Time series modelling of water resources and environmental systems:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam u.a.
Elsevier
1994
|
| Schriftenreihe: | Developments in water science
45 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXVII, 1013 S. graph. Darst. |
| ISBN: | 0444892702 |
Internformat
MARC
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| 100 | 1 | |a Hipel, Keith W. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Time series modelling of water resources and environmental systems |c Keith W. Hipel ; A. Ian McLeod |
| 264 | 1 | |a Amsterdam u.a. |b Elsevier |c 1994 | |
| 300 | |a XXXVII, 1013 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Developments in water science |v 45 | |
| 650 | 7 | |a Environnement - Modèles mathématiques |2 ram | |
| 650 | 7 | |a Environnement - Statistiques |2 ram | |
| 650 | 4 | |a Hydrologie - Modèles mathématiques | |
| 650 | 7 | |a Ressources en eau - Modèles mathématiques |2 ram | |
| 650 | 7 | |a Ressources en eau - Statistiques |2 ram | |
| 650 | 4 | |a Sciences de l'environnement - Modèles mathématiques | |
| 650 | 4 | |a Série chronologique | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Environmental sciences |x Mathematical models | |
| 650 | 4 | |a Hydrology |x Mathematical models | |
| 650 | 4 | |a Time-series analysis | |
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Datensatz im Suchindex
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| adam_text | TIME SERIES MODELLING OF WATER RESOURCES AND ENVIRONMENTAL SYSTEMS KEITH
W. HIPEL DEPARTMENTS OF SYSTEMS DESIGN ENGINEERING AND STATISTICS AND
ACTUARIAL SCIENCE UNIVERSITY OF WATERLOO, WATERLOO, ONTARIO, CANADA, N2L
3G1 A. IAN MCLEOD DEPARTMENT OF STATISTICAL AND ACTUARIAL SCIENCES, THE
UNIVERSITY OF WESTERN ONTARIO LONDON^ ONTARIO, CANADA, N6A 5B7 AND
DEPARTMENT OF SYSTEMS DESIGN ENGINEERING, UNIVERSITY OF WATERLOO
BLBLLOTHSB INSTITUT FOR WASSERBAU UNO WASSERWIPJSCHAFT TECHN1SSHE
UNJVERSI7RT DARMSTADT PETERSENSTR. 13, 64287 DARMSTADT TEL. 0 61 51 /16
21 43 * FAX: 16 32 43 ELSEVIER AMSTERDAM * LAUSANNE * NEW YORK * OXFORD
* SHANNON * TOKYO 1994 -VU- TABLE OF CONTENTS PARTI: SCOPE AND
BACKGROUND MATERIAL CHAPTER 1: ENVIRONMETRICS, SCIENCE AND DECISION
MAKING 3 1.1 THE NEW FIELD OF ENVIRONMETRICS 3 1.2 THE SCIENTIFIC METHOD
6 1.2.1 SPACESHIP EARTH 6 1.2.2 DESCRIPTION OF THE SCIENTIFIC METHOD 9
1.2.3 STATISTICS IN A SCIENTIFIC INVESTIGATION. 13 1.2.4 DATA ANALYSIS
14 1.3 PHILOSOPHY OF MODEL BUILDING 16 1.3.1 OCCAM S RAZOR 16 1.3.2
MODEL CONSTRUCTION 17 1.3.3 AUTOMATIC SELECTION CRITERIA 17 1.4 THE
HYDROLOGICAL CYCLE 19 1.4.1 ENVIRONMENTAL SYSTEMS 19 1.4.2 DESCRIPTION
OF THE HYDROLOGICAL CYCLE 20 1.4.3 CLASSIFYING MATHEMATICAL MODELS 22
1.5 DECISION MAKING 24 1.5.1 ENGINEERING DECISION MAKING 24 1.5.2
DECISION MAKING TECHNIQUES IN OPERATIONAL RESEARCH 27 1.5.3 CONFLICT
ANALYSIS OF THE GARRISON DIVERSION UNIT DISPUTE 30 1.6 ORGANIZATION OF
THE BOOK 35 1.6.1 THE AUDIENCE 35 1.6.2 A TRAVELLER S GUIDE 36 1.6.3
COMPARISONS TO OTHER AVAILABLE LITERATURE 49 1.7 DECISION SUPPORT SYSTEM
FOR TIME SERIES MODELLING 51 1.8 CONCLUDING REMARKS 52 PROBLEMS 54
REFERENCES 55 CHAPTER 2: BASIC STATISTICAL CONCEPTS 63 2.1 INTRODUCTION
_ ; 63 2.2 TIME SERIES 63 2.3 STOCHASTIC PROCESS 65 2.4 STATIONARITY 67
2.4.1 GENERAL DISCUSSION. 67 2.4.2 TYPES OF STATIONARITY 69 2.5
STATISTICAL DEFINITIONS 69 2.5.1 MEAN AND VARIANCE 69 -VLLL- 2.5.2
AUTOCOVARIANCE AND AUTOCORRELATION 70 AUTOCOVARIANCE AND AUTOCORRELATION
MATRICES 70 2.5.3 SHORT AND LONG MEMORY PROCESSES 71 2.5.4 THE SAMPLE
AUTOCOVARIANCE AND AUTOCORRELATION FUNCTIONS 72 2.5.5 ERGODICITY
CONDITIONS 76 2.6 SPECTRAL ANALYSIS 77 2.7 LINEAR STOCHASTIC MODELS 79
2.8 CONCLUSIONS 83 PROBLEMS 83 REFERENCES 84 PART II: LINEAR NONSEASONAL
MODELS 87 CHAPTER 3: STATIONARY NONSEASONAL MODELS 91 3.1 INTRODUCTION
91 3.2 AUTOREGRESSIVE PROCESSES 92 3.2.1 MARKOV PROCESS 92 3.2.2
AUTOREGRESSIVE PROCESS OF ORDER/? 93 STATIONARITY 94 AUTOCORRELATION
FUNCTION 95 YULE-WALKER EQUATIONS 96 PARTIAL AUTOCORRELATION FUNCTION 98
3.3 MOVING AVERAGE PROCESSES 102 3.3.1 FIRST ORDERMOVING AVERAGE PROCESS
102 3.3.2 MOVING AVERAGE PROCESS OF ORDER Q 103 STATIONARITY 103
INVERTIBILITY 104 AUTOCORRELATION FUNCTION 104 PARTIAL AUTOCORRELATION
FUNCTION 105 THE FIRST ORDER MOVING AVERAGE PROCESS 107 3.4
AUTOREGRESSIVE * MOVING AVERAGE PROCESSES ; 107 3.4.1 FIRST ORDER
AUTOREGRESSIVE - FIRST ORDER MOVING AVERAGE PROCESS 107 3.4.2 GENERAL
AUTOREGRESSIVE - MOVING AVERAGE PROCESS 108 STATIONARITY AND
INVERTIBILITY 109 AUTOCORRELATION FUNCTION 109 PARTIAL AUTOCORRELATION
FUNCTION. 110 ARMA(L.L) PROCESS 111 3.4.3 THREE FORMULATIONS OF THE
AUTOREGRESSIVE - MOVING AVERAGE PROCESS 114 RANDOM SHOCK FORM 114
INVERTED FORM 118 LINEAR FILTER INTERPRETATION. 120 LINEAR DIFFERENCE
EQUATIONS 121 -IX- 3.4.4 CONSTRAINED MODELS * 121 3.4.5 BOX-COX
TRANSFORMATION 122 3.5 THEORETICAL SPECTRUM *. 123 3J.I
DEFINITIONS...... . 123 3.5.2 PLOTS OF THE LOG NORMALIZED
SPECTRUM........................... 126 3.6 PHYSICAL JUSTIFICATION OF
ARMA MODELS 132 3.6.1 ENVIRONMENTAL SYSTEMS MODEL OF A WATERSHED 132
3.6.2 INDEPENDENT PRECIPITATION......................................
134 3.6.3 AR(1) PRECIPITATION 135 3.6.4 ARMA(L.L) PRECIPITATION * 135
3.7 CONCLUSIONS 136 APPENDK A3.1 - ALGORITHM FOR ESTIMATING THE PARTIAL
AUTOCORRELATION FUNCTION 137 APPENDK A3.2 - THEORETICAL ACF FOR AN ARMA
PROCESS 139 PROBLEMS 140 REFERENCES _ 142 CHAPTER 4: NONSTATIONARY
NONSEASONAL MODELS 145 4.1 INTRODUCTION 145 4.2 EXPLOSIVE
NONSTATIONARITY 145 4.3 HOMOGENEOUS NONSTATIONARITY 146 4.3.1
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL 146 4.3.2 AUTOCORRELATION
FUNCTION 150 4.3.3 EXAMPLES OF NONSTATIONARY TIME SERIES 154 ANNUAL
WATER USE FOR NEW YORK CITY 154 ELECTRICITY CONSUMPTION 154 BEVERIDGE
WHEAT PRICE INDEX .; 156 4.3.4 THREE FORMULATIONS OF THE ARIMA PROCESS
156 4.4 INTEGRATED MOVING AVERAGE PROCESSES 163 4.5 DIFFERENCING
ANALOGIES.... 165 4.6 DETERMINISTIC AND STOCHASTIC TRENDS 167 4.7
CONCLUSIONS 168 PROBLEMS 168 REFERENCES _ 169 PART HI: MODEL
CONSTRUCTION 171 CHAPTERS: MODEL IDENTIFICATION 173 5.1 INTRODUCTION 173
5.2 MODELLING PHILOSOPHIES 173 5.2.1 OVERVIEW 173 5.2.2 HYDROLOGICAL
UNCERTAINTIES :... 174 - X - 5.2.3 MODEL DISCRIMINATION 174 5.2.4
MODELLING PRINCIPLES 175 5.2.5 MODEL BUILDING 175 5.3 IDENTIFICATION
METHODS 175 5.3.1 INTRODUCTION 175 5.3.2 BACKGROUND INFORMATION 176
5.3.3 PLOT OF THE DATA * 178 5.3.4 SAMPLE AUTOCORRELATION FUNCTION. 181
5.3.5 SAMPLE PARTIAL AUTOCORRELATION FUNCTION . . 181 5.3.6 SAMPLE
INVERSE AUTOCORRELATION FUNCTION 182 5.3.7 SAMPLE INVERSE PARTIAL
AUTOCORRELATION FUNCTION 184 5.4 APPLICATIONS 185 5.4.1
INTRODUCTION..... 185 5.4.2 YEARLY ST. LAWRENCE RIVERFLOWS 186 5.4.3
ANNUAL SUNSPOT NUMBERS 191 5.5 OTHER IDENTIFICATION METHODS 194 5.5.1
INTRODUCTION 194 5.5.2 R AND S ARRAYS 195 5.5.3 THE COMER METHOD 195
5.5.4 EXTENDED SAMPLE AUTOCORRELATION FUNCTION 195 5.6 CONCLUSIONS 195
PROBLEMS 196 REFERENCES 197 CHAPTER 6: PARAMETER ESTIMATION 203 6.1
INTRODUCTION 203 6.2 MAXIMUM LIKELIHOOD ESTIMATION 204 6.2.1
INTRODUCTION 204 6.2.2 PROPERTIES OF MAXIMUM LIKELIHOOD ESTIMATORS 205
LIKELIHOOD PRINCIPLE 205 CONSISTENCY 206 EFFICIENCY 207 6.2.3 MAXIMUM
LIKELIHOOD ESTIMATORS 208 6.3 MODEL DISCRIMINATION USING THE AKADCE
INFORMATION CRITERION 210 6.3.1 INTRODUCTION 210 6.3.2 DEFINITION OF THE
AKAIKE INFORMATION CRITERION 210 6.3.3 THE AKAIKE INFORMATION CRITERION
IN MODEL CONSTRUCTION 211 6.3.4 PLAUSIBILITY 213 6.3.5 THE AKAIKE
INFORMATION CRITERION FOR ARMA AND ARIMA MODELS 213 6.3.6 OTHER
AUTOMATIC SELECTION CRITERIA 214 6.4 APPLICATIONS 216 6.4.1 INTRODUCTION
216 6.4.2 YEARLY ST LAWRENCE RIVERFLOWS 216 6.4.3 ANNUAL SUNSPOT NUMBERS
218 -XI- 6.5 CONCLUSIONS 221 APPENDIX A6.1 - ESTIMATOR FOR ARMA MODELS
221 APPENDIX A6.2 - INFORMATION MATRDC 225 APPENDIX A6.3 - FINAL
PREDICTION ERROR 227 PROBLEMS * 228 REFERENCES . 229 CHAPTER 7:
DIAGNOSTIC CHECKING 235 7.1 INTRODUCTION * 235 7.2 OVERFITTING 236 7.3
WHITENESS TESTS _ 238 7.3.1 INTRODUCTION 238 7.3.2 GRAPH OF THE RESIDUAL
AUTOCORRELATION FUNCTION 238 7.3.3 PORTMANTEAU TESTS 240 7.3.4 OTHER
WHITENESS TESTS 241 7.4 NORMALITY TESTS 241 7.4.1 INTRODUCTION 241 7.4.2
SKEWNESS AND KURTOSIS COEFFICIENTS 242 7.4.3 NORMAL PROBABILITY PLOT 243
7.4.4 OTHER NORMALITY TESTS 244 SHAPIRO-WILKTEST 244 BLOM S CORRELATION
COEFFICIENT 244 7.5 CONSTANT VARIANCE TESTS 245 7.5.1 INTRODUCTION 245
7.5.2 TESTS FOR HOMOSCEDASTICITY 245 7.6 APPLICATIONS 246 7.6.1
INTRODUCTION 246 7.6.2 YEARLY ST LAWRENCE RIVERFLOWS 247 7.6.3 ANNUAL
SUNSPOT NUMBERS 248 7.7 CONCLUSIONS 249 PROBLEMS 250 REFERENCES 252 PART
IV: FORECASTING AND SIMULATION 255 CHAPTER 8: FORECASTING WITH
NONSEASONAL MODELS 257 8.1 INTRODUCTION * 257 8.2 MINIMUM MEAN SQUARE
ERROR FORECASTS 259 8.2.1 INTRODUCTION 259 8.2.2 DEFINITION * I 261
8.2.3 PROPERTIES 263 8.2.4 CALCULATION OF FORECASTS 264 -XII-
FORECASTING WITH ARMA MODELS 264 FORECASTING WITH AN ARIMA MODEL 265
RULES FOR FORECASTING - 266 8.2.5 EXAMPLES 267 ARMA FORECASTING
ILLUSTRATION 267 ARMA FORECASTING APPLICATION 268 8.2.6 UPDATING
FORECASTS 270 8.2.7 INVERSE BOX-COX TRANSFORMATIONS 270 8.2.8
AMLICATIONS ..... ....... 272 PROBABILITY LIMITS 272 ARMA(L.L) FORECASTS
..... * 272 ARIMA(0,2,L) FORECASTS - 272 8.3 FORECASTING EXPERIMENTS 273
8.3.1 OVERVIEW 273 8.3.2 TESTS FOR COMPARING FORECAST ERRORS 275
INTRODUCTION 275 WILCOXON SIGNED RANK TEST 275 THE LIKELIHOOD RATIO AND
CORRELATION TESTS 276 8.3.3 FORECASTING MODELS 277 INTRODUCTION 277
MARKOV AND NONPARAMETRIC REGRESSION MODELS 278 8.3.4 FORECASTING STUDY
280 INTRODUCTION 280 FIRST FORECASTING EXPERIMENT 280 SECOND FORECASTING
EXPERIMENT 282 DISCUSSION ; 284 8.4 CONCLUSIONS 284 PROBLEMS .* 287
REFERENCES 288 CHAPTER 9: SIMULATING WITH NONSEASONAL MODELS 293 9.1
INTRODUCTION 293 9.2 GENERATING WHITE NOISE 295 9.2.1 INTRODUCTION 295
9.2.2 RANDOM NUMBER GENERATORS 296 OVERVIEW 296 LINEAR CONGNIENTIAL
RANDOM NUMBER GENERATORS 299 9.2.3 GENERATION OF INDEPENDENT RANDOM
VARIABLES 301 GENERAL APPROACH 301 SIMULATING INDEPENDENT NORMAL
SEQUENCES 302 GENERATING OTHER DISTRIBUTIONS 303 9.3 WATERLOO SIMULATION
PROCEDURE 1 304 9.4 WATERLOO SIMULATION PROCEDURE 2 306 9.4.1 WASIM2
ALGORITHM 306 -XIII- 9.4.2 THEORETICAL BASIS OF WASIM2 307 9.4.3
ARMA(L.L) SIMULATION EXAMPLE 307 9.5 SIMULATION OF INTEGRATED MODELS 310
9.5.1 INTRODUCTION 310 9.5.2 ALGORITHMS FOR NONSEASONAL AND SEASONAL
ARIMA MODELS 311 9.6 INVERSE BOX-COX TRANSFORMATION 312 9.7 WATERLOO
SIMULATION PROCEDURE 3 313 9.7.1 INTRODUCTION *...* * 313 9.7.2 WASIM3
ALGORITHM 313 9.7.3 PARAMETER UNCERTAINTY IN RESERVOIR DESIGN..__ 314
9.7.4 MODEL UNCERTAINTY...» 316 9.8 APPLICATIONS 316 9.8.1 INTRODUCTION
316 9.8.2 AVOIDANCE OF BIAS IN SIMULATION STUDIES 316 9.8.3 SIMULATION
STUDIES USING THE HISTORICAL DISTURBANCES 317 9.8.4 PARAMETER
UNCERTAINTY IN SIMULATION EXPERIMENTS 319 9.9 CONCLUSIONS 319 PROBLEMS
320 REFERENCES 321 PART V: LONG MEMORY MODELLING 325 CHAPTER 10: THE
HURST PHENOMENON AND FRACTIONAL GAUSSIAN NOISE * 327 10.1 INTRODUCTION
327 10.2 DEFINITIONS 328 10.3 HISTORICAL RESEARCH 331 10.3.1 THE HURST
PHENOMENON AND HURST COEFFICIENTS 331 10.3.2 THE HURST PHENOMENON AND
INDEPENDENT SUMMANDS 334 10.3.3 THE HURST PHENOMENON AND CORRELATED
SUMMANDS 336 INTRODUCTION 336 SHORT MEMORY MODELS 336 LONG MEMORY MODELS
- 338 10.4 FRACTIONAL GAUSSIAN NOISE 338 10.4.1 INTRODUCTION 338 10.4.2
DEFINITION OF FGN * 339 10.4.3 MAXIMUM LIKELIHOOD ESTIMATION 341 10.4.4
TESTING MODEL ADEQUACY 343 10.4.5 FORECASTING WITH FGN 344 10.4.6
SIMULATION OF FGN 345 10.4.7 APPLICATIONS TO ANNUAL RIVERFLOWS 346 10.5
SIMULATION STUDIES 352 103.1 INTRODUCTION 352 -XIV- 10^.2 SIMULATION OF
INDEPENDENT SUMMANDS 353 THE RESEATED ADJUSTED RANGE * 353 THE HURST
COEFFICIENT - 354 103.3 SIMULATION OF CORRELATED SUMMANDS 357 LONG
MEMORY MODELS 357 SHORT MEMORY MODELS 357 10.6 PRESERVATION OF THE
RESCALED ADJUSTED RANGE 362 10.6.1 INTRODUCTION 362 10.6.2 ARMA
MODELLING OF GEOPHYSICAL PHENOMENA 362 10.6.3 DISTRIBUTION OF THE RAR OR
K 363 10.6.4 PRESERVATION OF THE RAR AND K BY ARMA MODELS 367 10.7
ESTIMATES OF THE HURST COEFFICIENT 369 10.8 CONCLUSIONS * ~ 371 APPENDIX
A10.1 - REPRESENTATIVE EMPIRICAL CUMULATIVE DISTRIBUTION FUNCTIONS
(ECDF S) FOR HURST STATISTICS 374 PROBLEMS 381 REFERENCES 382 CHAPTER
11: FRACTIONAL AUTOREGRESSTVE-MOVING AVERAGE MODELS 389 11.1
INTRODUCTION 389 11.2 DEFINITIONS AND STATISTICAL PROPERTIES 390 11.2.1
LONG MEMORY 390 11.2.2 DEFINITION OF FARMA MODELS 391 11.2.3 STATISTICAL
PROPERTIES OF FARMA MODELS 394 11.3 CONSTRUCTING FARMA MODELS 397 11.3.1
OVERVIEW 397 11.3.2 IDENTIFICATION 397 11.3.3 ESTIMATION 397
BOOTSTRAPPING A TIME SERIES MODEL 399 11.3.4 DIAGNOSTIC CHECKS 400 11.4
SIMULATION AND FORECASTING 400 11.4.1 INTRODUCTION 400 11.4.2 SIMULATING
WITH FARMA MODELS 400 11.4.3 FORECASTING WITH FARMA MODELS 401 113
FITTING FARMA MODELS TO ANNUAL HYDROLOGICAL TIME SERIES 403 11.6
CONCLUSIONS 407 APPENDIX AL 1.1 - ESTIMATION ALGORITHM FOR FARMA MODELS
409 PROBLEMS 5. 411 REFERENCES 412 - X V - PARTVI: SEASONAL MODELS 415
CHAPTER 12: SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS ;
419 12.1 INTRODUCTION .... 419 12.2 MODEL DESIGN 420 12.2.1 DEFINITION
420 122.2 NOTATION 422 122.3 STATIONARITY AND INVERTIBILITY * 423 122.4
UNFACTORED AND NONMULTIPLICATIVE MODELS. 423 1223 AUTOCORRELATION
FUNCTION » 425 122.6 THREE FORMULATIONS OF THE SEASONAL PROCESSES 425
INTRODUCTION 425 RANDOM SHOCK FORM ;. . 425 INVERTED FORM 427 12.3 MODEL
CONSTRUCTION 427 12.3.1 INTRODUCTION 427 12.3.2 IDENTIFICATION 428
INTRODUCTION 428 TOOLS 428 SUMMARY 431 12.3.3 ESTIMATION 432
INTRODUCTION ._ 432 ALGORITHMS 433 MODEL DISCRIMINATION 434 12.3.4
DIAGNOSTIC CHECKS 434 INTRODUCTION 434 TESTS FOR WHITENESS 435 TEST FOR
PERIODIC CORRELATION 436 NORMALITY TESTS 437 HOMOSCEDASTICITY CHECKS 437
12.3.5 SUMMARY 437 12.4 APPLICATIONS 438 12.4.1 INTRODUCTION 438 12.4.2
AVERAGE MONTHLY WATER USEAGE 439 12.4.3 AVERAGE MONTHLY ATMOSPHERIC
CARBON DIOXIDE ; 445 12.4.4 AVERAGE MONTHLY SAUGEEN RIVERFLOWS 447 123
FORECASTING AND SIMULATION WITH S ARJMA MODELS 451 12.6 CONCLUSIONS 451
APPENDIX A12.1 DESIGNING MULTIPLICATIVE SARIMA MODELS USING THE ACF 453
APPENDIX A122 MAXIMUM LIKELIHOOD ESTIMATION FOR SARMA MODELS 455
PROBLEMS .* 459 -XVI- REFERENCES .......... 460 CHAPTER 13:
DESEASONALIZED MODELS 463 13.1 INTRODUCTION _ 463 132 DEFINITIONS OF
DESEASONALIZED MODELS 464 132.1 INTRODUCTION ..... ... . 464 13.2.2
DESEASONALIZATION....... 464 132.3 ARMA MODEL COMPONENT 466 13.3
CONSTRUCTING DESEASONALIZED MODELS 467 13.3.1 INTRODUCTION * .... 467
13.3.2 FULLY DESEASONALIZED MODELS 467 13.3.3 FOURIER APPROACH TO
DESEASONALIZED MODELS 469 OVERALL PROCEDURE 469 AIC FORMULAE FOR
DESEASONALIZED MODELS ; 469 13.4 APPLICATIONS OF DESEASONALIZED MODELS
473 13.4.1 INTRODUCTION 473 13.4.2 AVERAGE MONTHLY SAUGEEN RIVERFLOWS
473 13.4.3 OZONE DATA. 476 133 FORECASTING AND SIMULATING WITH
DESEASONALIZED MODELS 478 13.6 CONCLUSIONS 479 PROBLEMS 480 REFERENCES
481 CHAPTER 14: PERIODIC MODELS 483 14.1 INTRODUCTION 483 14.2
DEFINITIONS OF PERIODIC MODELS 484 14.2.1 INTRODUCTION 484 142.2 PAR
MODELS 484 DEFINITION 484 STATIONARITY 486 PERIODIC AUTOCORRELATION
FUNCTION 486 PERIODIC YULE-WALKER EQUATIONS 487 PERIODIC PARTIAL
AUTOCORRELATION FUNCTION 488 MARKOV MODEL 488 14.2.3 PARMA MODELS 489
DEFINITION 489 STATIONARITY AND INVERTIBILITY 489 PERIODIC
AUTOCORRELATION FUNCTION 489 PERIODIC PARTIAL AUTOCORRELATION FUNCTION
491 THREE FORMULATIONS OF A PARMA MODEL 491 EXAMPLE OF A PARMA MODEL 492
14.3 CONSTRUCTING PAR MODELS 493 14.3.1 INTRODUCTION 493 -XVII- 14.3.2
IDENTIFYING PAR MODELS, * 493 INTRODUCTION 493 SAMPLE PERIODIC AGF .. ._
493 SAMPLE PERIODIC PACF .. . 494 PERIODIC IACF AND PACF . ..... .* 495
TEST FOR PERIODIC CORRELATION 496 14.3.3 CALIBRATING PAR MODELS 496
INTRODUCTION . .... . . .... . 496 PERIODIC YULE-WALKER ESTIMATOR . 496
MULTIPLE LINEAR REGRESSION . . 496 OTHER ESTIMATION RESULTS . . ~ 497
MODEL SELECTION USING THE AIC 497 EXHAUSTIVE ENUMERATION FOR PAR MODEL
SELECTION *. 498 14.3.4 CHECKING PAR MODELS 499 14.4 PAR MODELLING
APPLICATION 501 143 PARSIMONIOUS PERIODIC AUTOREGRESSIVE (PPAR) MODELS
503 143.1 INTRODUCTION 503 143.2 DEFINITION OF PPAR MODELS 503 143.3
CONSTRUCTING PPAR MODELS 505 14.6 APPLICATIONS OF SEASONAL MODELS 507
14.7 CONSTRUCTING PARMA MODELS 510 14.8 SIMULATING AND FORECASTING WITH
PERIODIC MODELS 512 14.8.1 INTRODUCTION 512 14.8.2 PRESERVATION OF
CRITICAL PERIOD STATISTICS 513 INTRODUCTION 513 CRITICAL PERIODIC
STATISTICS FOR WATER SUPPLY 513 DESIGN OF SIMULATION EXPERIMENTS 514 THE
RESULTS OF THE SIMULATION EXPERIMENTS 515 14.9 CONCLUSIONS 517
PROBLEMS.... 518 REFERENCES 520 CHAPTER 15: FORECASTING WITH SEASONAL
MODELS 525 15.1 INTRODUCTION 525 152 CALCULATING FORECASTS FOR SEASONAL
MODELS 526 152.1 INTRODUCTION 526 152.2 FORECASTING WITH SARIMA MODELS
527 INVERSE BOX-COX TRANSFORMATION * 528 15.2.3 FORECASTING WITH
DESEASONALIZED MODELS 529 15.2.4 FORECASTING WITH PERIODIC MODELS 530
15.3 FORECASTING MONTHLY RIVERFLOW TIME SERIES 532 15.3.1 INTRODUCTION *
532 15.3.2 DATA SETS 533 15.3.3 SEASONAL MODELS 533 - XVIII - 15.3.4
FORECASTING STUDY -- 535 15.4 FORECASTING QUARTER MONTHLY AND MONTHLY
RIVERFLOWS 540 15.4.1 INTRODUCTION 540 15.4.2 TIME SERIES . . .- 541
15.4.3 SEASONAL MODELS _... ... . . 541 15.4.4 FORECASTING
EXPERIMENTS................
...................................................... *.* ... 541 153
COMBINING FORECASTS ACROSS MODELS 544 153.1 MOTIVATION . * 544 1532
FORMULAE FOR COMBINING FORECASTS................................... 544
153.3 COMBINING AVERAGE MONTHLY RIVERFLOW FORECASTS 545 15.6 AGGREGATION
OF FORECASTS . * 547 15.7 CONCLUSIONS 547 PROBLEMS * 547 REFERENCES 549
PART VH: MULTIPLE INPUT-SINGLE OUTPUT MODELS . 553 CHAPTER 16: CAUSALITY
555 16.1 INTRODUCTION 555 16.2 CAUSALITY 556 16.2.1 DEFINITION 556
16.2.2 RESIDUAL CROSS-CORRELATION FUNCTION 556 16.3 APPLICATIONS 561
16.3.1 DATA 561 16.32 PREWHITENING 561 16.3.3 CAUSALITY STUDIES 563 16.4
CONCLUSIONS 566 PROBLEMS 569 REFERENCES 570 CHAPTER 17: CONSTRUCTING
TRANSFER FUNCTION-NOISE MODELS 573 17.1 INTRODUCTION - 573 172 TRANSFER
FUNCTION-NOISE MODELS WITH A SINGLE INPUT 574 172.1 INTRODUCTION 574
172.2 DYNAMIC COMPONENT 575 17.2.3 NOISE TERM 579 172.4 TRANSFER
FUNCTION-NOISE MODEL..... 579 17.3 MODEL CONSTRUCTION FOR TRANSFER
FUNCTION-NOISE MODELS WITH ONE INPUT 580 17.3.1 MODEL IDENTIFICATION 580
EMPIRICAL IDENTIFICATION APPROACH 580 HAUGH AND BOX IDENTIFICATION
METHOD 581 -XIX- BOX AND JENKINS IDENTIFICATION PROCEDURE 583 COMPARISON
OF IDENTIFICATION METHODS 584 17.3.2 PARAMETER ESTIMATION 585 17.3.3
DIAGNOSTIC CHECKING * 586 17.4 HYDROLOGICAL APPLICATIONS OF TRANSFER
FUNCTION-NOISE MODELS WITH A SINGLE INPUT 588 17.4.1 INTRODUCTION 588
17.42 DYNAMIC MODEL FOR THE AVERAGE MONTHLY FLOWS OF THE RED DEER AND
SOUTH SASKATCHEWAN RIVERS.. 588 IDENTIFICATION 588 PARAMETER ESTIMATION
592 DIAGNOSTIC CHECKING 592 CONCLUDING REMARKS 592 17.4.3 DYNAMIC MODEL
FOR THE AUGUST TEMPERATURES AND ANNUAL FLOWS OF THE GOTO RIVER 592 173
TRANSFER FUNCTION-NOISE MODELS WITH MULTIPLE INPUTS 593 173.1
INTRODUCTION 593 173.2 MODEL DESCRIPTION 595 173.3 MODEL CONSTRUCTION
597 173.4 HYDROMETEOROLOGICAL APPLICATION 598 INTRODUCTION...... 598
MISSING DATA 599 IDENTIFYING THE DYNAMIC COMPONENT 600 COMBINING
MULTIPLE TIMES SERIES 601 THE TRANSFER FUNCTION-NOISE MODELS 602 17.6
ARMAX MODELS 605 17.7 CONCLUSIONS . . 608 APPENDIX A17.1 - ESTIMATOR FOR
TFN MODELS 609 PROBLEMS 612 REFERENCES 614 CHAPTER 18: FORECASTING WITH
TRANSFER FUNCTION-NOISE MODELS 617 18.1 INTRODUCTION 617 182 FORECASTING
PROCEDURES FOR TFN MODELS 618 182.1 OVERVIEW 618 182.2 FORECASTING
FORMULAE 619 SINGLE INPUT TFN MODEL HAVING ARMA NOISE 619 MULTIPLE INPUT
TFN MODEL HAVING ARMA NOISE 623 SEASONAL TFN MODEL 623 TFN MODEL HAVING
ARIMA NOISE 624 TFN MODEL HAVING A DETERMINISTIC TREND 626 18.2.3
APPLICATION 626 18.3 FORECASTING QUARTER-MONTHLY RIVERFLOWS 629 18.3.1
OVERVIEW 629 -XX- 18.3.2 CONSTRUCTING THE TIME SERIES MODELS 629 18.3.3
CONCEPTUAL HYDROLOGICAL MODEL 637 18.3.4 FORECASTING EXPERIMENTS 639
18.3.5 CONCLUSIONS.. *. » 641 18.4 COMBINING HYDROLOGICAL FORECASTS 641
18.4.1 OVERVIEW ~ 641 18.4.2 COMBINATION FORECASTING EXPERIMENTS 642
18.4.3 CONCLUSIONS.. »* 644 183 RECORD EXTENSIONS, CONTROL AND
SIMULATION 644 183.1 OVERVIEW *...* 644 183.2 RECORD EXTENSIONS *.. 644
183.3 CONTROL 646 183.4 SIMULATION 647 18.6 CONCLUSIONS 647 PROBLEMS 648
REFERENCES 650 PART VHI: INTERVENTION ANALYSIS 653 CHAPTER 19: BUILDING
INTERVENTION MODELS 655 19.1 INTRODUCTION 655 19.2 INTERVENTION MODELS
WITH MULTIPLE INTERVENTIONS . 660 19.2.1 INTRODUCTION 660 19.2.2 MODEL
DESCRIPTION 661 DYNAMIC COMPONENT 662 NOISE TERM 666 COMPLETE
INTERVENTION MODEL 667 EFFECTS OF AN INTERVENTION UPON THE MEAN LEVEL
667 192.3 MODEL CONSTRUCTION 670 DETECTION 671 IDENTIFICATION 674
ESTIMATION 678 DIAGNOSTIC CHECKING 679 192.4 EFFECTS OF THE ASWAN DAM ON
THE AVERAGE ANNUAL FLOWS OF THE NILE RIVER 679 CASE STUDY DESCRIPTION
679 MODEL CONSTRUCTION 680 EFFECTS OF THE INTERVENTION 684 192.5
STOCHASTIC INFLUENCE OF RESERVOIR OPERATION ON THE AVERAGE MONTHLY FLOWS
OF THE SOUTH SASKATCHEWAN RIVER 684 CASE STUDY DESCRIPTION 684 MODEL
DEVELOPMENT 686 -XXI- EFFECTS OF THE INTERVENTION * 688 INTERPRETATION
OF RESULTS 691 19.3 DATA FILLING USING INTERVENTION ANALYSIS 693 19.3.1
INTRODUCTION 693 19 3.2 TECHNIQUES FOR DATA FILLING 694 DATA FILLING
METHODS PRESENTED IN THIS TEXT 694 ADDITIONAL DATA FILLING METHODS *..
695 19.3.3 MODEL DESCRIPTION. 696 19.3.4 MODEL CONSTRUCTION 698 19.33
EXPERIMENTS TO CHECK THE PERFORMANCE OF THE DATA FILLING METHOD _ 699
19.3.6 ESTIMATING MISSING OBSERVATIONS IN THE AVERAGE MONTHLY LUCKNOW
TEMPERATURE DATA AND MIDDLE FORK RIVERFLOWS 701 19.4 INTERVENTION MODELS
WITH MULTIPLE INTERVENTIONS AND MISSING OBSERVATIONS 702 19.4.1
INTRODUCTION 702 19.4.2 MODEL DESCRIPTION 703 19.4.3 MODEL CONSTRUCTION
703 IDENTIFICATION 703 ESTIMATION 705 DIAGNOSTIC CHECKING 706 19.4.4
EXPERIMENT TO ASSESS DATA FILLING WHEN AN INTERVENTION IS PRESENT. 706
19.43 ENVIRONMENTAL IMPACT ASSESSMENT OF TERTIARY TREATMENT ON AVERAGE
MONTHLY PHOSPHOROUS LEVELS IN THE SPEED RIVER 707 193 INTERVENTION
MODELS WITH MULTIPLE INTERVENTIONS, MISSING OBSERVATIONS AND INPUT
SERIES 709 193.1 INTRODUCTION 709 193.2 MODEL DESCRIPTION 710 193.3
MODEL CONSTRUCTION 713 IDENTIFICATION 714 ESTIMATION 717 DIAGNOSTIC
CHECKS 717 193.4 EFFECTS OF A FOREST FIRE UPON THE SPRING FLOWS OF THE
PIPERS HOLE RIVER 718 CASE STUDY * 718 MODEL DEVELOPMENT 719 EFFECTS OF
THE FOREST FIRE 721 19.6 PERIODIC INTERVENTION MODELS 723 19.6.1
INTRODUCTION 723 19.6.2 PERIODIC INTERVENTION MODEL FOR THE AVERAGE
MONTHLY FLOWS OF THE SOUTH SASKATCHEWAN RIVER 724 19.6.3 OTHER TYPES OF
PERIODIC INTERVENTION MODELS 725 - X X U - 19.7 DATA COLLECTION 726 19.8
CONCLUSIONS 727 PROBLEMS 730 REFERENCES ^ 733 PART DC: MULTIPLE
INPUT-MULTIPLE OUTPUT MODELS 739 CHAPTER 20: GENERAL MULITVARIATE
AUTOREGRESSIVE MOVING AVERAGE MODELS 741 20.1 INTRODUCTION _ 741 20.2
DEFINITIONS OF MULTIVARIATE ARMA MODELS 743 202.1 INTRODUCTION 743
20.2.2 DEFINITIONS 743 GENERAL MULTIVARIATE ARMA MODEL 743 TFN MODEL 745
CARMA MODEL 746 20.3 CONSTRUCTING GENERAL MULTIVARIATE ARMA MODELS 747
20.3.1 LIMITATIONS 747 20.3.2 MODEL CONSTRUCTION 748 INTRODUCTION 748
CAUSALITY 748 IDENTIFICATION 749 ESTIMATION 750 DIAGNOSTIC CHECKING 750
20.3.3 SEASONALITY 751 DESEASONALIZED MULTIVARIATE MODEL 751 PERIODIC
MULTIVARIATE MODEL 751 20.4 HISTORICAL DEVELOPMENT 752 203 OTHER
FAMILIES OF MULTIVARIATE MODELS 755 203.1 INTRODUCTION 755 2032
DISAGGREGATION MODELS 756 203.3 GAUSSIAN AND NONGAUSSIAN VARIABLES 757
203.4 LINEAR AND NONLINEAR MODELS 758 203.5 MULTIVARIATE FRACTIONAL
AUTOREGRESSIVE-MOVING AVERAGE (FARMA) MODELS 758 203.6 TIME AND
FREQUENCY DOMAINS 758 203.7 PATTERN RECOGNITION 759 203.8 NONPARAMETRIC
TESTS 759 20.6 CONCLUSIONS 759 APPENDIX A20.1 - IDENTIFICATION METHODS
FOR GENERAL MULTIVARIATE ARMA MODELS 761 PROBLEMS 767 -XX111- REFERENCES
769 CHAPTER 21: CONTEMPORANEOUS AUTOREGRESSTVE-MOVING AVERAGE MODELS 779
21.1 INTRODUCTION 779 212 DERIVING CARMA MODELS 780 21.2.1 INTRODUCTION
780 212.3 SUBSET DEFINITION 780 212.3 CONCATENATION DEFINITION 783 21.3
CONSTRUCTING CARMA MODELS 784 21.3.1 INTRODUCTION 784 21.3.2
IDENTIFICATION 784 21.3.3 ESTIMATION 786 21.3.4 DIAGNOSTIC CHECKS 788
21.3.5 SEASONALITY 789 21.4 SIMULATING USING CARMA MODELS 790 21.4.1
INTRODUCTION 790 21.4.2 SIMULATION ALGORITHM 790 OVERALL ALGORITHM 790
CALCULATION OF THE INITIAL VALUE 792 213 PRACTICAL APPLICATIONS 792
21.5.1 INTRODUCTION 792 21.5.2 FOX AND WOLF RIVERS 793 213.3.WATER
QUALITY SERIES , 795 213.4 TWO RIVERFLOW SERIES HAVING UNEQUAL SAMPLE
SIZES... 796 21.6 CONCLUSIONS 798 APPENDIX A21.1 - ESTIMATOR FOR CARMA
MODELS HAVING UNEQUAL SAMPLE SIZES 800 PROBLEMS 802 REFERENCES 804
PARTX: HANDLING MESSY ENVIRONMENTAL DATA 807 CHAPTER 22: EXPLORATORY
DATA ANALYSIS AND INTERVENTION MODELLING IN CONFIRMATORY DATA ANALYSIS
809 22.1 INTRODUCTION 809 222 DATA FILLING USING SEASONAL ADJUSTMENT 811
22.3 EXPLORATORY DATA ANALYSIS 813 22.3.1 INTRODUCTION 813 22.3.2 TIME
SERIES PLOTS 815 22.3.3 BOX-AND-WHISKER GRAPHS 816 22.3.4
CROSS-CORRELATION FUNCTION 821 -XXIV- 22.3.5 TUKEY SMOOTHING 825
INTRODUCTION 825 BLURRED 3RSR SMOOTH 828 4253H, TWICE SMOOTH 829 22.3.6
AUTOCORRELATION FUNCTION 834 22.4 CONFIRMATORY DATA ANALYSIS USING
INTERVENTION ANALYSIS 837 22.4.1 INTRODUCTION 837 22.4.2 INTERVENTION
ANALYSIS APPLICATIONS 838 CASE STUDY 838 MIDDLE FORK FLOW INTERVENTION
MODEL 839 CABIN CREEK FLOW INTERVENTION MODEL 842 GENERAL WATER QUALITY
INTERVENTION MODEL 845 223 CONCLUSIONS 847 PROBLEMS 848 REFERENCES 850
CHAPTER 23: NONPARAMETRIC TESTS FOR TREND DETECTION 853 23.1
INTRODUCTION 853 23.2 STATISTICAL TESTS 858 23.2.1 INTRODUCTION 858
23.2.2 HYPOTHESIS TESTS 858 232.3 SIGNIFICANCE TESTS 859 23.3
NONPARAMETRIC TESTS 861 23.3.1 INTRODUCTION 861 23.3.2 NONPARAMETRIC
TESTS FOR TREND DETECTION 864 INTRODUCTION 864 INTRABLOCK METHODS 864
ALIGNED RANK METHODS 872 COMPARISON OF INTRABLOCK AND ALIGNED RANK
METHODS 874 23.3.3 GROUPING SEASONS FOR TREND DETECTION 875 23.3.4
COMBINING TESTS OF HYPOTHESES 877 23.3.5 FLOW ADJUSTMENT OF WATER
QUALITY DATA 878 23.3.6 PARTIAL RANK CORRELATION TESTS 880 INTRODUCTION
880 SPEARMAN S RHO TEST 880 SPEARMAN PARTIAL RANK CORRELATION TEST 882
COMPARISON TO THE SEASONAL MANN-KENDALL TEST 883 KENDALL PARTIAL RANK
CORRELATION COEFFICIENT 883 23.3.7 NONPARAMETRIC TEST FOR STEP TRENDS
884 23.3.8 MULTIPLE CENSORED DATA : 887 INTRODUCTION 887 CENSORING
DEFINITIONS IN SURVIVAL ANALYSIS 888 MULTIPLE CENSORING IN ENVIRONMENTAL
ENGINEERING 889 23.4 POWER COMPARISONS OF PARAMETRIC AND NONPARAMETRIC
-XXV- TREND TESTS 891 23.4.1 INTRODUCTION 891 23.4.2 AUTOCORRELATION
FUNCTION AT LAG ONE 891 23.4.3 KENDALL S TAU 893 23.4.4 ALTERNATIVE
GENERATING MODELS 894 LINEAR MODEL * 896 LOGISTICS MODEL 896 STEP
FUNCTION MODEL 896 BARNARD S MODEL 896 SECOND ORDER AUTOREGRESSIVE MODEL
897 THRESHOLD AUTOREGRESSIVE MODEL 897 23.4.5 SIMULATION EXPERIMENTS 897
LINEAR MODEL 898 LOGISTICS MODEL 899 STEP FUNCTION MODEL 899 BARNARD S
MODEL 900 SECOND ORDER AUTOREGRESSIVE MODEL 900 THRESHOLD AUTOREGRESSIVE
MODEL 901 23.4.6 CONCLUSIONS 901 233 WATER QUALITY APPLICATIONS 902
23.5.1 INTRODUCTION 902 233.2 TREND ANALYSIS OF THE LAKE ERIE WATER
QUALITY SERIES 905 SELECTING APPROPRIATE STATISTICAL TESTS 905 DATA
LISTING 906 GRAPHS OF THE DATA 909 BOX-AND-WHISKER GRAPHS 911 SEASONAL
MANN-KENDALL TESTS 915 WILCOXON SIGNED RANK TESTS 918 KRUSKAL-WALLIS
TESTS 920 23.6 CONCLUSIONS 922 APPENDIX A23.1 - KENDALL RANK CORRELATION
TEST 924 APPENDIX A23.2 - WILCOXON SIGNED RANK TEST 925 APPENDIX A23.3 -
KRUSKAL-WALLIS TEST 927 PROBLEMS 928 REFERENCES 930 CHAPTER 24:
REGRESSION ANALYSIS AND TREND ASSESSMENT 939 24.1 INTRODUCTION 939 242
REGRESSION ANALYSIS 940 24.2.1 INTRODUCTION 940 24.2.2 ROBUST LOCALLY
WEIGHTED REGRESSION SMOOTH 943 OVERVIEW 943 GENERAL PROCEDURE 944
SPECIFIC PROCEDURE 945 -XXVI- SELECTING VARIABLES 946 APPLICATIONS 947
24.2.3 BUILDING REGRESSION MODELS 949 OVERVIEW 949 LAKE ERIE WATER
QUALITY STUDY 949 24.3 TREND ANALYSIS METHODOLOGY FOR WATER QUALITY TIME
SERIES MEASURED IN RIVERS 956 24.3.1 INTRODUCTION 956 24.3.2 METHODOLOGY
DESCRIPTION 957 OVERVIEW 957 GRAPHICAL TREND STUDIES 959 MEAN MONTHLY
DATA 961 TREND TESTS 963 24.3.3 SUMMARY 968 24.4 CONCLUSIONS 970
PROBLEMS 972 REFERENCES 974 DATA APPENDIX 979 DATA ACQUISITION 979 DATA
LISTING 980 STATIONARY NONSEASONAL TIME SERIES 980 NONSTATIONARY
NONSEASONAL TIME SERIES 982 TIME SERIES CONTAINING AN INTERVENTION 985
REFERENCES 987 AUTHOR INDEX 989 SUBJECT INDEX 1001
|
| any_adam_object | 1 |
| author | Hipel, Keith W. McLeod, A. Ian |
| author_facet | Hipel, Keith W. McLeod, A. Ian |
| author_role | aut aut |
| author_sort | Hipel, Keith W. |
| author_variant | k w h kw kwh a i m ai aim |
| building | Verbundindex |
| bvnumber | BV009623412 |
| callnumber-first | G - Geography, Anthropology, Recreation |
| callnumber-label | GE40 |
| callnumber-raw | GE40 |
| callnumber-search | GE40 |
| callnumber-sort | GE 240 |
| callnumber-subject | GE - Environmental Sciences |
| classification_rvk | RB 10345 |
| ctrlnum | (OCoLC)30157283 (DE-599)BVBBV009623412 |
| dewey-full | 363.7/001/5118 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 363 - Other social problems and services |
| dewey-raw | 363.7/001/5118 |
| dewey-search | 363.7/001/5118 |
| dewey-sort | 3363.7 11 45118 |
| dewey-tens | 360 - Social problems and services; associations |
| discipline | Soziologie Geographie |
| format | Book |
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| id | DE-604.BV009623412 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:38:06Z |
| institution | BVB |
| isbn | 0444892702 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006359321 |
| oclc_num | 30157283 |
| open_access_boolean | |
| owner | DE-12 DE-703 DE-634 |
| owner_facet | DE-12 DE-703 DE-634 |
| physical | XXXVII, 1013 S. graph. Darst. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Elsevier |
| record_format | marc |
| series | Developments in water science |
| series2 | Developments in water science |
| spelling | Hipel, Keith W. Verfasser aut Time series modelling of water resources and environmental systems Keith W. Hipel ; A. Ian McLeod Amsterdam u.a. Elsevier 1994 XXXVII, 1013 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Developments in water science 45 Environnement - Modèles mathématiques ram Environnement - Statistiques ram Hydrologie - Modèles mathématiques Ressources en eau - Modèles mathématiques ram Ressources en eau - Statistiques ram Sciences de l'environnement - Modèles mathématiques Série chronologique Mathematisches Modell Environmental sciences Mathematical models Hydrology Mathematical models Time-series analysis Wasserreserve (DE-588)4124439-4 gnd rswk-swf Hydrologie (DE-588)4026309-5 gnd rswk-swf Umwelt (DE-588)4061616-2 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Wasserreserve (DE-588)4124439-4 s Umwelt (DE-588)4061616-2 s Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Hydrologie (DE-588)4026309-5 s McLeod, A. Ian Verfasser aut Developments in water science 45 (DE-604)BV000000334 45 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006359321&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hipel, Keith W. McLeod, A. Ian Time series modelling of water resources and environmental systems Developments in water science Environnement - Modèles mathématiques ram Environnement - Statistiques ram Hydrologie - Modèles mathématiques Ressources en eau - Modèles mathématiques ram Ressources en eau - Statistiques ram Sciences de l'environnement - Modèles mathématiques Série chronologique Mathematisches Modell Environmental sciences Mathematical models Hydrology Mathematical models Time-series analysis Wasserreserve (DE-588)4124439-4 gnd Hydrologie (DE-588)4026309-5 gnd Umwelt (DE-588)4061616-2 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4124439-4 (DE-588)4026309-5 (DE-588)4061616-2 (DE-588)4067486-1 |
| title | Time series modelling of water resources and environmental systems |
| title_auth | Time series modelling of water resources and environmental systems |
| title_exact_search | Time series modelling of water resources and environmental systems |
| title_full | Time series modelling of water resources and environmental systems Keith W. Hipel ; A. Ian McLeod |
| title_fullStr | Time series modelling of water resources and environmental systems Keith W. Hipel ; A. Ian McLeod |
| title_full_unstemmed | Time series modelling of water resources and environmental systems Keith W. Hipel ; A. Ian McLeod |
| title_short | Time series modelling of water resources and environmental systems |
| title_sort | time series modelling of water resources and environmental systems |
| topic | Environnement - Modèles mathématiques ram Environnement - Statistiques ram Hydrologie - Modèles mathématiques Ressources en eau - Modèles mathématiques ram Ressources en eau - Statistiques ram Sciences de l'environnement - Modèles mathématiques Série chronologique Mathematisches Modell Environmental sciences Mathematical models Hydrology Mathematical models Time-series analysis Wasserreserve (DE-588)4124439-4 gnd Hydrologie (DE-588)4026309-5 gnd Umwelt (DE-588)4061616-2 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Environnement - Modèles mathématiques Environnement - Statistiques Hydrologie - Modèles mathématiques Ressources en eau - Modèles mathématiques Ressources en eau - Statistiques Sciences de l'environnement - Modèles mathématiques Série chronologique Mathematisches Modell Environmental sciences Mathematical models Hydrology Mathematical models Time-series analysis Wasserreserve Hydrologie Umwelt Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006359321&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000000334 |
| work_keys_str_mv | AT hipelkeithw timeseriesmodellingofwaterresourcesandenvironmentalsystems AT mcleodaian timeseriesmodellingofwaterresourcesandenvironmentalsystems |