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Troels Norvin Vilhelmsen

Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts

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Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts. / Vilhelmsen, Troels Norvin; Ferre, Ty Paul.

I: Ground Water, Bind 56, Nr. 3, 2018, s. 399-412.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{ba13ba511ed745b1be2ea0439c3359c3,
title = "Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts",
abstract = "Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty. Given our common financial restrictions, it is critical that we identify data with maximal information content with respect to forecast of interest. In practice, this often devolves to qualitative decisions based on expert opinion. However, there is no assurance that this will lead to optimal design, especially for complex hydrogeological problems. Specifically, these complexities include considerations of multiple forecasts, shared information among potential observations, information content of existing data, and the assumptions and simplifications underlying model construction. In the present study, we extend previous data worth analyses to include: simultaneous selection of multiple new measurements and consideration of multiple forecasts of interest. We show how the suggested approach can be used to optimize data collection. This can be used in a manner that suggests specific measurement sets or that produces probability maps indicating areas likely to be informative for specific forecasts. Moreover, we provide examples documenting that sequential measurement election approaches often lead to suboptimal designs and that estimates of data covariance should be included when selecting future measurement sets.",
keywords = "FRAMEWORK, GROUNDWATER, MANAGEMENT, MODEL CALIBRATION, OPTIMAL-DESIGN, PILOT POINTS, PREDICTIVE UNCERTAINTY, QUALITY MONITORING NETWORK, Decision Making, Uncertainty, Groundwater, Forecasting",
author = "Vilhelmsen, {Troels Norvin} and Ferre, {Ty Paul}",
note = "{\textcopyright} 2017, National Ground Water Association.",
year = "2018",
doi = "10.1111/gwat.12595",
language = "English",
volume = "56",
pages = "399--412",
journal = "Ground Water",
issn = "0017-467X",
publisher = "Wiley-Blackwell Publishing, Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts

AU - Vilhelmsen, Troels Norvin

AU - Ferre, Ty Paul

N1 - © 2017, National Ground Water Association.

PY - 2018

Y1 - 2018

N2 - Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty. Given our common financial restrictions, it is critical that we identify data with maximal information content with respect to forecast of interest. In practice, this often devolves to qualitative decisions based on expert opinion. However, there is no assurance that this will lead to optimal design, especially for complex hydrogeological problems. Specifically, these complexities include considerations of multiple forecasts, shared information among potential observations, information content of existing data, and the assumptions and simplifications underlying model construction. In the present study, we extend previous data worth analyses to include: simultaneous selection of multiple new measurements and consideration of multiple forecasts of interest. We show how the suggested approach can be used to optimize data collection. This can be used in a manner that suggests specific measurement sets or that produces probability maps indicating areas likely to be informative for specific forecasts. Moreover, we provide examples documenting that sequential measurement election approaches often lead to suboptimal designs and that estimates of data covariance should be included when selecting future measurement sets.

AB - Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty. Given our common financial restrictions, it is critical that we identify data with maximal information content with respect to forecast of interest. In practice, this often devolves to qualitative decisions based on expert opinion. However, there is no assurance that this will lead to optimal design, especially for complex hydrogeological problems. Specifically, these complexities include considerations of multiple forecasts, shared information among potential observations, information content of existing data, and the assumptions and simplifications underlying model construction. In the present study, we extend previous data worth analyses to include: simultaneous selection of multiple new measurements and consideration of multiple forecasts of interest. We show how the suggested approach can be used to optimize data collection. This can be used in a manner that suggests specific measurement sets or that produces probability maps indicating areas likely to be informative for specific forecasts. Moreover, we provide examples documenting that sequential measurement election approaches often lead to suboptimal designs and that estimates of data covariance should be included when selecting future measurement sets.

KW - FRAMEWORK

KW - GROUNDWATER

KW - MANAGEMENT

KW - MODEL CALIBRATION

KW - OPTIMAL-DESIGN

KW - PILOT POINTS

KW - PREDICTIVE UNCERTAINTY

KW - QUALITY MONITORING NETWORK

KW - Decision Making

KW - Uncertainty

KW - Groundwater

KW - Forecasting

U2 - 10.1111/gwat.12595

DO - 10.1111/gwat.12595

M3 - Journal article

C2 - 28914971

VL - 56

SP - 399

EP - 412

JO - Ground Water

JF - Ground Water

SN - 0017-467X

IS - 3

ER -