Name
Abstract | ||
|---|---|---|
The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and "thick" isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69839-5_50 | ICCSA (1) |
Keywords | Field | DocType |
cross validation | Data mining,General regression neural network,Decision level,Computer science,Visualization,Interpolation,Raw data,Pollution,Automatic tuning,Uncertainty estimation | Conference |
Volume | ISSN | Citations |
5072 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikhail F. Kanevski | 1 | 154 | 19.67 |
| Vadim Timonin | 2 | 37 | 4.93 |
| Alexei Pozdnoukhov | 3 | 216 | 18.87 |