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More About This Title Entropy Theory and its Application inEnvironmental and Water Engineering
English
Entropy Theory and its Application in Environmental and Water Engineering responds to the need for a book that deals with basic concepts of entropy theory from a hydrologic and water engineering perspective and then for a book that deals with applications of these concepts to a range of water engineering problems. The range of applications of entropy is constantly expanding and new areas finding a use for the theory are continually emerging. The applications of concepts and techniques vary across different subject areas and this book aims to relate them directly to practical problems of environmental and water engineering.
The book presents and explains the Principle of Maximum Entropy (POME) and the Principle of Minimum Cross Entropy (POMCE) and their applications to different types of probability distributions. Spatial and inverse spatial entropy are important for urban planning and are presented with clarity. Maximum entropy spectral analysis and minimum cross entropy spectral analysis are powerful techniques for addressing a variety of problems faced by environmental and water scientists and engineers and are described here with illustrative examples.
Giving a thorough introduction to the use of entropy to measure the unpredictability in environmental and water systems this book will add an essential statistical method to the toolkit of postgraduates, researchers and academic hydrologists, water resource managers, environmental scientists and engineers. It will also offer a valuable resource for professionals in the same areas, governmental organizations, private companies as well as students in earth sciences, civil and agricultural engineering, and agricultural and rangeland sciences.
This book:
- Provides a thorough introduction to entropy for beginners and more experienced users
- Uses numerous examples to illustrate the applications of the theoretical principles
- Allows the reader to apply entropy theory to the solution of practical problems
- Assumes minimal existing mathematical knowledge
- Discusses the theory and its various aspects in both univariate and bivariate cases
- Covers newly expanding areas including neural networks from an entropy perspective and future developments.
English
English
Acknowledgments, xix
1 Introduction, 1
1.1 Systems and their characteristics, 1
1.1.1 Classes of systems, 1
1.1.2 System states, 1
1.1.3 Change of state, 2
1.1.4 Thermodynamic entropy, 3
1.1.5 Evolutive connotation of entropy, 5
1.1.6 Statistical mechanical entropy, 5
1.2 Informational entropies, 7
1.2.1 Types of entropies, 8
1.2.2 Shannon entropy, 9
1.2.3 Information gain function, 12
1.2.4 Boltzmann, Gibbs and Shannon entropies, 14
1.2.5 Negentropy, 15
1.2.6 Exponential entropy, 16
1.2.7 Tsallis entropy, 18
1.2.8 Renyi entropy, 19
1.3 Entropy, information, and uncertainty, 21
1.3.1 Information, 22
1.3.2 Uncertainty and surprise, 24
1.4 Types of uncertainty, 25
1.5 Entropy and related concepts, 27
1.5.1 Information content of data, 27
1.5.2 Criteria for model selection, 28
1.5.3 Hypothesis testing, 29
1.5.4 Risk assessment, 29
Questions, 29
References, 31
Additional References, 32
2 Entropy Theory, 33
2.1 Formulation of entropy, 33
2.2 Shannon entropy, 39
2.3 Connotations of information and entropy, 42
2.3.1 Amount of information, 42
2.3.2 Measure of information, 43
2.3.3 Source of information, 43
2.3.4 Removal of uncertainty, 44
2.3.5 Equivocation, 45
2.3.6 Average amount of information, 45
2.3.7 Measurement system, 46
2.3.8 Information and organization, 46
2.4 Discrete entropy: univariate case and marginal entropy, 46
2.5 Discrete entropy: bivariate case, 52
2.5.1 Joint entropy, 53
2.5.2 Conditional entropy, 53
2.5.3 Transinformation, 57
2.6 Dimensionless entropies, 79
2.7 Bayes theorem, 80
2.8 Informational correlation coefficient, 88
2.9 Coefficient of nontransferred information, 90
2.10 Discrete entropy: multidimensional case, 92
2.11 Continuous entropy, 93
2.11.1 Univariate case, 94
2.11.2 Differential entropy of continuous variables, 97
2.11.3 Variable transformation and entropy, 99
2.11.4 Bivariate case, 100
2.11.5 Multivariate case, 105
2.12 Stochastic processes and entropy, 105
2.13 Effect of proportional class interval, 107
2.14 Effect of the form of probability distribution, 110
2.15 Data with zero values, 111
2.16 Effect of measurement units, 113
2.17 Effect of averaging data, 115
2.18 Effect of measurement error, 116
2.19 Entropy in frequency domain, 118
2.20 Principle of maximum entropy, 118
2.21 Concentration theorem, 119
2.22 Principle of minimum cross entropy, 122
2.23 Relation between entropy and error probability, 123
2.24 Various interpretations of entropy, 125
2.24.1 Measure of randomness or disorder, 125
2.24.2 Measure of unbiasedness or objectivity, 125
2.24.3 Measure of equality, 125
2.24.4 Measure of diversity, 126
2.24.5 Measure of lack of concentration, 126
2.24.6 Measure of flexibility, 126
2.24.7 Measure of complexity, 126
2.24.8 Measure of departure from uniform distribution, 127
2.24.9 Measure of interdependence, 127
2.24.10 Measure of dependence, 128
2.24.11 Measure of interactivity, 128
2.24.12 Measure of similarity, 129
2.24.13 Measure of redundancy, 129
2.24.14 Measure of organization, 130
2.25 Relation between entropy and variance, 133
2.26 Entropy power, 135
2.27 Relative frequency, 135
2.28 Application of entropy theory, 136
Questions, 136
References, 137
Additional Reading, 139
3 Principle of Maximum Entropy, 142
3.1 Formulation, 142
3.2 POME formalism for discrete variables, 145
3.3 POME formalism for continuous variables, 152
3.3.1 Entropy maximization using the method of Lagrange multipliers, 152
3.3.2 Direct method for entropy maximization, 157
3.4 POME formalism for two variables, 158
3.5 Effect of constraints on entropy, 165
3.6 Invariance of total entropy, 167
Questions, 168
References, 170
Additional Reading, 170
4 Derivation of Pome-Based Distributions, 172
4.1 Discrete variable and discrete distributions, 172
4.1.1 Constraint E[x] and the Maxwell-Boltzmann distribution, 172
4.1.2 Two constraints and Bose-Einstein distribution, 174
4.1.3 Two constraints and Fermi-Dirac distribution, 177
4.1.4 Intermediate statistics distribution, 178
4.1.5 Constraint: E[N]: Bernoulli distribution for a single trial, 179
4.1.6 Binomial distribution for repeated trials, 180
4.1.7 Geometric distribution: repeated trials, 181
4.1.8 Negative binomial distribution: repeated trials, 183
4.1.9 Constraint: E[N] = n: Poisson distribution, 183
4.2 Continuous variable and continuous distributions, 185
4.2.1 Finite interval [a, b], no constraint, and rectangular distribution, 185
4.2.2 Finite interval [a, b], one constraint and truncated exponential distribution, 186
4.2.3 Finite interval [0, 1], two constraints E[ln x] and E[ln(1 − x)] and beta distribution of first kind, 188
4.2.4 Semi-infinite interval (0,∞), one constraint E[x] and exponential distribution, 191
4.2.5 Semi-infinite interval, two constraints E[x] and E[ln x] and gamma distribution, 192
4.2.6 Semi-infinite interval, two constraints E[ln x] and E[ln(1 + x)] and beta distribution of second kind, 194
4.2.7 Infinite interval, two constraints E[x] and E[x2] and normal distribution, 195
4.2.8 Semi-infinite interval, log-transformation Y = lnX, two constraints E[y] and E[y2] and log-normal distribution, 197
4.2.9 Infinite and semi-infinite intervals: constraints and distributions, 199
Questions, 203
References, 208
Additional Reading, 208
5 Multivariate Probability Distributions, 213
5.1 Multivariate normal distributions, 213
5.1.1 One time lag serial dependence, 213
5.1.2 Two-lag serial dependence, 221
5.1.3 Multi-lag serial dependence, 229
5.1.4 No serial dependence: bivariate case, 234
5.1.5 Cross-correlation and serial dependence: bivariate case, 238
5.1.6 Multivariate case: no serial dependence, 244
5.1.7 Multi-lag serial dependence, 245
5.2 Multivariate exponential distributions, 245
5.2.1 Bivariate exponential distribution, 245
5.2.2 Trivariate exponential distribution, 254
5.2.3 Extension to Weibull distribution, 257
5.3 Multivariate distributions using the entropy-copula method, 258
5.3.1 Families of copula, 259
5.3.2 Application, 260
5.4 Copula entropy, 265
Questions, 266
References, 267
Additional Reading, 268
6 Principle of Minimum Cross-Entropy, 270
6.1 Concept and formulation of POMCE, 270
6.2 Properties of POMCE, 271
6.3 POMCE formalism for discrete variables, 275
6.4 POMCE formulation for continuous variables, 279
6.5 Relation to POME, 280
6.6 Relation to mutual information, 281
6.7 Relation to variational distance, 281
6.8 Lin’s directed divergence measure, 282
6.9 Upper bounds for cross-entropy, 286
Questions, 287
References, 288
Additional Reading, 289
7 Derivation of POME-Based Distributions, 290
7.1 Discrete variable and mean E[x] as a constraint, 290
7.1.1 Uniform prior distribution, 291
7.1.2 Arithmetic prior distribution, 293
7.1.3 Geometric prior distribution, 294
7.1.4 Binomial prior distribution, 295
7.1.5 General prior distribution, 297
7.2 Discrete variable taking on an infinite set of values, 298
7.2.1 Improper prior probability distribution, 298
7.2.2 A priori Poisson probability distribution, 301
7.2.3 A priori negative binomial distribution, 304
7.3 Continuous variable: general formulation, 305
7.3.1 Uniform prior and mean constraint, 307
7.3.2 Exponential prior and mean and mean log constraints, 308
Questions, 308
References, 309
8 Parameter Estimation, 310
8.1 Ordinary entropy-based parameter estimation method, 310
8.1.1 Specification of constraints, 311
8.1.2 Derivation of entropy-based distribution, 311
8.1.3 Construction of zeroth Lagrange multiplier, 311
8.1.4 Determination of Lagrange multipliers, 312
8.1.5 Determination of distribution parameters, 313
8.2 Parameter-space expansion method, 325
8.3 Contrast with method of maximum likelihood estimation (MLE), 329
8.4 Parameter estimation by numerical methods, 331
Questions, 332
References, 333
Additional Reading, 334
9 Spatial Entropy, 335
9.1 Organization of spatial data, 336
9.1.1 Distribution, density, and aggregation, 337
9.2 Spatial entropy statistics, 339
9.2.1 Redundancy, 343
9.2.2 Information gain, 345
9.2.3 Disutility entropy, 352
9.3 One dimensional aggregation, 353
9.4 Another approach to spatial representation, 360
9.5 Two-dimensional aggregation, 363
9.5.1 Probability density function and its resolution, 372
9.5.2 Relation between spatial entropy and spatial disutility, 375
9.6 Entropy maximization for modeling spatial phenomena, 376
9.7 Cluster analysis by entropy maximization, 380
9.8 Spatial visualization and mapping, 384
9.9 Scale and entropy, 386
9.10 Spatial probability distributions, 388
9.11 Scaling: rank size rule and Zipf’s law, 391
9.11.1 Exponential law, 391
9.11.2 Log-normal law, 391
9.11.3 Power law, 392
9.11.4 Law of proportionate effect, 392
Questions, 393
References, 394
Further Reading, 395
10 Inverse Spatial Entropy, 398
10.1 Definition, 398
10.2 Principle of entropy decomposition, 402
10.3 Measures of information gain, 405
10.3.1 Bivariate measures, 405
10.3.2 Map representation, 410
10.3.3 Construction of spatial measures, 412
10.4 Aggregation properties, 417
10.5 Spatial interpretations, 420
10.6 Hierarchical decomposition, 426
10.7 Comparative measures of spatial decomposition, 428
Questions, 433
References, 435
11 Entropy Spectral Analyses, 436
11.1 Characteristics of time series, 436
11.1.1 Mean, 437
11.1.2 Variance, 438
11.1.3 Covariance, 440
11.1.4 Correlation, 441
11.1.5 Stationarity, 443
11.2 Spectral analysis, 446
11.2.1 Fourier representation, 448
11.2.2 Fourier transform, 453
11.2.3 Periodogram, 454
11.2.4 Power, 457
11.2.5 Power spectrum, 461
11.3 Spectral analysis using maximum entropy, 464
11.3.1 Burg method, 465
11.3.2 Kapur-Kesavan method, 473
11.3.3 Maximization of entropy, 473
11.3.4 Determination of Lagrange multipliers λk, 476
11.3.5 Spectral density, 479
11.3.6 Extrapolation of autocovariance functions, 482
11.3.7 Entropy of power spectrum, 482
11.4 Spectral estimation using configurational entropy, 483
11.5 Spectral estimation by mutual information principle, 486
References, 490
Additional Reading, 490
12 Minimum Cross Entropy Spectral Analysis, 492
12.1 Cross-entropy, 492
12.2 Minimum cross-entropy spectral analysis (MCESA), 493
12.2.1 Power spectrum probability density function, 493
12.2.2 Minimum cross-entropy-based probability density functions given total expected spectral powers at each frequency, 498
12.2.3 Spectral probability density functions for white noise, 501
12.3 Minimum cross-entropy power spectrum given auto-correlation, 503
12.3.1 No prior power spectrum estimate is given, 504
12.3.2 A prior power spectrum estimate is given, 505
12.3.3 Given spectral powers: Tk = Gj, Gj = Pk, 506
12.4 Cross-entropy between input and output of linear filter, 509
12.4.1 Given input signal PDF, 509
12.4.2 Given prior power spectrum, 510
12.5 Comparison, 512
12.6 Towards efficient algorithms, 514
12.7 General method for minimum cross-entropy spectral estimation, 515
References, 515
Additional References, 516
13 Evaluation and Design of Sampling and Measurement Networks, 517
13.1 Design considerations, 517
13.2 Information-related approaches, 518
13.2.1 Information variance, 518
13.2.2 Transfer function variance, 520
13.2.3 Correlation, 521
13.3 Entropy measures, 521
13.3.1 Marginal entropy, joint entropy, conditional entropy and transinformation, 521
13.3.2 Informational correlation coefficient, 523
13.3.3 Isoinformation, 524
13.3.4 Information transfer function, 524
13.3.5 Information distance, 525
13.3.6 Information area, 525
13.3.7 Application to rainfall networks, 525
13.4 Directional information transfer index, 530
13.4.1 Kernel estimation, 531
13.4.2 Application to groundwater quality networks, 533
13.5 Total correlation, 537
13.6 Maximum information minimum redundancy (MIMR), 539
13.6.1 Optimization, 541
13.6.2 Selection procedure, 542
Questions, 553
References, 554
Additional Reading, 556
14 Selection of Variables and Models, 559
14.1 Methods for selection, 559
14.2 Kullback-Leibler (KL) distance, 560
14.3 Variable selection, 560
14.4 Transitivity, 561
14.5 Logit model, 561
14.6 Risk and vulnerability assessment, 574
14.6.1 Hazard assessment, 576
14.6.2 Vulnerability assessment, 577
14.6.3 Risk assessment and ranking, 578
Questions, 578
References, 579
Additional Reading, 580
15 Neural Networks, 581
15.1 Single neuron, 581
15.2 Neural network training, 585
15.3 Principle of maximum information preservation, 588
15.4 A single neuron corrupted by processing noise, 589
15.5 A single neuron corrupted by additive input noise, 592
15.6 Redundancy and diversity, 596
15.7 Decision trees and entropy nets, 598
Questions, 602
References, 603
16 System Complexity, 605
16.1 Ferdinand’s measure of complexity, 605
16.1.1 Specification of constraints, 606
16.1.2 Maximization of entropy, 606
16.1.3 Determination of Lagrange multipliers, 606
16.1.4 Partition function, 607
16.1.5 Analysis of complexity, 610
16.1.6 Maximum entropy, 614
16.1.7 Complexity as a function of N, 616
16.2 Kapur’s complexity analysis, 618
16.3 Cornacchio’s generalized complexity measures, 620
16.3.1 Special case: R = 1, 624
16.3.2 Analysis of complexity: non-unique K-transition points and conditional complexity, 624
16.4 Kapur’s simplification, 627
16.5 Kapur’s measure, 627
16.6 Hypothesis testing, 628
16.7 Other complexity measures, 628
Questions, 631
References, 631
Additional References, 632
Author Index, 633
Subject Index, 639
