Data analysis techniques for the coastal zone
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Revision as of 17:31, 20 November 2021 by Dronkers J (talk | contribs)
Here we introduce a series of Coastal Wiki articles dealing with data analysis techniques. The aim of data analysis methods is generally to find a small number of functions that resolve with sufficient accuracy the spatial and temporal properties of the data in terms of external forcing factors. The data analysis techniques presented in the Coastal Wiki are:
- Linear regression
- Principal component analysis, empirical orthogonal functions and singular spectrum analysis
- Wavelets
- Artificial neural networks
- Kriging
Each technique has advantages and disadvantages. The most suitable technique depends on the problem at hand and on the quantity and quality of the available data. In the table below we provide some guidance for choosing the most appropriate technique for the analysis of data on coastal processes.
| Analysis technique | Strengths | Limitations | Application example |
|---|---|---|---|
| Linear regression analysis | * Trend detection (linear, nonlinear) from data records * Robust, cheap, easy to implement |
* Data errors must be uncorrelated and Gaussian distributed * Error margins of interpolations and extrapolations are underestimated * Trend functions are arbitrarily chosen |
Trend analysis |
| Principal component analysis, empirical orthogonal functions and singular spectrum analysis | * Techniques are basically the same * Can handle large data sets * Identification of 'hidden' spatial (1D, 2D) or temporal patterns * Guides interpretation towards underlying processes * Enables data reduction and noise removal |
* Bias towards variables with high variance * Less suited than wavelets in case of phase-shifted patterns |
Identification of patterns in large datasets |
| Wavelets | * Analysis of irregular, non-cyclic and nonlinear processes * Can handle large data sets * Enables data reduction and noise removal * Guides interpretation towards underlying processes |
* Requires equidistant data * Not suited for small data records * Less performant than Fourier or harmonic analysis in case of regular cyclic processes |
Analysis of phenomena with strong spatial and temporal variation |
| Artificial Neural Networks | * Prediction tool based on machine learning from training data * Can handle complex nonlinear systems * Identification of major influencing factors |
* Predictions only within the range of the trained situations * Black box prediction tool * Requires large datasets * No general prescription for optimal network design * Possibly unreliable results due to overfitting * No guarantee for convergence to optimal solution |
Prediction of features driven by multiple external factors |
| Kriging | * Optimal interpolation method in case of correlated data errors * Provides uncertainty estimate * Can handle non-uniform sampling |
* Assumption that error correlations only depend on distance * Data records must be either in space or time domain |
Data records with variability at a wide range of scales |
Related articles
- Linear regression analysis of coastal processes
- Analysis of coastal processes with Empirical Orthogonal Functions
- Wavelet analysis of coastal processes
- Artificial Neural Networks and coastal applications
- Data interpolation with Kriging
References
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