1,721,041 research outputs found
Crop modelling and validation : integration of IRENE_DLL in the WARM environment
No full textThe growing importance of biophysical models in research and application-oriented projects is driving a growing interest in
developing suitable approaches to evaluate model performance. Valuable validation techniques should assess the performance
of complex models under a variety of conditions, and should include a wide range of validation measures. After discussing
validation issues and methods currently used to assess the quality of simulation models, the integration of the software
component for model output evaluation IRENE_DLL within the rice modelling system WARM is illustrated. The
purpose is to demonstrate, via a case study, that great utility in validation can be gained through the implementation and use
of object-oriented software tools targeting at modularity and reusability inside a modelling environment. This facilitates
model validation sessions and extensibility of tools towards new approaches possibly coming out of research. These challenges
can be met by using a wide range of approaches and by expanding horizons in validation whilst tailoring the evaluation
requirements to the specific objectives of the model application. The availability of appropriate software tools allow
actions that are not frequently executed within the context of project-based modelling activities, thus helping the dissemination
of validation experiences and preventing future modelling projects from repetition of validation efforts
A software component to compute agro-meteorological indicators
No full textClimIndices is a software component to compute agro-meteorological indicators on yearly series of daily weather data. The component is released as .NET2 dynamic link libraries (DLL), allowing the development of clients under Windows using .NET languages. The design allows extending the computing capabilities without requiring re-compilation
New indices to quantify patterns of residuals produced by model estimates
No full textThe evaluation of patterns in the residuals of model estimates vs. other variables can be useful in both model evaluation and parameter calibration. New indices that allow quantifying such patterns (pattern indices) are presented. Groups of residuals are created by dividing the range of the variable under evaluation into two, three, four, or five subranges. Two types of indices are proposed. The first type (PI-type) is based on the absolute value of the maximum difference between pairwise comparisons among average residuals of each group of residuals. A variant of this index is computed by using variance ratios (PI-F type). The subranges of the variable that determines the grouping of residuals may be of equal length (PI) or variable length (PIv). In the second case, they are generated by an algorithm that optimizes subranges to maximize patterns. The power of the diverse pattern indices at identifying patterns was investigated, and their effectiveness was compared against the runs test. Critical values for pattern indices were generated by Monte Carlo simulations. Monte Carlo probability tables, the results of power analysis, and the results of using pattern indices at two case studies (i.e., daily radiation and soil water content estimates) were presented. The analysis based on pattern indices provided insight in model structure and parameter calibration. Pattern indices also allowed evaluating model performance and discriminating among alternative models. Higher power in identifying patterns was given by range-based pattern indices than by those based on variance ratios
Historical reconstruction of erosive storms driving damaging hydrological events in Southern Italy (Bonea basin)
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Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice
No full textWARM (Water Accounting Rice Model) simulates paddy rice (Oryza sativa L.), based on temperature-driven development and radiation-driven crop growth. It also simulates: biomass partitioning, floodwater effect on temperature, spikelet sterility, floodwater and chemicals management, and soil hydrology. Biomass estimates from WARM were evaluated and compared with the ones from two generic crop models (CropSyst, WOFOST). The test-area was the Po Valley (Italy). Data collected at six sites from 1989 to 2004 from rice crops grown under flooded and non-limiting conditions were split into a calibration (to estimate some model parameters) and a validation set. For model evaluation, a fuzzy-logic based multiple-metrics indicator (MQI) was used: 0 (best) ≤ MQI ≤ 1 (worst). WARM estimates compared well with the actual data (mean MQI = 0.037 against 0.167 and 0.173 with CropSyst and WOFOST, respectively). On an average, the three models performed similarly for individual validation metrics such as modelling efficiency (EF > 0.90) and correlation coefficient (R > 0.98). WARM performed best in a weighed measure of the Akaike Information Criterion: (worst) 0 < wk < 1 (best), considering estimation accuracy and number of parameters required to achieve it (mean wk = 0.983 against 0.007 and ∼0.000 with CropSyst and WOFOST, respectively). WARM results were sensitive to 30% of the model parameters (ratio being lower with both CropSyst, <10%, and WOFOST, <20%), but appeared the easiest model to use because of the lowest number of crop parameters required (10 against 15 and 34 with CropSyst and WOFOST, respectively). This study provides a concrete example of the possibilities offered using a range of assessment metrics to evaluate model estimates, predictive capabilities, and complexity
An extensible model library for generating wind speed data
No full textWind is a library providing a collection of stochastic approaches to generate wind speed data on daily and hourly time steps. Daily generation refers to as mean, maximum and minimum daily wind speed. The library is made available as software component, inclusive of hypertext help file, allowing the development of language-independent clients under Windows operating systems. The component includes advanced features for re-use in custom developed applications, and it allows independent extensions by third parties without requiring its re-compilation. Illustrative examples on how to extend and re-use the library are provided as C# code projects
IRENE_DLL: A Class Library for Evaluating Numerical Estimates
No full textIRENE_DLL (Integrated Resources for Evaluating Numerical Estimates-Dynamic Link Library) is a Microsoft (MS) COM (component object model) class library providing a set of routines designed to facilitate the implementation of model evaluation techniques. Statistical procedures (difference-based analysis, correlation-regression analysis, probability distributions, pattern analysis, statistics aggregation, and time mismatch analysis) are applied to allow comparing estimates against measurements, either individually taken or replicated. The dynamic link library (DLL) can be easily interfaced with applications developed under a MS Windows programming language. An essential description of the program is given along with the basic concepts of usage
Software Component for Model Output Evaluation
No full textAs the role of biophysical models in ecological, biological and agronomic areas grows in importance, there is an associated increase in the need for suitable approaches to evaluate the adequacy of model outputs (model testing, often referred as to "validation"). Effective testing techniques are required to assess complex models under a variety of conditions, including a wide range of validation measures, possibly integrated into composite metrics. Both simple and composite metrics are being proposed by the scientific community, continuously broadening the pool of options for model evaluation. However, such new metrics are not available in commonly used statistical packages. At the same time, the large amount of data generally involved in model testing makes the operational use of new metrics a labour-consuming process, even more when composite metrics are meant to be used. An extensible and easily reusable library encapsulating such metrics would be an operational way to share the knowledge developed on model testing. The emergence of the component-oriented programming in model-based simulation has fostered debate on the reuse of models. There is a substantial consensus that componentbased development is indeed an effective and affordable way of creating model applications, if components meet via their architecture a set of requirements which make them scalable, transparent, robust, easily reusable, and extensible. This paper illustrates the Windows .NET2 component IRENE (Integrated Resources for Evaluating Numerical Estimates) and a first prototype application using it, SOE (Simulation Output Evaluator), to present a concrete application matching the above requirements in the area of model testing
IRENE : a software to evaluate model performance
No full textThe software IRENE (Integrated Resources for Evaluating Numerical Estimates) is a data analysis tool designed to provide easy access to statistical techniques for use in model evaluation. Mostly, non-replicated model estimates (Ei) are compared against non-replicated measurements (Mi). The software also allows comparing individual estimates against replicated measurements (or vice versa) and replicated estimates against replicated measurements. The evaluation of model performance is essentially based on the difference Ei-Mi, or on the correlation-regression of Ei vs. Mi (or vice versa). In addition, model evaluation by probability distributions, pattern analysis, or fuzzy-based aggregation statistics is allowed. Graphics are included in most analytical tasks. The results are displayed in separate spreadsheets and can be exported into MS Excel workbooks
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