Artificial Intelligent Methods for Handling Spatial Data. Fuzzy Rulebase Systems and Gridded Data Problems

Foreword......Page 3
Preface......Page 4
Contents......Page 5
--- Preliminaries......Page 9
1.1.1 Introduction......Page 10
1.1.2 Organization of This Book......Page 12
1.2.2 Feature Based Models......Page 13
1.2.3 Grid Based Models......Page 15
1.2.4 Digital Elevation Models......Page 16
1.2.5 Data Structuring in GIS......Page 17
2.1 Overlaying Gridded Data......Page 20
2.2.2 Regridding......Page 22
2.2.3 Spatial Disaggregation......Page 24
2.3.2 Spatial Smoothing......Page 25
2.3.4 Data Fusion......Page 27
3.1.1 Spatial Disaggregation......Page 29
3.1.2 Regridding......Page 31
3.2 The Rulebase System......Page 32
3.3.2 Inference Systems......Page 34
--- Translating the Spatial Problem......Page 35
4.1.1 Definition of a Fuzzy Set......Page 36
4.1.2 α-Cut......Page 37
4.1.3 Height......Page 38
4.3 Mamdani Rulebase Systems......Page 39
5.1.1 Concept......Page 41
5.1.2 Defining the Variables......Page 42
5.2.1 Using a Global Range......Page 43
5.2.2 Using a Local Range......Page 47
5.2.3 Estimated Ranges......Page 49
5.3.1 Input Parameters......Page 50
5.3.2 Output Parameter......Page 51
6.1 Introduction......Page 52
6.2 Generating Rules from Examples with Constant Spaces......Page 53
6.3.1 What Are Variable Spaces?......Page 54
6.3.2 Combining the Ranges......Page 55
6.3.3 Rescaling Most Possible Range Prior to Rulebase Construction......Page 57
6.4 Developed Algorithm for Learning from Examples with Variable Spaces......Page 58
7.1.1 Definition......Page 61
7.1.2 Criteria......Page 62
7.2.1 Introduction......Page 63
7.2.2 Definition......Page 65
7.2.3 Examples......Page 67
8.1 Introduction......Page 72
8.2.2 Value Comparison: Typical Approaches......Page 73
8.3.1 Preprocessing of the Reference......Page 76
8.3.2 Defining the Ranking Value......Page 77
9.1 Data......Page 79
9.3 Applying the Rulebase and Defuzzifying the Results......Page 80
9.4 Complexity......Page 81
--- Algorithm & Experiments......Page 83
10.1 Programatory Aspects......Page 84
10.2.1 Introduction......Page 85
10.2.2 Algorithm......Page 87
10.2.3 Determining the 9-Intersection Matrix......Page 88
10.3.1 Values and Possible Range......Page 90
10.4 Training Data......Page 92
11.1.1 Source Data......Page 94
11.1.2 Algorithm Settings......Page 99
11.1.3 Datasets for Disaggregation......Page 100
11.2.1 Quality of Proxy Data......Page 102
11.2.2 Disaggregation to Higher Resolutions......Page 109
11.2.3 Multiple Proxy Data......Page 111
11.3 Regridding of Warsaw Test Data......Page 115
11.3.1 Angle of the Input Grids......Page 116
11.3.3 Multiple Proxy Data......Page 119
11.4 Concluding Remarks......Page 124
12.1 Novel Aspects Presented in This Work......Page 125
12.2 Future Directions for Research......Page 127
12.3 Other Application Fields......Page 129
Refs......Page 130