Comparing modelling frameworks – A workshop approach

Argent, Robert M.; Voinov, Alexey; Maxwell, Thomas; Cuddy, Susan M.; Rahman, Joel; Seaton, Shane; Vertessy, Rob; Braddock, Roger David

doi:10.1016/j.envsoft.2005.05.004

Cited by 73 publications

(30 citation statements)

References 13 publications

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“…Various object-and component-oriented solutions have approached the issue of agricultural and environmental modeling, such as maize irrigation scheduling (Bergez et al 2001), multiple spatial scales ecosystems (Woodbury et al 2002), greenhouse control systems (Aaslyng et al 2003), weather modeling , households, landscape, and livestock integrated systems (Matthews 2006). In the same context of the agricultural and environmental modeling community, alternative frameworks have been made available to support modular model development through provision of libraries of core environmental modeling modules, as well as reusable tools for data manipulation, analysis, and visualization (Argent et al 2006). There is some consensus (Glasow and Pace 1999) that component-based development is indeed an effective and affordable way not only for creating model applications but also for conducting model evaluation.…”

Section: Modular Simulationmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

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Section: Modular Simulationmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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“…Water 2017, 9, 639 8 of 16 The total runoff for the watershed is calculated as the sum of the runoff from all tanks. As described in the previous section, infiltration is used as input data for simulating the storage for each tank for the following day, while total storage is used as input data for simulating K s .…”

Section: -Tank Modulementioning

confidence: 99%

Simulink Implementation of a Hydrologic Model: A Tank Model Case Study

Song

Her

Park

et al. 2017

Water

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Abstract:Simulink, an extension of MATLAB, is a graphics-based model development environment for system modeling and simulation. Simulink's user-friendly features, including block (data process) and arrow (data transfer) objects, a large number of existing blocks, no need to write codes, and a drag and drop interface, provide modelers with an easy development environment. In this study, a Tank model was developed using Simulink and applied to a rainfall-runoff simulation for a study watershed to demonstrate the potential of Simulink as a tool for hydrological analysis. In the example given here, the Tank model was extended by two sub-modules representing evapotranspiration and storage-runoff distribution. In addition, model pre-and post-processing, such as input data preparation and results plotting, was carried out in MATLAB. Moreover, model parameters were calibrated using MATLAB optimization tools without any additional programming for linking the calibration algorithms and the model. The graphical representation utilized in the Simulink version of the Tank model helped us to understand the hydrological interactions described in the model, and the modular structure of the program facilitated the addition of new modules and the modification of existing modules as needed. From the study, we found that Simulink could be a useful and convenient environment for hydrological analysis and model development.

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“…This is frequently based on object-oriented concepts [7,8] where individual model parts, or "modules," are designed to encapsulate a single concept, promoting flexibility in use and supporting module reuse [8][9][10][11]. Within different modeling spectra of discrete time and entities, many problems and modeling tools are amendable to approaches based on object theory and are readily addressed using appropriate software engineering designs and development methods [8]. These new approaches have led to the development of new modeling frameworks that permit the selective use of libraries of modules and have model architectures customized to address a variety of unique environmental problems [8,[12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Component-Based Modeling Framework for Agricultural Water-Resource Management

Kang

Srivastava

Song

et al. 2016

Water

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Because hydrologic responses of an agricultural watershed are influenced by many natural and man-made factors including pond/reservoir, management practices, and/or irrigation/drainage, strategies of hydrological modeling for the watershed must be case-dependent and thus carefully designed to effectively reflect their roles as critical hydrologic components in simulation processes. In this study, we propose a component-based modeling framework that accommodates a flexible modeling approach to consider a variety of hydrologic processes and management practices, especially irrigation-reservoir operation and paddy-farming practices, in watershed-scale modeling. The objectives of this study are twofold: to develop a COmponent-based Modeling Framework for Agricultural water-Resources Management (COMFARM) using an object-oriented programming technique, and to evaluate its applicability as a modeling tool to predict the responses of an agricultural watershed characterized with diverse land uses in a case study. COMFARM facilitates quick and easy development of watershed-specific hydrologic models by providing multiple interchangeable simulation routines for each hydrologic component considered. COMFARM is developed with the JAVA programming language, using Eclipse software. The framework developed in this study is applied to simulating hydrologic processes of the Seon-Am irrigation-district watershed consisting primarily of reservoir-irrigated rice paddies in South Korea. The application study clearly demonstrates the applicability of the framework as a convenient method to build models for hydrologic simulation of an agricultural watershed. The newly developed modeling framework, COMFARM is expected to serve as a useful tool in watershed management planning by allowing quick development of case-oriented analysis tools and evaluation of management scenarios customized to a specific watershed.

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Comparing modelling frameworks – A workshop approach

Cited by 73 publications

References 13 publications

Deliberative processes for comprehensive evaluation of agroecological models. A review

Deliberative processes for comprehensive evaluation of agroecological models. A review

Simulink Implementation of a Hydrologic Model: A Tank Model Case Study

Development of a Component-Based Modeling Framework for Agricultural Water-Resource Management

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