Research Institute for Knowledge Systems

Maastricht, Netherlands

Research Institute for Knowledge Systems

Maastricht, Netherlands
Time filter
Source Type

Newman J.P.,University of Adelaide | Newman J.P.,Bushfire and Natural Hazards Cooperative Research Center | Maier H.R.,University of Adelaide | Maier H.R.,Bushfire and Natural Hazards Cooperative Research Center | And 14 more authors.
Environmental Modelling and Software | Year: 2017

Natural hazard risk is largely projected to increase in the future, placing growing responsibility on decision makers to proactively reduce risk. Consequently, decision support systems (DSSs) for natural hazard risk reduction (NHRR) are becoming increasingly important. In order to provide directions for future research in this growing area, a comprehensive classification system for the review of NHRR-DSSs is introduced, including scoping, problem formulation, the analysis framework, user and organisational interaction with the system, user engagement, monitoring and evaluation. A review of 101 papers based on this classification system indicates that most effort has been placed on identifying areas of risk and assessing economic consequences resulting from direct losses. However, less effort has been placed on testing risk-reduction options and considering future changes to risk. Furthermore, there was limited evidence within the reviewed papers on the success of DSSs in practice and whether stakeholders participated in DSS development and use. © 2017

Maier H.R.,University of Adelaide | Guillaume J.H.A.,Aalto University | van Delden H.,University of Adelaide | van Delden H.,Research Institute for Knowledge Systems | And 4 more authors.
Environmental Modelling and Software | Year: 2016

A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of "best-guess" future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the development of strategies that are best suited to dealing with a deeply uncertain future. © 2016 Elsevier Ltd.

van Vliet J.,Research Institute for Knowledge Systems | Hurkens J.,Research Institute for Knowledge Systems | White R.,Memorial University of Newfoundland | van Delden H.,Research Institute for Knowledge Systems
Environment and Planning B: Planning and Design | Year: 2012

In recent decades several methods have been proposed to simulate land-use changes in a spatially explicit way. In these models land is generally represented on a lattice with cell states indicating the predominant land use. Since a cell can have only one state, mixed land uses and different densities of one land use can only be introduced superficially, as separate cell states. In this paper we describe a cellular automata model that simulates dynamics in both land uses and activities, where activities represent quantitative information, such as the number of inhabitants at a location. Therefore each cell has associated with it (1) a value representing one of a finite set of land-use classes, and (2) a vector of numerical values representing the quantity of each modelled activity that is present at that location. This allows simulation of incremental changes as well as mixed land uses. The proposed model is tested with a synthetic application that uses two activities: population and jobs. It simulates the emergence of human settlements over time from local inter- actions between activities and land uses. Assessment of results indicates that the model generates realistic urbanization patterns. © 2011 Pion Ltd and its Licensors.

Volk M.,Helmholtz Center for Environmental Research | Lautenbach S.,Helmholtz Center for Environmental Research | Van Delden H.,Research Institute for Knowledge Systems | Newham L.T.H.,Australian National University | Seppelt R.,Helmholtz Center for Environmental Research
Environmental Management | Year: 2010

This article analyses the benefits and shortcomings of the recently developed decision support systems (DSS) FLUMAGIS, Elbe-DSS, CatchMODS, and MedAction. The analysis elaborates on the following aspects: (i) application area/decision problem, (ii) stakeholder interaction/users involved, (iii) structure of DSS/model structure, (iv) usage of the DSS, and finally (v) most important shortcomings. On the basis of this analysis, we formulate four criteria that we consider essential for the successful use of DSS in landscape and river basin management. The criteria relate to (i) system quality, (ii) user support and user training, (iii) perceived usefulness and (iv) user satisfaction. We can show that the availability of tools and technologies for DSS in landscape and river basin management is good to excellent. However, our investigations indicate that several problems have to be tackled. First of all, data availability and homogenisation, uncertainty analysis and uncertainty propagation and problems with model integration require further attention. Furthermore, the appropriate and methodological stakeholder interaction and the definition of 'what end-users really need and want' have been documented as general shortcomings of all four examples of DSS. Thus, we propose an iterative development process that enables social learning of the different groups involved in the development process, because it is easier to design a DSS for a group of stakeholders who actively participate in an iterative process. We also identify two important lines of further development in DSS: the use of interactive visualization tools and the methodology of optimization to inform scenario elaboration and evaluate trade-offs among environmental measures and management alternatives. © 2009 Springer Science+Business Media, LLC.

van Delden H.,Research Institute for Knowledge Systems | van Vliet J.,Research Institute for Knowledge Systems | Rutledge D.T.,Landcare Research | Kirkby M.J.,University of Leeds
Agriculture, Ecosystems and Environment | Year: 2011

Recently an increasing number of integrated land-use models have become available that support policy making. Inevitably, their model components represent processes that act on different scales and that use different levels of detail to represent those processes. Therefore, it is a challenge to integrate them properly. In this paper we analyse and compare scaling issues from four integrated models that are explicitly spatial and dynamic. All have a strong agricultural component and are developed to support policy making. From these examples we find that scaling issues in model integration typically involve trade-offs among four factors: (1) the scale at which end users or policy makers require information; (2) the scale at which processes take place and the representation of those processes in a single model; (3) the way to integrate model components representing processes occurring at different scales; and (4) the limitations posed by practical restrictions such as data limitations and computation speed. Furthermore we conclude that the complexity of the model components and the spatial and temporal resolutions applied in the models are generally related to the size of the study area, while its thematic resolution is mostly driven by user requirements. Finally we argue that more detail does not necessarily generate better results and might even give a false impression of the model's accuracy. © 2011 Elsevier B.V.

Van Delden H.,Research Institute for Knowledge Systems | Seppelt R.,Helmholtz Center for Environmental Research | White R.,Research Institute for Knowledge Systems | White R.,Memorial University of Newfoundland | Jakeman A.J.,Australian National University
Environmental Modelling and Software | Year: 2011

The development of Decision Support Systems (DSS) to inform policy making has been increasing rapidly. This paper aims to provide insight into the design and development process of policy support systems that incorporate integrated models. It will provide a methodology for the development of such systems that attempts to synthesize knowledge and experience gained over the past 15-20 years from developing a suite of these DSSs for a number of users in different geographical contexts worldwide.The methodology focuses on the overall iterative development process that includes policy makers, scientists and IT-specialists. The paper highlights important tasks in model integration and system development and illustrates these with some practical examples from DSS that have dynamic, spatial and integrative attributes.Crucial integrative features of modelling systems that aim to provide support to policy processes, and to which we refer as integrated Decision Support Systems, are:. •Synthesis of relevant drivers, processes and characteristics of the real world system at relevant spatial and temporal scales.•An integrated approach linking economic, environmental and social domains.•Connection to the policy context, interest groups and end-users.•Engagement with the policy process.•Ability to provide added value to the current decision-making practice.With this paper we aim to provide a methodology for the design and development of these integrated Decision Support Systems that includes the 'hard' elements of model integration and software development as well as the 'softer' elements related to the user-developer interaction and social learning of all groups involved in the process. © 2010 Elsevier Ltd.

Hagen-Zanker A.,Research Institute for Knowledge Systems | Hagen-Zanker A.,TU Eindhoven | Martens P.,Maastricht University
Studies in Computational Intelligence | Year: 2011

Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro-level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in model results. Such evaluation requires a geospatial perspective; it is not sufficient if micro-level interactions are realistic. Importantly, interactions should be such that meso- and macro- level patterns emerging from the model are realistic. In recent years, a host of map comparison methods have been developed, which address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions. © 2011 Springer-Verlag Berlin Heidelberg.

Van Delden H.,Research Institute for Knowledge Systems | McDonald G.,Market Economics Ltd.
Modelling for Environment's Sake: Proceedings of the 5th Biennial Conference of the International Environmental Modelling and Software Society, iEMSs 2010 | Year: 2010

Although in the past decades several attempts have been made to integrate economic models with land use change (LUC) models, none of them have been fully satisfactory. In this paper we analyse four different integrated models for policy support that include economic and LUC models. We describe several functional forms used to integrate LUC and economic models, highlighting the various strengths and weaknesses of each form, and in turn, suggesting possible pathways for improved integration. Analysing the concepts and underlying assumptions of both types of models show their vast difference. When integrating these models, underlying assumptions and limitations of the existing individual models are passed on to the integrated model. A proper integration therefore requires a thorough understanding of the underlying theories of both types of models and a solution at this theoretical level. We argue that concepts of evolutionary economics and -spatially explicit- agent based modelling, where creation of ideas and learning are embodied into fully integrated LUC and economic models, provide some key mechanisms for bridging the described gap. These approaches are however very data demanding and have -at least at present- major limitations in performing a proper calibration and validation.

van Vliet J.,Research Institute for Knowledge Systems | Bregt A.K.,Wageningen University | Hagen-Zanker A.,University of Cambridge
Ecological Modelling | Year: 2011

Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K Simulation are compared using the results of a land-use model. It is found that only K Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time. © 2011 Elsevier B.V.

Shi Y.-E.,Research Institute for Knowledge Systems | Ray R.K.,Indian Institute of Technology Mandi | Nguyen K.D.,University Paris Est Creteil
Computers and Fluids | Year: 2013

A numerical model is presented for simulating shallow-water flows over dry and irregular bottom. The model is based on a projection method, which consists of combining the momentum and continuity equations in order to establish a Poisson-type equation for the water surface level. The computed domain is discretised by finite volumes on an unstructured grid. A 2nd-order upwind scheme coupled with a Least Square technique is used for handling advection terms. The present model ensures the exact C-property that has been theoretically and numerically demonstrated in this paper. Wetting and drying treatment technique used in the model has been also validated by several benchmark tests. The accuracy, stability and reliability of the present model are verified by comparing numerical results with observed data for the Malpasset dam-break event. © 2013 Elsevier Ltd.

Loading Research Institute for Knowledge Systems collaborators
Loading Research Institute for Knowledge Systems collaborators