THE IDENTIFIABILITY ANALYSIS FOR SETTING UP MEASURING CAMPAIGNS FOR INTEGRATED WATER QUALITY MODELLING

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Abstract

Identifiability analysis enables one the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology is able to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for the setting up of measuring campaigns for integrated water quality modelling. The analysis has been applied to a real case study characterized by a partially urbanized catchment represented by two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterized by different monitoring stations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameter quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2010

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water quality
methodology
data quality
water
modeling
cross section
catchment
calibration
wastewater
economics
river
analysis
measuring
appraisal
wastewater treatment plant
monitoring station
parameter

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title = "THE IDENTIFIABILITY ANALYSIS FOR SETTING UP MEASURING CAMPAIGNS FOR INTEGRATED WATER QUALITY MODELLING",
abstract = "Identifiability analysis enables one the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology is able to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for the setting up of measuring campaigns for integrated water quality modelling. The analysis has been applied to a real case study characterized by a partially urbanized catchment represented by two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterized by different monitoring stations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameter quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data.",
keywords = "Identifiability, mathematical modelling, uncertainty",
author = "Gabriele Freni and Giorgio Mannina",
year = "2010",
language = "English",

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T1 - THE IDENTIFIABILITY ANALYSIS FOR SETTING UP MEASURING CAMPAIGNS FOR INTEGRATED WATER QUALITY MODELLING

AU - Freni, Gabriele

AU - Mannina, Giorgio

PY - 2010

Y1 - 2010

N2 - Identifiability analysis enables one the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology is able to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for the setting up of measuring campaigns for integrated water quality modelling. The analysis has been applied to a real case study characterized by a partially urbanized catchment represented by two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterized by different monitoring stations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameter quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data.

AB - Identifiability analysis enables one the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology is able to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for the setting up of measuring campaigns for integrated water quality modelling. The analysis has been applied to a real case study characterized by a partially urbanized catchment represented by two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterized by different monitoring stations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameter quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data.

KW - Identifiability, mathematical modelling, uncertainty

UR - http://hdl.handle.net/10447/52625

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