Rules for responsible modeling - 4th edition

Now available from CHI, this is the fourth edition (2003) of a popular and well-used book that provides important background to the functions and attributes of the PCSWMM program. Seventeen chapters (listed below) are presented, all peer-reviewed for relevance and clarity. The 317 page book is presented in a beautiful, glossy, wire bound, soft cover format complete with references and glossary.

James, William, 1937- , Editor
Compiled and published by CHI, Guelph, Ontario, Canada.
International Standard Book Number 0-9683681-5-8
CHI Catalog Number: R184

Contents

1. Introduction 1
1.1 What is a model? 2
1.2 Introduction to the development of deterministic models 5
1.3 Newtonian mechanics, partial differential equations, finite difference equations and computer programs 6
1.4 When to use a model 10
1.5 How a model relates to design problems 11
1.6 Concluding remarks 13
References 14
Bibliography 14

2. How to Discretize and Disaggregate, and More on Complexity 15
2.1 Introduction 15
2.2 The procedure of discretization 15
2.3 Introduction to spatial discretization 18
2.4 Introduction to temporal resolution 19
2.5 More introductory discussion of model complexity 20
2.6 Brief introduction to data collection 28
2.7 Introduction to graphical data management 29
2.8 Summary 30
References 31
Bibliography 31

3. Reliability of Input Parameters, Good Input Data Files and
File Management. 33
3.1 Introduction 33
3.2 Short-term time series dataset for calibration. 34
3.3 Long-term dataset for inference. 34
3.4 Categories of uncertain data 35
3.5 Some samples of well-documented data files 52
3.6 Urban water systems GIS concepts 58
3.7 Summary 60
References 60
Bibliography 60

4. Optimal Model Complexity 61
4.1 Introduction 61
4.2 Earlier work on complexity 63
4.3 Choosing the best complexity 65
4.4 Distinguishing between level of detail and complexity 66
4.5 Objective measures of model complexity 68
4.6 Data availability and model complexity 70
4.7 Complexity, reliability and uncertainty 71
4.8 Relationship between complexity and available data 73
4.9 Concluding remarks 75
References 76
Bibliography 77

5. Continuous Models 79
5.1 Summary 79
5.2 Introduction 79
5.3 "Populution" issues 81
5.4 Coming to terms with sustainability issues 82
5.5 Some eco-ethical concerns 84
5.6 Processes relevant to ecosystems 86
5.7 Concluding discussion 88
References 89
Bibliography 90

6. Introduction to Generation of Long-Term Rain Input 91
6.1 Introduction 91
6.2 Review of rainfall modeling and analysis techniques 93
6.3 Stochastic models 94
6.4 Disaggregation models 96
References 97
Bibliography 98

7. Dynamic Rain Systems 103
7.1 Introduction 103
7.2 Kinematics of storm cells 104
7.3 Uncertainty 109
7.4 Sensitivity of storm velocity to timing error 109
7.5 Sensitivity of storm velocity to timing resolution 111
7.6 Conclusions 112
References 118
Bibliography 118

8. Decision Support Systems 119
8.1 Introduction 119
8.2 DSSs in water quality modeling 122
8.3 SWMM shells 124
8.4 PCSWMM (this section written 1993) 127
8.5 Concluding remarks 134
References 135
Bibliography 136

9. Objective Functions 139
9.1 Introduction 139
9.2 Another question of terminology 140
9.3 Response functions and statistical objective functions in SWMM 141
9.4 Multi-objective functions 143
9.5 Concluding remarks 144
References 145
Bibliography 145

10. Uncertainty Analysis 147
10.1 Introduction 147
10.2 Previous work on model uncertainty 150
10.3 Sources of error 152
10.4 Discretization and observation error 155
10.5 Uncertainty due to estimates of the limiting values of parameters 157
10.6 How to carry out an uncertainty analysis 158
10.7 Uncertainty analysis for a water distribution model 160
10.8 Model reliability 163
10.9 Concluding remarks 164
References 165
Bibliography 168

11. Sensitivity Analysis 171
11.1 Introduction 171
11.2 Sensitivity analysis and hydrological models 175
11.3 Sensitivity gradients 180
References 181

12. State Variable Space 183
12.1 Introduction 183
12.2 State variable sub-spaces 184
References 192

13. Performance Evaluation Functions 193
13.1 Introduction 193
13.2 Survey of evaluation functions 194
13.3 Concluding remarks 202
References 203

14. Parameter Optimization and Process Calibration 205
14.1 Introduction 205
14.2 Model Validation 208
14.3 When is a model “sufficiently calibrated”? 208
14.4 Sensitivity-based calibration 209
14.5 Robustness and efficiency 210
14.6 Limiting values of parameters 211
14.7 Automatic calibration 213
14.8 Genetic algorithm 216
14.9 Application of the genetic algorithm to calibration 219
References 236
Bibliography 237

15. On Using a Little Fuzzy Logic 241
15.1 Introduction 241
15.2 Fuzzification 243
15.3 Defuzzification. 260
15.4 Conclusion. 261
References 262
Bibliography 262

16. Presenting Continuous Uncertainty and Model Reliability in Real
Time 263
16.1 Introduction 263
16.2 Parameter uncertainty 265
16.3 Sensitivity analysis 267
16.4 Error analysis 268
References 271
Bibliography 272

17. Conclusions And Recommendations 273
17.1 Framework for continuous modeling: 273
17.2 Recommendations: 274

The Last Word 278

References 279
Glossary 293
SI-US Units Conversion Table 299
Programs and Models 301
 

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