Wavelets and Field Forecast Verification

Briggs, William M.; Levine, Richard A.

doi:10.1175/1520-0493(1997)125<1329:waffv>2.0.co;2

Cited by 97 publications

(103 citation statements)

References 35 publications

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“…These new methods consider, for example, proximity [21], the presence of recognizable structures, i.e., features [22], moving windows [23] or wavelet decomposition [24]. Others have evaluated model performance based on metrics summarizing the whole landscape [25,26].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Model Validation Techniques in Land Cover Dynamics

Ahmed

Zhu

2013

IJGI

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This paper applies different methods of map comparison to quantify the characteristics of three different land change models. The land change models used for simulation are termed as -Stochastic Markov (St_Markov)‖, -Cellular Automata Markov (CA_Markov)‖ and -Multi Layer Perceptron Markov (MLP_Markov)‖ models. Various model validation techniques such as per category method, kappa statistics, components of agreement and disagreement, three map comparison and fuzzy methods have then been applied. A comparative analysis of the validation techniques has also been discussed. In all cases, it is found that -MLP_Markov‖ gives the best results among the three modeling techniques. Fuzzy set theory is the method that seems best able to distinguish areas of minor spatial errors from major spatial errors. Based on the outcome of this paper, it is recommended that scientists should try to use the Kappa, three map comparison and fuzzy methods for model validation. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of model validation techniques and articulates priorities for future research.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Model Validation Techniques in Land Cover Dynamics

Ahmed

Zhu

2013

IJGI

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“…The disadvantage of the methods is that either spatial structure (local) or spatial specificity (global) is ignored. The continuum from local to global has been investigated by multi-scale analysis on the basis of step-wise aggregations of model results and data (Costanza 1989, Kok et al 2001, Pontius Jr. 2002 and wavelet decomposition (Briggs and Levine 1997). Both aggregation and wavelet based approaches however, suffer under the rather arbitrary positioning of the coarse scale grid relative to the original grid.…”

Section: The Axis: Local Global Focalmentioning

confidence: 99%

A Framework of Map Comparison Methods to Evaluate Geosimulation Models from a Geospatial Perspective

Hagen‐Zanker

Martens

2011

Geocomputation, Sustainability and Environmental Planning

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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 the model results. Such evaluation requires a geospatial perspective; it is not sufficient if the micro-level interactions are realistic. Importantly the interactions should be such that the meso and macro level patterns that emerge from the model are realistic. In recent years, a host of map comparison methods have been developed that address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess the breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to the 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.

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“…The wavelet method has been pioneered by Briggs and Levine (1997) who decomposed the observations and forecasted fields into maps at different scales by a discrete wavelet transformation. The maps at the different scales are then compared by any similarity measure such as root mean squared error (RMSE) or anomaly correlation coefficient (ACC).…”

Section: Wavelets (Wav)mentioning

confidence: 99%

Evaluating forecasts of extreme events for hydrological applications: an approach for screening unfamiliar performance measures

Cloke

Pappenberger

2008

Meteorological Applications

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Many different performance measures have been developed to evaluate field predictions in meteorology. However, a researcher or practitioner encountering a new or unfamiliar measure may have difficulty in interpreting its results, which may lead to them avoiding new measures and relying on those that are familiar. In the context of evaluating forecasts of extreme events for hydrological applications, this article aims to promote the use of a range of performance measures. Some of the types of performance measures that are introduced in order to demonstrate a six-step approach to tackle a new measure. Using the example of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble precipitation predictions for the Danube floods of July and August 2002, to show how to use new performance measures with this approach and the way to choose between different performance measures based on their suitability for the task at hand is shown.

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Wavelets and Field Forecast Verification

Cited by 97 publications

References 35 publications

Evaluation of Model Validation Techniques in Land Cover Dynamics

Evaluation of Model Validation Techniques in Land Cover Dynamics

A Framework of Map Comparison Methods to Evaluate Geosimulation Models from a Geospatial Perspective

Evaluating forecasts of extreme events for hydrological applications: an approach for screening unfamiliar performance measures

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