/16/2003
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              Y: 2003
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            – TitleFull: European Journal of Operational Research
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