Advanced mapping of environmental data: geostatistics, machine learning and Bayesian maximum entropy
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| Other Authors: | |
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| Format: | Book |
| Language: | English |
| Published: |
London
ISTE [u.a.]
2008
|
| Edition: | 1. publ. |
| Series: | Geographical information systems series
|
| Subjects: | |
| Online Access: | Publisher description Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIII, 313 S. Ill., graph. Darst., Kt. |
| ISBN: | 9781848210608 |
Staff View
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| 245 | 1 | 0 | |a Advanced mapping of environmental data |b geostatistics, machine learning and Bayesian maximum entropy |c ed. by Mikhail Kanevski |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a London |b ISTE [u.a.] |c 2008 | |
| 300 | |a XIII, 313 S. |b Ill., graph. Darst., Kt. | ||
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| 490 | 0 | |a Geographical information systems series | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Geologie | |
| 650 | 4 | |a Geology |x Statistical methods | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 700 | 1 | |a Kanevski, Mikhail |4 edt | |
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Record in the Search Index
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|---|---|
| adam_text | Titel: Advanced mapping of environmental data
Autor: Kanevski, Mikhail
Jahr: 2008
Table of Contents
Preface.............................................xi
Chapter 1. Advanced Mapping of Environmental Data: Introduction..... 1
M. KANEVSKI
1.1. Introduction...................................... 1
1.2. Environmental data analysis: problems and methodology........... 3
1.2.1. Spatial data analysis: typical problems.................... 3
1.2.2. Spatial data analysis: methodology...................... 5
1.2.3. Model assessment and model selection................... 8
1.3. Resources...................................... 12
1.3.1. Books, tutorials................................ 12
1.3.2. Software..................................... 12
1.4. Conclusion...................................... 14
1.5. References...................................... 15
Chapter 2. Environmental Monitoring Network Characterization and
Clustering.......................................... 19
D. TUIA and M. KANEVSKI
2.1. Introduction..................................... 19
2.2. Spatial clustering and its consequences..................... 20
2.2.1. Global parameters............................... 21
2.2.2. Spatial predictions............................... 22
2.3. Monitoring network quantification........................ 23
2.3.1. Topological quantification.......................... 23
2.3.2. Global measures of clustering........................ 23
2.3.2.1. Topological indices........................... 23
2.3.2.2. Statistical indices............................. 24
2.3.3. Dimensional resolution: fractal measures of clustering......... 26
2.3.3.1. Sandbox method............................. 27
vi Advanced Mapping of Environmental Data
2.3.3.2. Box-counting method.......................... 30
2.3.3.3. Lacunarity................................. 33
2.4. Validity domains..................................
2.5. Indoor radon in Switzerland: an example of a real monitoring network. . 36
2.5.1. Validity domains................................ 37
2.5.2. Topological index............................... 37
2.5.3. Statistical indices............................... 38
2.5.3.1. Morisita index.............................. 3^
2.5.3.2. AT-function................................. 39
2.5.4. Fractal dimension............................... 40
2.5.4.1. Sandbox and box-counting fractal dimension............ 40
2.5.4.2. Lacunarity................................. 42
2.6. Conclusion...................................... 43
2.7. References...................................... 44
Chapter 3. Geostatistics: Spatial Predictions and Simulations......... 47
E. SAVELIEVA, V. DEMYANOV and M. MAIGNAN
3.1. Assumptions of geostatistics........................... 47
3.2. Family of kriging models............................. 49
3.2.1. Simple kriging................................. 50
3.2.2. Ordinary kriging................................ 50
3.2.3. Basic features of kriging estimation.................... 51
3.2.4. Universal kriging (kriging with trend)................... 56
3.2.5. Lognormal kriging............................... 56
3.3. Family of co-kriging models........................... 58
3.3.1. Kriging with linear regression........................ 58
3.3.2. Kriging with external drift.......................... 58
3.3.3. Co-kriging................................... 59
3.3.4. Collocated co-kriging............................. 60
3.3.5. Co-kriging application example....................... 61
3.4. Probability mapping with indicator kriging................... 64
3.4.1. Indicator coding................................ 64
3.4.2. Indicator kriging................................ 66
3.4.3. Indicator kriging applications........................ 69
3.4.3.1. Indicator kriging for241 Am analysis................. 69
3.4.3.2. Indicator kriging for aquifer layer zonation............. 71
3.4.3.3. Indicator kriging for localization of crab crowds.......... 74
3.5. Description of spatial uncertainty with conditional stochastic simulations 76
3.5.1. Simulation vs. estimation...................... 76
3.5.2. Stochastic simulation algorithms................ 77
3.5.3. Sequential Gaussian simulation....................... 81
3.5.4. Sequential indicator simulations...................... 84
Table of Contents vii
3.5.5. Co-simulations of correlated variables................... 88
3.6. References...................................... 92
Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning
Algorithms.......................................... 95
F. RATLE, A. POZDNOUKHOV, V. DEMYANOV, V. TIMONIN and
E. SAVELIEVA
4.1. Introduction..................................... 95
4.2. Machine learning: an overview.......................... 96
4.2.1. The three learning problems......................... 96
4.2.2. Approaches to learning from data...................... 100
4.2.3. Feature selection................................ 101
4.2.4. Model selection................................ 103
4.2.5. Dealing with uncertainties.......................... 107
4.3. Nearest neighbor methods............................. 108
4.4. Artificial neural network algorithms....................... 109
4.4.1. Multi-layer perceptron neural network................... 109
4.4.2. General Regression Neural Networks................... 119
4.4.3. Probabilistic Neural Networks........................ 122
4.4.4. Self-organizing (Kohonen) maps...................... 124
4.5. Statistical learning theory for spatial data: concepts and examples..... 131
4.5.1. VC dimension and structural risk minimization............. 131
4.5.2. Kernels..................................... 132
4.5.3. Support vector machines........................... 133
4.5.4. Support vector regression.......................... 137
4.5.5. Unsupervised techniques........................... 141
4.5.5.1. Clustering................................. 142
4.5.5.2. Nonlinear dimensionality reduction.................. 144
4.6. Conclusion....................................... 146
4.7. References...................................... 146
Chapter 5. Advanced Mapping of Environmental Spatial Data:
Case Studies......................................... 149
L. FORESTI, A. POZDNOUKHOV, M. KANEVSKI, V. TIMONIN,
E. SAVELIEVA, C. KAISER, R. TAPIA and R. PURVES
5.1. Introduction..................................... 149
5.2. Air temperature modeling with machine learning algorithms
and geostatistics..................................... 150
5.2.1. Mean monthly temperature......................... 151
5.2.1.1. Data description............................. 151
5.2.1.2. Variography................................ 152
5.2.1.3. Step-by-step modeling using a neural network........... 153
viii Advanced Mapping of Environmental Data
5.2.1.4. Overfitting and undertraining.....................154
5.2.1.5. Mean monthly air temperature prediction mapping........156
5.2.2. Instant temperatures with regionalized linear dependencies......159
5.2.2.1. The Föhn phenomenon.........................159
5.2.2.2. Modeling of instant air temperature influenced by Föhn.....160
5.2.3. Instant temperatures with nonlinear dependencies............163
5.2.3.1. Temperature inversion phenomenon................. 163
5.2.3.2. Terrain feature extraction using Support Vector Machines . ... 164
5.2.3.3. Temperature inversion modeling with MLP............. 165
5.3. Modeling of precipitation with machine learning and geostatistics..... 168
5.3.1. Mean monthly precipitation.........................169
5.3.1.1. Data description.............................169
5.3.1.2. Precipitation modeling with MLP...................171
5.3.2. Modeling daily precipitation with MLP..................173
5.3.2.1. Data description............................. 173
5.3.2.2. Practical issues of MLP modeling................... 174
5.3.2.3. The use of elevation and analysis of the results........... 177
5.3.3. Hybrid models: NNRK and NNRS..................... 179
5.3.3.1. Neural network residual kriging.................... 179
5.3.3.2. Neural network residual simulations................. 182
5.3.4. Conclusions................................... 184
5.4. Automatic mapping and classification of spatial data using machine
learning.......................................... 185
5.4.1. k-nearest neighbor algorithm........................185
5.4.1.1. Number of neighbors with cross-validation.............187
5.4.2. Automatic mapping of spatial data.....................187
5.4.2.1. KNN modeling..............................188
5.4.2.2. GRNN modeling.............................190
5.4.3. Automatic classification of spatial data..................192
5.4.3.1. KNN classification.......................... 193
5.4.3.2. PNN classification............................ I94
5.4.3.3. Indicator kriging classification..................... I97
5.4.4. Automatic mapping - conclusions..................... I99
5.5. Self-organizing maps for spatial data-case studies.............200
5.5.1. SOM analysis of sediment contamination................. 200
5.5.2. Mapping of socio-economic data with SOM............... 204
5.6. Indicator kriging and sequential Gaussian simulations for probability
mapping. Indoor radon case study.............. 209
5.6.1. Indoor radon measurements................ 209
5.6.2. Probability mapping................ 211
5.6.3. Exploratory data analysis.................. 212
5.6.4. Radon data variography............ 216
5.6.4.1. Variogram for indicators................ 216
Table of Contents ix
5.6.4.2. Variogram for Nscores.........................217
5.6.5. Neighborhood parameters..........................218
5.6.6. Prediction and probability maps.......................219
5.6.6.1. Probability maps with IK........................219
5.6.6.2. Probability maps with SGS.......................220
5.6.7. Analysis and validation of results......................221
5.6.7.1. Influence of the simulation net and the number of neighbors. . . 221
5.6.7.2. Decision maps and validation of results...............222
5.6.8. Conclusions...................................225
5.7. Natural hazards forecasting with support vector machines - case study:
snow avalanches.....................................225
5.7.1. Decision support systems for natural hazards............... 227
5.7.2. Reminder on support vector machines................... 228
5.7.2.1. Probabilistic interpretation of SVM.................. 229
5.7.3. Implementing an SVM for avalanche forecasting............ 230
5.7.4. Temporal forecasts.............................. 230
5.7.4.1. Feature selection............................. 231
5.7.4.2. Training the SVM classifier...................... 232
5.7.4.3. Adapting SVM forecasts for decision support............ 233
5.7.5. Extending the SVM to spatial avalanche predictions.......... 237
5.7.5.1. Data preparation.............................237
5.7.5.2. Spatial avalanche forecasting......................239
5.7.6. Conclusions...................................241
5.8. Conclusion......................................241
5.9. References.......................................242
Chapter 6. Bayesian Maximum Entropy - BME..................247
G. CHRJSTAKOS
6.1. Conceptual framework...............................247
6.2. Technical review of BME.............................251
6.2.1. The spatiotemporal continuum....................... 251
6.2.2. Separable metric structures......................... 253
6.2.3. Composite metric structures......................... 255
6.2.4. Fractal metric structures........................... 256
6.3. Spatiotemporal random field theory....................... 257
6.3.1. Pragmatic S/TRF tools............................ 258
6.3.2. Space-time lag dependence: ordinary S/TRF............... 260
6.3.3. Fractal S/TRF................................. 262
6.3.4. Space-time heterogenous dependence: generalized S/TRF....... 264
6.4. About BME..................................... 267
6.4.1. The fundamental equations.........................267
6.4.2. A methodological outline..........................273
x Advanced Mapping of Environmental Data
6.4.3. Implementation of BME: the SEKS-GUI.................275
6.5. Abrief review of applications..........................281
6.5.1. Earth and atmospheric sciences.......................282
6.5.2. Health, human exposure and epidemiology................291
6.6. References......................................299
List of Authors.......................................307
Index.............................................309
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| spelling | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy ed. by Mikhail Kanevski 1. publ. London ISTE [u.a.] 2008 XIII, 313 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Geographical information systems series Includes bibliographical references and index Geologie Geology Statistical methods Machine learning Bayesian statistical decision theory Kanevski, Mikhail edt http://www.loc.gov/catdir/enhancements/fy0834/2008016237-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017764008&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy Geologie Geology Statistical methods Machine learning Bayesian statistical decision theory |
| title | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy |
| title_auth | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy |
| title_exact_search | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy |
| title_full | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy ed. by Mikhail Kanevski |
| title_fullStr | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy ed. by Mikhail Kanevski |
| title_full_unstemmed | Advanced mapping of environmental data geostatistics, machine learning and Bayesian maximum entropy ed. by Mikhail Kanevski |
| title_short | Advanced mapping of environmental data |
| title_sort | advanced mapping of environmental data geostatistics machine learning and bayesian maximum entropy |
| title_sub | geostatistics, machine learning and Bayesian maximum entropy |
| topic | Geologie Geology Statistical methods Machine learning Bayesian statistical decision theory |
| topic_facet | Geologie Geology Statistical methods Machine learning Bayesian statistical decision theory |
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