Parameter estimation of ecological models

Marsili-Libelli, Stefano

doi:10.1016/0304-3800(92)90001-u

Cited by 64 publications

(34 citation statements)

References 11 publications

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“…flexible polyhedron search, MarsiliLibelli). [19] Due to the nature of the STELLA software, automatic calibration was not performed. Instead, calibration was performed by trial and error methods.…”

Section: Simulation Methodsmentioning

confidence: 99%

Nitrogen dynamics model for a pilot field-scale novel dewatered alum sludge cake-based constructed wetland system

Kumar

Zhao

et al. 2014

Environmental Technology

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A model simulating the effluent nitrogen (N) concentration of treated animal farm wastewater in a pilot on-site constructed wetland (CW) system, using dewatered alum sludge cake (DASC) as wetland substrate, is presented. The N-model was developed based on the Structural Thinking Experiential Learning Laboratory with Animation software and is considering organic nitrogen, ammonia nitrogen (NH3) and nitrate nitrogen (NO3-N) as the major forms of nitrogen involved in the transformation chains. Ammonification (AMM), ammonia volatilization, nitrification (NIT), denitrification, plant uptake, plant decaying and uptake of inorganic nitrogen by algae and bacteria were considered in this model. pH, dissolved oxygen, temperature, precipitation, solar radiation and nitrogen concentrations were considered as forcing functions in the model. The model was calibrated by observed data with a reasonable agreement prior to its applications. The simulated effluent detritus nitrogen, NH4-N, NO3-N and TN had a considerably good agreement with the observed results. The mass balance analysis shows that NIT accounts for 65.60%, adsorption (ad) (11.90%), AMM (8.90%) followed by NH4-N (Plants) (5.90%) and NO3-N (Plants) (4.40%). The TN removal was found 52% of the total influent TN in the CW. This study suggested an improved overall performance of a DASC-based CW and efficient N removal from wastewater.

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Section: Simulation Methodsmentioning

confidence: 99%

Nitrogen dynamics model for a pilot field-scale novel dewatered alum sludge cake-based constructed wetland system

Kumar

Zhao

et al. 2014

Environmental Technology

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“…(5) are inversely proportional to the measurement uncertainty s j,k defined in Section 1.2. The optimization of the error functional is solved through a numerical search coupled with the model in Table 1 computing the model output y for each trial of the parameter vector p. The Simplex algorithm used in this application is an improved version of the classical flexible polyhedron (Himmelblau, 1972), where the expansion length is optimized in the search direction (Marsili-Libelli, 1992). This optimization method is inherently unconstrained, though in practice model parameters may require constraints, first of all positivity; however, there are several reasons which prevent the introduction of constraints in the Simplex search, as discussed in Checchi and Marsili-Libelli (2005), the most important being that constraints may disrupt its convergence properties and produce a less realistic covariance matrix.…”

Section: Processmentioning

confidence: 99%

Water quality modelling for small river basins

Marsili-Libelli

Giusti

2008

Environmental Modelling & Software

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Water quality modelling in small rivers is often considered unworthy from a practical and economic viewpoint. This paper shows instead that a simple model structure can be set up to describe the stationary water quality in small river basins in terms of carbon and nitrogen compounds, when the use of complex models is unfeasible. In short rivers point and nonpoint sources play a key role in shaping the model response, being as important as the self-purification dynamics. Further, the varying river characteristics, in terms of morphology, hydraulics and vegetation, require the introduction of variable parameters, thus complicating the originally simple model structure. To determine the identifiability of the resulting model an identifiability assessment was carried out, based on sensitivity analysis and optimal experiment design criteria. The identifiable subset was determined by ranking the parameters in terms of sensitivity and computing the associated Fisher Information Matrices. It was found that the inclusion of the nonpoint sources as piecewise constant parameters affected the identifiability to a considerable extent. However, the combined parameteresources calibration was made possible by the use of a robust estimation algorithm, which also provided estimation confidence limits. The calibrated model responses are in good agreement with the data and can be used as scenario generators in a general strategy to conserve or improve the water quality.

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“…The minimisation of Eq. (15) was obtained through a modified version of the Simplex search method (Marsili-Libelli, 1992) and the result is shown in Table 5 and Fig. 15.…”

Section: Design Of the Fuzzy Predictormentioning

confidence: 99%

Fuzzy pattern recognition of circadian cycles in ecosystems

Marsili-Libelli

2004

Ecological Modelling

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Many ecological variables show a wide range of fluctuations, the most important of which is the diurnal variation. This cycling may contain important information regarding the ecosystem's functioning and, if properly interpreted, can represent a valuable predictive tool in ecosystems management. This paper describes a simple algorithm for extracting meaningful information from daily cycles using fuzzy pattern recognition techniques. The algorithm is organised in three parts: in the first, typical patterns are extracted from experimental data to form the knowledge-base upon which the algorithm operates. The second step is to condense the information contained in the knowledge-base into mathematical objects, referred to in the paper as fuzzy masks. The third step is the set-up of an inferential set of fuzzy rules, using the fuzzy masks as antecedents. Depending on how the inference engine is structured, the algorithm output can be viewed as an assessment of the current daily cycle with respect to a set of "typical" system behaviours and decisions can be made accordingly. Two applications are presented to demonstrate the algorithm. In the first a medium-scale wastewater treatment plant with minimum instrumentation is considered. Based on diurnal flow variations of the last 2 days, the algorithm determines the current treatment level in terms of sludge recycling. The other application is the prediction of the eutrophication level in a coastal lagoon. In this case, the algorithm considers the diurnal fluctuations of basic water quality parameters such as dissolved oxygen, oxidation-reduction potential, pH and temperature to detect the possibility of a macroalgae bloom. In both cases, the algorithm provides information about the shift of the system from one trend to another on the basis of the analysis of diurnal cycles. The algorithm performance is assessed against practical situations and the results discussed.

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Parameter estimation of ecological models

Cited by 64 publications

References 11 publications

Nitrogen dynamics model for a pilot field-scale novel dewatered alum sludge cake-based constructed wetland system

Nitrogen dynamics model for a pilot field-scale novel dewatered alum sludge cake-based constructed wetland system

Water quality modelling for small river basins

Fuzzy pattern recognition of circadian cycles in ecosystems

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