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The paper briefly reviews the main features of the CAPTAIN Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines.

International audience; Direct identification of continuous-time models from sampled data is now mature. The developed methods have proven successful in many practical applications and are available as user-friendly and computationally efficient algorithms in the CAPTAIN and CONTSID toolboxes for MatlabTM. Surprisingly many practitioners appear unaware that such methods not only exist but may be better suited to their modelling problems. This paper discusses and illustrates with the help of real-life data the advantages of these direct schemes to continuous-time model identification.

The paper discusses the emulation of large, distributed parameter, computer models by low order, continuous-time, transfer function models obtained using the SRIVC method of identification and estimation for continuous-time models. This yields a minimally parameterized, reduced order, ‘nominal’ emulation model that often reproduces the dynamic behavior of the large model to a remarkable degree. In full Dynamic Model Emulation (DEM), the objective is to emulate the high order model over a whole, user-defined range of parameter values, so that it can act as a surrogate for the high order model in applications that demand fast, repeated solution, as in Monte Carlo simulation and sensitivity analysis, or be used as a low order model in automatic control system design and adaptive forecasting applications. Most of the paper deals with the ‘stand-alone’ emulation of two high order, distributed parameter, computer models for the transport and dispersion of solutes in water systems.

This paper demonstrates the feasibility of a new approach to system inversion and input signal estimation based on the exploitation of non-minimal state space feedback control system design methods that can be applied to non-minimum phase and unstable systems. The real and simulated examples demonstrate its practical utility and show that it has particular relevance in a hydrological systems context.

The paper is concerned with the practical aspects of a unified approach to the identification and estimation of multiple-input, single-output (MISO) transfer function models for both continuous and discrete-time systems. The estimation algorithms considered in the paper are based on the Refined Instrumental Variable (RIV) approach to identification and estimation, where the MISO model denominator polynomials are normally constrained to be equal. Unconstrained RIV estimation presents a more difficult problem and it is necessary to exploit an iterative, back-fitting routine to handle this more general situation. The paper focuses on the practical realization of this back-fitting algorithm, including its initiation from either common denominator MISO or repeated SISO estimation. The rivcdd algorithm for continuous-time model estimation, as implemented in the CAPTAIN Toolbox for Matlab is then used in three practical examples: first, the modelling of solute transport and dispersion in a water body; secondly, modelling for two control problems, namely a pair of connected laboratory DC motors and a nonlinear wind turbine simulation.

The paper describes a software framework for implementing the main stages of the Data Based Mechanistic (DBM) modelling approach to the reduced order emulation (meta-modelling) of large dynamic system computer models, within the Matlab software environment. The framework exploits routines in the CAPTAIN Toolbox to identify and estimate transfer function models that reflect the dominant modes of the dynamic behaviour in the large model. This allows for the 'nominal emulation' and validation of the large model for a single, specified set of parameters; as well as 'stand-alone, full emulation' based on the construction and validation of hyper-dimensional maps between a user-specified range of large model parameters and the parameters of the associated, low order transfer function models. The software framework uses the multivariable structure constructs available within Matlab^(TM) to form a small library of routines that will become part of the Captain Toolbox. The library is formed around special data structures that facilitate multivariable operations and visualisations which both enhance the efficiency of the emulation modelling analysis and the modeller's interaction with the process of emulation. The nature of the analysis is illustrated by a topical example concerned with the emulation of the OTIS computer simulation model for the transport and dispersion of solutes in a river system.

Emulation modelling is an effective way of overcoming the large computational burden associated with the process-based models traditionally adopted by the environmental modelling community. An emulator is a low-order, computationally efficient model identified from the original large model and then used to replace it for computationally intensive applications. As the number and forms of the problem that benefit from the identification and subsequent use of an emulator is very large, emulation modelling has emerged in different sectors of science, engineering and social science. For this reason, a variety of different strategies and techniques have been proposed in the last few years. The main aim of the paper is to provide an introduction to emulation modelling, together with a unified strategy for its application, so that modellers from different disciplines can better appreciate how it may be applied in their area of expertise. Particular emphasis is devoted to Dynamic Emulation Modelling (DEMo), a methodological approach that preserves the dynamic nature of the original process-based model, with consequent advantages in a wide variety of problem areas. The different techniques and approaches to DEMo are considered in two macro categories: structure-based methods, where the mathematical structure of the original model is manipulated to a simpler, more computationally efficient form; and data-based approaches, where the emulator is identified and estimated from a data-set generated from planned experiments conducted on the large simulation model. The main contribution of the paper is a unified, six-step procedure that can be applied to most kinds of dynamic emulation problem.

Editorial of the special issue : Identification and Control of Nonlinear Electro-Mechanical Systems; International audience; Electro-mechanical systems are systems composed of both electrical and mechanical parts. They include motors, robots, cranes, compactors, electro-mechanical positioning systems, nanopositioning systems, and piezoelectric actuators, amongst others. As a consequence, mechanical and electrical engineering communities encompass a variety of fields such as robotics, mechatronics, electrotechnics, electronics, and power engineering, all of which can be considered a part of an over-arching electro-mechanical community.Within the electro-mechanical community, system identification refers to the whole process of identifying the most appropriate model form and estimating the parameters of this model from measured input/output data or from a combination of such data and prior knowledge. Dynamical models obtained in this manner are useful for tasks such as analysing the system properties (e.g. identification of nonlinear friction models); performing simulation experiments; and control system design (e.g. model-based control, predictive control, sliding-mode control).Such dynamical models are usually formulated in terms of differential equations, or transfer functions in the differential operator, because the physical laws on which the models are based are normally synthesised in terms of differential equation relationships based on natural laws, such as Newton's laws, Ohm's law, Kirchhoff's relations and Maxwell's equations. It is not surprising, therefore, that most electro-mechanical engineering theory and practice is based on continuous-time models. Electro-mechanical system control has similar objectives to most automatic control systems, i.e. to control the system so that its output follows a desired reference, which may be a fixed or changing value (a set point or trajectory).Despite the similarities between the automatic control and electro-mechanical engineering communities, some important differences remain. For example, within the electro-mechanical community, the identification and/or control methodology is mostly devoted to specific real-world systems rather than to general systems. The theoretical aspects of methodology, such as the statistical efficiency of the model parameter estimates or the optimality of the control system, are addressed much less often, whereas these are prolific topics in the automatic control community, where many papers are based on theoretical analysis and results. The electro-mechanical community, on the other hand, is often more reluctant to make general theoretical assumptions, tending to mistrust such generalisations when dealing with real-world systems and producing experimental results. And finally, both nonlinearity and high dimensionality are often unavoidable in electro-mechanical systems and so the consideration of such factors is much more prevalent in the electro-mechanical engineering community.Fortunately, there is evidence that these differences between the two communities are growing less and the present special issue is intended to encourage still greater cross-fertilisation between the communities, which we believe will result in advantages to both. It presents papers dealing with examples that are concerned with the identification and control of various different electro-mechanical systems; and examples that help to reveal the capabilities of current identification/control methods when they are applied to challenging, real-world applications.In the review of the content below, the papers have been separated into those that are involved with the implementation of the proposed methodology on a real electro-mechanical system, which are considered first, and then those that use computer simulation to demonstrate the feasibility of the proposed methodology.

Citation Littlewood, I. G., Croke, B. F. W. & Young, P. C. (2011) Discussion of “Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge” by Y. Wang, B. He & K. Takase (2009, Hydrol. Sci. J. 54(5), 886–898). Hydrol. Sci. J. 56(3), 521–524.

The article describes a new methodology for the emulation of high-order, dynamic simulation models. This exploits the technique of dominant mode analysis to identify a reduced-order, linear transfer function model that closely reproduces the linearized dynamic behavior of the large model. Based on a set of such reduced-order models, identified over a specified region of the large model’s parameter space, nonparametric regression, tensor product cubic spline smoothing, or Gaussian process emulation are used to construct a computationally efficient, low-order, dynamic emulation (or meta) model that can replace the large model in applications such as sensitivity analysis, forecasting, or control system design. Two modes of emulation are possible, one of which allows for novel ‘stand-alone’ operation that replicates the dynamic behavior of the large simulation model over any time horizon and any sequence of the forcing inputs. Two examples demonstrate the practical utility of the proposed technique and supple...

This special issue of the Journal of Forecasting jointly celebrates the 40th anniversary of the publication of George Box and Gwilym Jenkins' highly influential book Time Series Analysis: Forecasting and Control, which introduced a robust and easily implementable strategy for modelling time series, and the 50th anniversary of the appearance of Rudolf Kalman's article ‘A new approach to linear filtering and prediction problems’ in the Journal of Basic Engineering, which has had an extraordinary impact in many diverse fields, has led to major advances in recursive estimation, and has introduced the term Kalman filter into the lexicon of time series analysis and forecasting. The huge number of papers published in the Journal of Forecasting that reference these two publications bears testament to their seminal status and long-lasting influence, making them a natural choice to base a special issue around. Copyright © 2010 John Wiley & Sons, Ltd.

The paper considers how the Kalman filter has influenced the development of recursive parameter estimation since the publication of Rudolf Kalman's seminal article in 1960. It will present a partial review of developments over the past half century and provide a tutorial introduction to the refined instrumental variable approach to the optimal recursive estimation of parameters in both discrete and continuous-time transfer function models. The paper concludes with a case study that shows how recursive parameter estimation and the Kalman filter can be combined in the design and development of a real-time adaptive forecasting and data assimilation system for flow in river systems. Copyright © 2010 John Wiley & Sons, Ltd.

The January 2005 flood event in the Eden catchment (UK) has focused considerable research effort towards strengthening and extending operational flood forecasting in the region. The Eden catchment has become a key study site within the remit of phase two of the Flood Risk Management Research Consortium. This paper presents a synthesis of results incorporating model uncertainty analysis, computationally efficient real-time data assimilation/forecasting algorithms, two-dimensional (2D) inundation modelling, and data visualization for decision support. The emphasis here is on methods of presenting information from a new generation of probabilistic flood forecasting models. Using Environment Agency rain and river-level gauge data, a data-based mechanistic model is identified and incorporated into a modified Kalman Filter (KF) data assimilation algorithm designed for real-time flood forecasting applications. The KF process generates forecasts within a probabilistic framework. A simulation of the 6-h ahead forecast for river levels at Sheepmount (Carlisle) covering the January 2005 flood event is presented together with methods of visualizing the associated uncertainty. These methods are then coupled to the 2D hydrodynamic LISFLOOD-FP model to produce real-time flood inundation maps. The value of incorporating probabilistic information is emphasized.

In this paper, we develop a discrete time, state variable feedback control regime to analyze the closed-loop properties associated with stabilizing the global mean surface temperature anomaly at 2^oC within a sequential decision making framework made up of 20 year review periods beginning in 2020. The design of the feedback control uses an optimal control approach that minimizes the peak deceleration of anthropogenic CO"2 emissions whilst avoiding overshooting the 2^oC target. The peak value for emissions deceleration that satisfies the closed-loop optimization was found to be linearly related to climate sensitivity and a climate sensitivity of 3.5^oC gave a deceleration of -1.9GtC/a/20 years^2. In addition to accounting for the predicted climate dynamics, the control system design includes a facility to emulate a robust corrective action in the face of uncertainty. The behavior of the overall control action is evaluated using an uncertainty scenario for climate model equilibrium sensitivity.

An inflow prediction model is developed to compute flow from temperature records, taking into consideration snow-melt contribution to the flow using a Data-Based Mechanistic (DBM) modeling approach. DBM is used in order to keep at a minimum all the a-priori assumptions on the physical mechanism driving the flow formation process and to provide an a-posteriori meaningful interpretation of the model structure. A simulation version of the model is also identified based on such interpretation. The two models have been applied on the Jakulsa river basin, Iceland.

Water level predictions made with hydraulic models are uncertain and evaluating this uncertainty using Monte Carlo ensemble prediction is computationally very expensive. In this paper we show how a reduced order Dynamic Model Emulator (DME) can be used to reproduce, with high accuracy, the outputs of a large and complex 1-D hydraulic model (HEC-RAS) at specified cross-sections along the Montford to Buildwas reach of the River Severn in the U.K, together with estimates of uncertainty in the predictions. This emulation model is obtained by the application of Dominant Mode Analysis (DMA), involving the identification and estimation of nonlinear State-Dependent Parameter (SDP) transfer function models, using data generated by dynamic experiments conducted on the HEC-RAS model. The paper shows how this ‘nominal’ DME is able to emulate the distributed hydraulic model for a nominal set of its physically-defined parameters and it presents initial results from a complete DME that emulates the HEC-RAS model over a user-defined region of its parameter space.

The paper outlines the development of time variable parameter (TVP) estimation as an approach to modelling time varying dynamic systems. It then describes one of the latest methods for estimating time variable parameters in transfer function models and shows how it overcomes problems associated with earlier methods based on the least squares estimation of time variable parameters in the more restricted auto-regressive, exogenous variables (ARX) model. This ‘dynamic transfer function’ (DTF) estimation methodology is a combination of recursive-iterative instrumental variable filtering and fixed interval smoothing algorithms.

This paper describes optimal instrumental variable methods for identifying discrete-time transfer function models when the system operates in closed-loop. Several noise models required for the design of optimal prefilters and instruments are analyzed and different approaches are developed according to whether the controller is known or not. Moreover, a new optimal refined instrumental variable technique is developed to handle the identification of a linear (ARX) predictor combined with an ARMA noise model in a closed-loop framework. The proposed refined instrumental variable algorithm achieves minimum variance estimation of the process model parameters. The performance of the proposed approaches is evaluated by Monte-Carlo analysis in comparison with other alternative closed-loop estimation methods.

This paper considers model predictive control (MPC) using a non-minimal state-space (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative forward path structure. There is a close analogy with proportional-integral-plus (PIP) control system design, which is also based on the definition of an NMSS model with two control structures. However, the MPC/NMSS approach has the advantage of handling system constraints at the design stage. Also, since the NMSS model is obtained directly from the identified transfer function model, the covariance matrix for the parameter estimates can be used to evaluate the robustness of the predictive control system to model uncertainty using Monte Carlo simulation. The effectiveness of the approach is demonstrated by means of simulation examples, including the IFAC′93 benchmark and the ALSTOM non-linear gasifier problem. For the simulation examples considered here, the forward path form preserves the good performance properties of the original MPC/NMSS controller, while at the same time yielding improved robustness.

The paper considers the differences between hypothetico-deductive and inductive modeling: between modelers who put their primary trust in their scientific intuition about the nature of an environmental model and tend to produce quite complex computer simulation models; and those who prefer to rely on the analysis of observational data to identify the simplest form of model that can represent these data. The tension that sometimes arises because of the different philosophical outlooks of these two modeling groups can be harmful because it tends to fractionate the effort that goes into the investigation of important environmental problems, such as global warming. In an attempt to improve this situation, the paper will outline a new Data-Based Mechanistic (DBM) approach to modeling that tries to meld together the best aspects of these two modeling philosophies in order to develop a unified approach that combines the hypothetico-deductive virtues of good scientific intuition and simulation modeling with the pragmatism of inductive data-based modeling, where more objective inference from data is the primary driving force. In particular, it demonstrates the feasibility of a new method for complex simulation model emulation, in which the methodological tools of DBM modeling are used to develop a reduced dynamic order model that represents the ‘dominant modes’ of the complex simulation model. In this form, the ‘dynamic emulation’ model can be compared with the DBM model obtained directly from the analysis of real data and any tensions between the two modeling approaches may be relaxed to produce models that suit multiple modeling objectives.

This paper develops a short-term forecasting system for hourly electricity load demand based on Unobserved Components set up in a State Space framework. The system consists of two options, a univariate model and a non-linear bivariate model that relates demand to temperature. In order to handle the rapidly sampling interval of the data, a multi-rate approach is implemented with models estimated at different frequencies, some of them with ‘periodically amplitude modulated’ properties. The non-linear relation between demand and temperature is identified via a Data-Based Mechanistic approach and finally implemented by Radial Basis Functions. The models also include signal extraction of daily and weekly components. Both models are tested on the basis of a thorough experiment in which other options, like ARIMA and Artificial Neural Networks are also used. The models proposed compare very favourably with the rest of alternatives in forecasting load demand.

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.

This paper describes the unified Refined Instrumental Variable approach to the time domain identification and estimation of both discrete-time (RIV) and continuous-time (RIVC) transfer function models. It demonstrates how this approach yields parameter estimates with optimal statistical properties for the Box-Jenkins and hybrid Box-Jenkins model forms on which the associated RIV and RIVC estimation algorithms are based. The performance of the algorithms, which can be implemented in en-bloc or recursive form, is evaluated by Monte-Carlo simulation analysis and their practical utility is illustrated by a number of practical environmental examples.

Among the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes several well-known random walk models as special cases. Later, these theoretical results are used to derive an alternative algorithm, based on optimization in the frequency domain, for the identification and estimation of DHR models. The main advantages of this algorithm are linearity, fast computational speed, avoidance of some numerical issues, and automatic identification of the DHR model. The signal extraction performance of the algorithm is evaluated using empirical applications and comprehensive Monte Carlo simulation analysis.

This paper formally introduces the concept of mitigation as a stochastic control problem. This is illustrated by applying a digital state variable feedback control approach known as Non-Minimum State Space (NMSS) control to the problem of specifying carbon emissions to control atmospheric CO2 concentrations in the presence of uncertainty. It is shown that the control approach naturally lends itself to integrating both anticipatory and reflexive mitigation strategies within a single unified framework. The framework explicitly considers the closed-loop nature of climate mitigation, and employs a policy orientated optimisation procedure to specify the properties of this closed-loop system. The product of this exercise is a control law that is suitably conditioned to regulate atmospheric CO2 concentrations through assimilating online information within a 25-year review cycle framework. It is shown that the optimal control law is also robust when faced with significant levels of uncertainty about the functioning of the global carbon cycle.

The Data-Based Mechanistic (DBM) modelling philosophy emphasises the importance of parametrically efficient, low order, 'dominant mode' models, as well as the development of stochastic methods and the associated statistical analysis required for their identification and estimation. Furthermore, it stresses the importance of explicitly acknowledging the basic uncertainty in the process, which is particularly important for the characterisation and forecasting of environmental and other poorly defined systems. The paper focuses on a Matlab^(R) compatible toolbox that has evolved from this DBM modelling research. Based around a state space and transfer function estimation framework, Captain extends Matlab^(R) to allow, in the most general case, for the identification and estimation of a wide range of unobserved components models. Uniquely, however, Captain focuses on models with both time variable and state dependent parameters and has recently been implemented with the latest methodological developments in this regard. Here, the main innovations are: the automatic optimisation of the hyper-parameters, which define the statistical properties of the time variable parameters; the provision of smoothed as well as filtered parameter estimates; the robust and statistically efficient identification and estimation of both discrete and continuous time transfer function models; and the availability of various special model structures that have wide application potential in the environmental sciences.

This paper describes a new method for the design of model predictive control (MPC) using non-minimal state space models, in which the state variables are chosen as the set of measured input and output variables and their past values. It shows that the proposed design approach avoids the use of an observer to access the state information and, as a result, the disturbance rejection, particularly the system input disturbance rejection, is significantly improved when constraints become activated. In addition, when there is no model/plant mismatch, the paper shows that the system output constraints can be realised in the proposed approach. Furthermore, closed-form transfer function representation of the model predictive control system enables the application of frequency response analysis tools to the nominal performance of the system.

The paper briefly reviews the topic of rainfall-flow modelling and the inductive, Data-Based Mechanistic (DBM) approach to modelling stochastic, dynamic systems. It then uses DBM modelling methods to investigate the nonlinear relationship between daily rainfall and flow in the Leaf River, Mississippi, USA. Initially, recursive State-Dependent Parameter (SDP) estimation is used to identify, in non-parametric (graphical) terms, the location and nature of the 'effective rainfall' nonlinearity. Parameterization of this nonlinearity and optimization of a constrained version of the resulting model allow for its interpretation in a hydrologically meaningful State-Dependent Parameter Transfer Function (SDTF) form. Finally, the model its used as the basis for the design of a realtime flow forecasting using an optimized SDP Kalman Filter (SDPKF) forecasting engine that includes a model of the heteroscedastic measurement noise.

Unobserved components models provide a natural framework for the estimation and forecasting of periodic components embedded in the time series, such as business cycles or seasonality. However, periodic behaviour can be complicated to analyse when dealing with rapidly sampled data of the kind encountered in electricity demand forecast problems. Data of this nature tend to show a multiplicity of superimposed periodic patterns, including annual, weekly and daily cycles. In this paper, we present a new seasonal component model based on modulated periodic components, which is capable of replicating multiplicative periodic components in an efficient manner, in the sense that the number of parameters in the model is much lower than in a standard unobserved components model without modulation. The model performance compares favourably with respect to standard techniques on a rolling forecasting exercise based on actual hourly electricity load demand data at a certain transformer in the UK.

The paper outlines the main algorithms and functions available in the CAPTAIN identification and time series analysis Toolbox for Matlab.

The identification and estimation of rainfall-flow models is one of the most challenging problems in hydrology. This paper presents the results of direct continuous-time identification and estimation of a rainfall-flow model for the Canning, an ephemeral river in Western Australia, based on daily sampled data. It compares simplified and full implementations of the optimal instrumental variable algorithm used in the application and discusses the advantages of this direct method when compared with the alternative indirect approach to the problem based on discrete-time model estimation.

This paper describes an optimal instrumental variable method for identifying discrete-time transfer function models of the Box-Jenkins transfer function form in the closed-loop situation. This method is based on the Refined Instrumental Variable (RIV) algorithm which, because of an appropriate choice of particular design variables, achieves minimum variance estimation of the model parameters. The Box-Jenkins model is the most natural since it does not constrain the process and the noise models to have common polynomials. The performance of the proposed approach is evaluated by Monte Carlo analysis in comparison with other alternative closed loop estimation methods.

PurposeSince the early 1990s there has been a growth in local authorities of risk management. However, despite a range of different strategies, initiatives and practices the issue of financing the risks to which authorities are exposed has remained problematic. The traditional dependence on the commercial insurance market has proved to be a flawed strategy. This paper aims to analyse an alternative risk financing strategy which has been successful in local authorities in other countries, that of risk pooling.Design/methodology/approachThe paper analyses the rationale behind risk pools, investigates the legislative environment that appears to make these acceptable to central government and evaluates the likely benefits to local authorities of their adoption.FindingsThe paper finds that the perceived main legislative barrier to risk pools may no longer exist. Given that, there is a strategic, financial and operational case to be made for at least exploring the possibility of risk pooling. The experience from the USA would suggest that pools can have an important role to play in risk financing, and evidence now exists that a number of UK local authorities are actively pursuing pool formation.Practical implicationsThe development of risk pools is likely to result in a significant reduction in the use of conventional insurance by local authorities. The evidence would suggest that this will be beneficial, but this is subject to the proviso that actuarial, financial and managerial practice within pools is rigorous.Originality/valueThis is an under‐researched area, with almost no extant UK‐relevant academic, or indeed practitioner, literature. The paper adds to the understanding of public sector risk management and financing for both academic and practitioner audiences.

This note compares and contrasts the non-linear parameter varying (NLPV) and state-dependent parameter (SDP) model classes. It shows that, while they have similarities, the two-stage SDP modelling procedure, involving non-parametric identification, followed by parametric estimation, is quite different from the single stage NLPV procedure. In particular, the SDP procedure allows for the identification of the model structure and the nature of the non-linearities, prior to the estimation of the parameters that characterize this identified model structure. In contrast to NLPV modelling, therefore, SDP estimation opens up the ‘black box’ and reveals the inner nature of the non-linear system.

A precipitation-temperature-flow model is developed to compute flow from raw precipitation records, taking into consideration snow-melt contribution to the flow. The model does not require other measurements than flow, temperature and raw precipitation, thus resulting particularly useful in all those situations, the majority, where these are the only observed data. A Data-Based Mechanistic (DBM) modelling approach is used in order to keep at a minimum all the a-priori assumptions on the physical mechanism driving the flow formation process. The model has been applied on a classical set of data (the Jakulsa river basin, Iceland) which is well known in the non-linear modellers community.

Although structural constraints such as model order and time delay have been incorporated in the continuous time system identification since its origin, the constraints on the estimated model parameters were rarely enforced. This paper proposes a continuous time system identification approach with constraints. It shows that by incorporating physical parameter information known a priori as hard constraints, the traditional parameter estimation schemes are modified to minimize a quadratic cost function with linear inequality constraints. Using the structure of Frequency Sampling Filters as the vehicle, the paper shows that the constraints can be readily imposed on continuous time frequency response estimation and step response estimation. In particular, a priori knowledge in both time-domain and frequency domain is utilized simultaneously as the constraints for the optimal parameter solution. A Monte-Carlo simulation study with 100 noise realization is used to demonstrate the improvement of the estimation results in terms of continuous time frequency response and continuous time step response.

Computer programs to simulate traffic flow offer an opportunity to evaluate new strategies for reducing delays, congestion, fuel consumption and pollution. This paper describes a Statistical Traffic Model or STM, which is based on accepted macro-modelling concepts, such as the conservation of vehicles and the fundamental traffic diagram. In this case, the model is constructed using the well known Matlab/Simulink™ software package, so providing an integrated approach for data processing, graphical presentation of data, control system design and macroscopic simulation in one straightforward to use, widely available environment. To illustrate the methodology, the STM is applied to a section of the M3/M27 Ramp Metering Pilot Scheme in the UK. This Highways Agency sponsored project, based in the Southampton area, utilises traffic lights at the on-ramp entrances to regulate access to the main carriageway of the motorway, in an attempt to maintain flow close to the capacity. The paper utilises the model to help design a locally-coordinated ramp metering algorithm, based on proportional-integral-plus (PIP) control methods. In this manner, the STM proves particularly valuable for the application of multi-objective optimisation techniques in the design of new traffic management systems.

The subjects of ethical risk and ethical risk management are alluded to frequently in academic and practitioner literature on organizational risk management, but there is scant evidence to indicate that either issue has received deeper analysis. Thus, the question ‘What does it mean to manage ethical risks?’ remains largely unaddressed. This paper represents a first step in seeking a language for both ethical risks and ethical risk management. As such, it relies on an analytical framework developed by the Caux Round Table in its Principles for Business. This framework is the product of an extended international effort (by scholars and practitioners) to create a statement of business principles with global application. In turn, the framework supports an assessment process that allows organizations to evaluate their conformity with the principles.

Novel nonstationary and nonlinear dynamic time series analysis tools are applied to multiyear eddy covariance CO2 flux and micrometeorological data from the Harvard Forest and University of Michigan Biological Station field study sites. Firstly, the utility of these tools for partitioning the gross photosynthesis and bulk respiration signals within these series is demonstrated when employed within a simple model framework. This same framework offers a promising new method for gap filling missing CO2 flux data. Analysing the dominant seasonal components extracted from the CO2 flux data using these tools, models are inferred for daily gross photosynthesis and bulk respiration. Despite their simplicity, these models fit the data well and yet are characterized by well-defined parameter estimates when the models are optimized against calibration data. Predictive validation of the models also demonstrates faithful forecasts of annual net cumulative CO2 fluxes for these sites.

This paper is concerned with proportional-integral-plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. The PIP controller can be interpreted as a logical extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, but with inherent model-based predictive control action. In particular, the paper considers the design of an optimal, scheduled gain PIP algorithm for a 22 m3 forced ventilation test chamber at the Katholieke Universiteit Leuven. Such a PIP approach proves more robust to pressure disturbances than an equivalent PID design and constitutes a preliminary step towards the development of the complete micro-climate controller.

An on-line mathematical approach was used to model the spatio-temporal temperature distribution in the imperfectly mixed air inside a car. A second-order model proved to be a sufficiently good description of the temperature dynamics (R-2 = 0.985) of the system. Furthermore, it was possible to fully understand the physical meaning of the second-order model structure. Using this model, a proportional integral plus (PIP) climate controller was developed for a single input single output (SISO) system. The controller was able to follow a temperature level of 18-23-21degreesC at any desirable point, and to robustly react to a random local disturbance.

Outsourcing of risk management activities is a well-established practice, involving a range of services from actuarial audits to loss control training to risk financing management to claims administration services. Surprisingly, little work has been done to examine the risks associated with outsourcing risk management activities. This article examines the outsourcing of claims management services by reviewing the research on outsourcing risks and by interviewing leading practitioners. In doing so, the authors draw some provisional observations about risks and risk costs associated with outsourcing claims management services—observations that seem generalizable to all risk management outsourcing.

Initially, the paper will provide a tutorial introduction to the main aspects of existing methods for identifying linear continuous-time models from discrete-time data and show how one of these methods has been applied to the identification and estimation of a model for the transportation and dispersion of a pollutant in a river. It will then go to introduce a widely applicable class of nonlinear, State-Dependent Parameter (SDP) models for continuous or discrete-time systems. Finally, the paper will describe how this SDP approach has been used to identify and estimate a nonlinear differential equation model of global carbon cycle-dynamics and global warming.

The theory and practice of risk management has expanded in recent years, and now includes measures to assess and address almost all risks that local governments encounter. This new view of risk management places great emphasis on the proper formation and effective management of contracts, as contracting proves to be a major source of risk. This article examines contracts and risks related to outsourcing claims management services, and in doing so draws some provisional observations about all contracting and outsourcing—and the risk management issues arising from both practices.

Regionalization approaches to daily streamflow prediction are investigated for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters). Model parameters are considered to represent the dynamic response characteristics (DRCs) of a catchment. It is demonstrated that all catchments within the region cannot be assumed to have a similar hydrological behaviour, and thence a regionalization approach considering differences in physical catchment descriptors (PCDs) is required. Such a regionalization approach can be regarded as a top-down method, in the sense that factors controlling parameter variability are identified first within the entire region under study, and then such information is exploited to predict runoff in a smaller sub-region. Regionalization results reveal that consideration of interrelation s between dependent variables, which here are the parameters of the rainfall-runoff model, can improve performance of regression as a regionalization method. Breaking the parameter correlation structure inherent in the model, and exploiting merely relationships between model parameters and PCDs (no matter how weakly related they are), can result in a significant decrease in regionalization performance. Also, high significance of regression between values of PCDs and DRCs does not guarantee a set of parameters with a good predictive power. When there is a reason to believe that, in the sense of hydrological behaviour, a gauged catchment resembles the ungauged catchment, then it may be worthwhile to adopt the entire set of calibrated parameters from the gauged catchment instead of deriving quantitative relationships between catchment descriptors and model parameters. Copyright © 2003 John Wiley & Sons, Ltd.