Intelligent retrieval of archived meteorological data

Jones, Eric K.; Roydhouse, Aaron

doi:10.1109/64.483252

Cited by 14 publications

(7 citation statements)

References 6 publications

(1 reference statement)

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“…KBS technology is a companion to expert systems, one of the most successful branches of artificial intelligence research (Doukidis & Whitley 1988, Studer et al 1999. KBS has been applied in the fields of computer science, natural resource management (Schmoldt & Rauscher 1996), hydrologic modeling (Lam & Swayne 1993), meteorology (Jones & Roydhouse 1994), and GIS (geographical information systems; Yuan 1997). Applications of KBS to spatial problems in GIS are a particularly useful analogue for climate mapping, because both involve spatial problems, which are inherently difficult to solve using generalized computational techniques.…”

mentioning

confidence: 99%

A knowledge-based approach to the statistical mapping of climate

Daly

Gibson²,

Taylor³

et al. 2002

Clim. Res.

993

797

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The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the production of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowledge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeatability. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; testing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.KEY WORDS: Climate map · Knowledge-based system · Climate interpolation · Spatial climate · Climate data sets · GIS · PRISM · Geospatial climatology Resale or republication not permitted without written consent of the publisherClim Res 22: [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113] 2002 expert and statistical. Human-expert methods use human experience, expertise, and knowledge acquisition capabilities to infer climate patterns from meteorological regimes, physiographic features, biotic character...

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mentioning

confidence: 99%

A knowledge-based approach to the statistical mapping of climate

Daly

Gibson²,

Taylor³

et al. 2002

Clim. Res.

993

797

View full text Add to dashboard Cite

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“…We propose a quantitative characterization technique that can be applied to both homogeneous and non-homogeneous regions [32]. Although the characterization of ROIs is of particular interest in meteorological [33] or medical image databases [6], the methodology we propose here can be applied to other types of image data as well.…”

Section: Methodsmentioning

confidence: 99%

Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data

Kontos

Megalooikonomou

2005

Pattern Recognition

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We propose a method for characterizing spatial region data. The method efficiently constructs a kdimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural and internal volume properties. We evaluate our approach by performing experiments on classification and similarity searches, using artificial and real datasets. To generate artificial regions we introduce a region growth model. Similarity searches on artificial data demonstrate that our technique, although straightforward, compares favorably to mathematical morphology, while being two orders of magnitude faster. Experiments with real datasets show its effectiveness and general applicability.

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“…Intelligent technologies are usually required to utilize context-based explanation of past decisions for real-time decision-making. These technologies include the use of fuzzy sets, multiple criteria decision making, neural networks, case based reasoning, and other soft computing tools that facilitate intelligent retrieval of cases (Chan, 2005;Dash, Liew, Rahman, & Ramakrishna, 1995;Hansen, 1997;Jeng & Liang, 1995;Jones & Roydhouse, 1995;Wang, 2003;Yen, 1999;Yeung & Tsang, 1994;Zaheeruddin & Jain, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%