A Nonlinear Local Approximation Approach for Catchment Classification Article Swipe
Shakera K. Khan
,
Bellie Sivakumar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/e26030218
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/e26030218
Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.
Related Topics
Concepts
Predictability
Extrapolation
Streamflow
Curse of dimensionality
Nonlinear system
Phase space
Computer science
Interpolation (computer graphics)
Dimension (graph theory)
Embedding
Mathematics
Data mining
Artificial intelligence
Statistics
Drainage basin
Geography
Physics
Motion (physics)
Cartography
Quantum mechanics
Pure mathematics
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e26030218
- https://www.mdpi.com/1099-4300/26/3/218/pdf?version=1709218646
- OA Status
- gold
- Cited By
- 1
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392354357
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392354357Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/e26030218Digital Object Identifier
- Title
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A Nonlinear Local Approximation Approach for Catchment ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-29Full publication date if available
- Authors
-
Shakera K. Khan, Bellie SivakumarList of authors in order
- Landing page
-
https://doi.org/10.3390/e26030218Publisher landing page
- PDF URL
-
https://www.mdpi.com/1099-4300/26/3/218/pdf?version=1709218646Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1099-4300/26/3/218/pdf?version=1709218646Direct OA link when available
- Concepts
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Predictability, Extrapolation, Streamflow, Curse of dimensionality, Nonlinear system, Phase space, Computer science, Interpolation (computer graphics), Dimension (graph theory), Embedding, Mathematics, Data mining, Artificial intelligence, Statistics, Drainage basin, Geography, Physics, Motion (physics), Cartography, Quantum mechanics, Pure mathematics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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- Related works (count)
-
10Other works algorithmically related by OpenAlex
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