Structure Characterization - An Overview

Spriet, J.A.

doi:10.1016/s1474-6670(17)60650-5

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Spriet (1985) indicates that comparison and selection should be guided by a tradeoff between fit, parsimony and balanced accuracy. Selecting a model on the basis of fit alone at the expense of a larger number of parameters has the danger that the model is contaminated with structure obtained from noise in the finite data set.…”

Section: )mentioning

confidence: 99%

“…The criteria of quality of fit given in Table 1 provide a direct means of judging models, but care must be exercised in using them for comparing models with different numbers of parameters. Spriet (1985) indicates that comparison and selection should be guided by a tradeoff between fit, parsimony and balanced accuracy. Selecting a model on the basis of fit alone at the expense of a larger number of parameters has the danger that the model is contaminated with structure obtained from noise in the finite data set.…”

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confidence: 99%

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Uncertainty propagation and speculation in projective forecasts of environmental change: A lake‐eutrophication example

Straten

Keesman

1991

Journal of Forecasting

116

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The issue of whether models developed for current conditions can yield correct predictions when used under changed control, as is often the case in environmental management, is discussed. Two models of different complexity are compared on the basis of performance criteria, but it appears that good performance at the calibration stage does not guarantee correctly predicted behavior. A requirement for the detection of such a failure of the model is that the prediction uncertainty range is known. Two techniques to calculate uncertainty propagation are presented and compared: a stochastic first-order error propagation based on the extended Kalman filter (EKF), and a newly developed and robust Monte Carlo set-membership procedure (MCSM). The procedures are applied to a case study of water quality, generating a projective forecast of the algal dynamics in a lake (Lake Veluwe) in response to management actions that force the system into a different mode of behavior. It is found that the forecast from the more complex model falls within the prediction uncertainty range, but its informative value is low due to large uncertainty bounds. As a substitute for time-consuming revisions of the model, educated speculation about parameter shifts is offered as an alternative approach to account for expected but unmodelled changes in the system. KEY WORDS Uncertainty Prediction Parameter estimation Water quality modelling Lake eutrophicationAmong the incentives for modelling an environmental system, perhaps the most tempting is the desire to predict its future behavior. Yet while the literature on model construction is large, only very few convincing accounts are given of model verification, and reports on actual prediction performance are rare. The study of the requirements and peculiarities of the prediction process itself seems to be in its infancy. This is quite an unfortunate situation, because environmental systems are poorly understood and usually badly monitored, yet decisions regarding control measures will have a lasting impact in the years to come, and cannot easily be changed should they appear to be defective. The subject discussed therefore in this paper is: why do models, carefully constructed and calibrated on the basis of present information, sometimes perform so badly when used to predict the system's response to future management actions? Also, closely associated with this:Present address:

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confidence: 99%

Section: )mentioning

confidence: 99%

Uncertainty propagation and speculation in projective forecasts of environmental change: A lake‐eutrophication example

Straten

Keesman

1991

Journal of Forecasting

116

View full text Add to dashboard Cite

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“…In other words, these complex models are nearly always overparameterized [37]. As such, empirical models can be a good option according to the parsimony principle of Spriet [38], in which the model should be less complicated than necessary for the description of the observed data. However, as a black-box approach, one should keep in mind that these data-based models only focus on system influent and effluent characteristics, resulting in poor predictive performance and significant underestimation of the uncertainty estimates hence are not globally predictive [39].…”

Section: Discussionmentioning

confidence: 99%

A Practical Protocol for the Experimental Design of Comparative Studies on Water Treatment

Thas

Echelpoel

et al. 2019

Water

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The design and execution of effective and informative experiments in comparative studies on water treatment is challenging due to their complexity and multidisciplinarity. Often, environmental engineers and researchers carefully set up their experiments based on literature information, available equipment and time, analytical methods and experimental operations. However, because of time constraints but mainly missing insight, they overlook the value of preliminary experiments, as well as statistical and modeling techniques in experimental design. In this paper, the crucial roles of these overlooked techniques are highlighted in a practical protocol with a focus on comparative studies on water treatment optimization. By integrating a detailed experimental design, lab experiment execution, and advanced data analysis, more relevant conclusions and recommendations are likely to be delivered, hence, we can maximize the outputs of these precious and numerous experiments. The protocol underlines the crucial role of three key steps, including preliminary study, predictive modeling, and statistical analysis, which are strongly recommended to avoid suboptimal designs and even the failure of experiments, leading to wasted resources and disappointing results. The applicability and relevance of this protocol is demonstrated in a case study comparing the performance of conventional activated sludge and waste stabilization ponds in a shock load scenario. From that, it is advised that in the experimental design, the aim is to make best possible use of the statistical and modeling tools but not lose sight of a scientific understanding of the water treatment processes and practical feasibility.

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An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis

Alvarado

Larriva

et al. 2019

Water Research

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Structure Characterization - An Overview

Cited by 6 publications

References 18 publications

Uncertainty propagation and speculation in projective forecasts of environmental change: A lake‐eutrophication example

Uncertainty propagation and speculation in projective forecasts of environmental change: A lake‐eutrophication example

A Practical Protocol for the Experimental Design of Comparative Studies on Water Treatment

An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis

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