Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Hill, Mary C. (VerfasserIn), Tiedeman, Claire R. (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Hoboken, NJ Wiley-Interscience 2007
Schlagworte:
Online-Zugang:Table of contents only
Inhaltsverzeichnis
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!

MARC

LEADER 00000nam a2200000zc 4500
001 BV022531764
003 DE-604
005 20070906
007 t|
008 070726s2007 xxuabd| |||| 00||| eng d
010 |a 2005036657 
015 |a GBA652597  |2 dnb 
020 |a 047177636X  |c cloth  |9 0-471-77636-X 
020 |a 9780471776369  |9 978-0-471-77636-9 
035 |a (OCoLC)62728602 
035 |a (DE-599)DNB 2005036657 
040 |a DE-604  |b ger  |e aacr 
041 0 |a eng 
044 |a xxu  |c US 
049 |a DE-29  |a DE-91 
050 0 |a GB1001.72.M35 
082 0 |a 551.4901/5118 
084 |a RB 10354  |0 (DE-625)142220:12705  |2 rvk 
084 |a BAU 672f  |2 stub 
100 1 |a Hill, Mary C.  |e Verfasser  |4 aut 
245 1 0 |a Effective groundwater model calibration  |b with analysis of data, sensitivities, predictions, and uncertainty  |c Mary C. Hill ; Claire R. Tiedeman 
264 1 |a Hoboken, NJ  |b Wiley-Interscience  |c 2007 
300 |a XVIII, 455 S.  |b Ill., graph. Darst., Kt.  |c 24 cm 
336 |b txt  |2 rdacontent 
337 |b n  |2 rdamedia 
338 |b nc  |2 rdacarrier 
500 |a Includes bibliographical references (p. 407-426) and index 
650 4 |a Mathematisches Modell 
650 4 |a Groundwater  |x Mathematical models 
650 4 |a Hydrologic models 
650 0 7 |a Mathematisches Modell  |0 (DE-588)4114528-8  |2 gnd  |9 rswk-swf 
650 0 7 |a Grundwasser  |0 (DE-588)4022369-3  |2 gnd  |9 rswk-swf 
689 0 0 |a Grundwasser  |0 (DE-588)4022369-3  |D s 
689 0 1 |a Mathematisches Modell  |0 (DE-588)4114528-8  |D s 
689 0 |5 DE-604 
700 1 |a Tiedeman, Claire R.  |e Verfasser  |4 aut 
856 4 |u http://www.loc.gov/catdir/toc/ecip065/2005036657.html  |3 Table of contents only 
856 4 2 |m OEBV Datenaustausch  |q application/pdf  |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015738347&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA  |3 Inhaltsverzeichnis 
943 1 |a oai:aleph.bib-bvb.de:BVB01-015738347 

Datensatz im Suchindex

DE-BY-TUM_call_number 0001 2007 A 7277
DE-BY-TUM_katkey 1602950
DE-BY-TUM_location Mag
DE-BY-TUM_media_number 040006279378
_version_ 1820876554666770432
adam_text IMAGE 1 CONTENTS PREFACE 1 INTRODUCTION 1.1 BOOK AND ASSOCIATED CONTRIBUTIONS: METHODS, GUIDELINES, EXERCISES, ANSWERS, SOFTWARE, AND POWERPOINT FILES, 1 1.2 MODEL CALIBRATION WITH INVERSE MODELING, 3 1.2.1 PARAMETERIZATION, 5 1.2.2 OBJECTIVE FUNCTION, 6 1.2.3 UTILITY OF INVERSE MODELING AND ASSOCIATED METHODS, 6 1.2.4 USING THE MODEL TO QUANTITATIVELY CONNECT PARAMETERS, OBSERVATIONS, AND PREDICTIONS, 7 1.3 RELATION OF THIS BOOK TO OTHER IDEAS AND PREVIOUS WORKS, 8 1.3.1 PREDICTIVE VERSUS CALIBRATED MODELS, 8 1.3.2 PREVIOUS WORK, 8 1.4 A FEW DEFINITIONS, 12 1.4.1 LINEAR AND NONLINEAR, 12 1.4.2 PRECISION, ACCURACY, RELIABILITY, AND UNCERTAINTY, 13 1.5 ADVANTAGEOUS EXPERTISE AND SUGGESTED READINGS, 14 1.6 OVERVIEW OF CHAPTERS 2 THROUGH 15, 16 XVII 1 VII IMAGE 2 VIII CONTENTS 2 COMPUTER SOFTWARE AND GROUNDWATER MANAGEMENT PROBLEM USED IN THE EXERCISES 18 2.] COMPUTER PROGRAMS MODFLOW-2000, UCODE_2005, AND PEST, 18 2.2 GROUNDWATER MANAGEMENT PROBLEM USED FOR THE EXERCISES, 21 2.2.1 PURPOSE AND STRATEGY, 23 2.2.2 FLOW SYSTEM CHARACTERISTICS, 23 2.3 EXERCISES, 24 EXERCISE 2.1: SIMULATE STEADY-STATE HEADS AND PERFORM PREPARATORY STEPS, 25 3 COMPARING OBSERVED AND SIMULATED VALUES USING OBJECTIVE FUNCTIONS 3.1 WEIGHTED LEAST-SQUARES OBJECTIVE FUNCTION, 26 3.1.] WITH A DIAGONAL WEIGHT MATRIX, 27 3.1.2 WITH A FUH WEIGHT MATRIX, 28 3.2 ALTERNATIVE OBJECTIVE FUNCTIONS, 28 3.2.1 MAXIMUM-LIKELIHOOD OBJECTIVE FUNCTION, 29 3.2.2 LI NORM OBJECTIVE FUNCTION, 29 3.2.3 MULTIOBJECTIVE FUNCTION, 29 3.3 REQUIRERNENTS FOR ACEURATE SIMU1ATED RESULTS, 30 3.3.1 ACEURATE MODEL, 30 3.3.2 UNBIASED OBSERVATIONS AND PRIOR INFORMATION, 30 3.3.3 WEIGHTING REFLECTS ERRORS, 31 3.4 ADDITIONAL ISSUES 3.4.] PRIOR INFORMATION, 32 3.4.2 WEIGHTING, 34 3.4.3 RESIDUALS AND WEIGHTED RESIDUALS, 35 3.5 LEAST-SQUARES OBJECTIVE-FUNCTION SURFACES, 35 3.6 EXERCISES, 36 EXERCISE 3.]: STEADY-STATE PARAMETER DEFINITION, 36 EXERCISE 3.2: OBSERVATIONS FOR THE STEADY-STATE PROBLEM, 38 EXERCISE 3.3: EVALUATE MODEL FIT USING STARTING PARAMETER VALUCS, 40 4 DETERRNINING THE INFORMATION THAT OBSERVATIONS PROVIDE ON PARAMETER VALUES USING FIT-INDEPENDENT STATISTICS 4.1 USING OBSERVATIONS, 42 4.1. I MODEL CONSLRUCTION AND PARAMETER DEFINITION, 42 4.1.2 PARAMETER VALUES, 43 26 41 IMAGE 3 CONTENTS IX 4.2 WHEN TO DETERMINE THE INFORMATION THAT OBSERVATIONS PROVIDE ABOUT PARAMETER VALUES, 44 4.3 FIT-INDEPENDENT STATISTICS FOR SENSITIVITY ANALYSIS, 46 4.3.1 SENSITIVITIES, 47 4.3.2 SCALING, 48 4.3.3 DIMENSIONLESS SCALED SENSITIVITIES (DSS), 48 4.3.4 COMPOSITE SCALED SENSITIVITIES (CSS), 50 4.3.5 PARAMETER CORRELATION COEFFICIENTS (PCC), 51 4.3.6 LEVERAGE STATISTICS, 54 4.3.7 ONE-PERCENT SCALED SENSITIVITIES, 54 4.4 ADVANTAGES AND LIMITATIONS OF FIT-INDEPENDENT STATISTICS FOR SENSITIVITY ANALYSIS, 56 4.4.1 SCALED SENSITIVITIES, 56 4.4.2 PARAMETER CORRELATION COEFFICIENTS, 58 4.4.3 LEVERAGE STATISTICS, 59 4.5 EXERCISES, 60 EXERCISE 4.1: SENSITIVITY ANALYSIS FOR THE STEADY-STATE MODEL WITH STARTING PARAMETER VALUES, 60 5 ESTLMATING PARAMETER VALUES 5.1 THE MODIFIED GAUSS-NEWTON GRADIENT METHOD, 68 5.1.1 NORMAL EQUATIONS, 68 5.1.2 AN EXAMPLE, 74 5.1.3 CONVERGENCE CRITERIA, 76 5.2 ALTERNATIVE OPTIMIZATION METHODS, 77 5.3 MULTIOBJECTIVE OPTIMIZATION, 78 5.4 LOG-TRANSFORMED PARAMETERS, 78 5.5 USE OF LIMITS ON ESTIMATED PARAMETER VALUES, 80 5.6 EXERCISES, 80 EXERCISE 5.1: MODIFIED GAUSS-NEWTON METHOD AND APPLICATION TO A TWO-PARAMETER PROBLEM, 80 EXERCISE 5.2: ESTIMATE THE PARAMETERS OF THE STEADY-STATE MODEL, 87 6 EVALUATING MODEL FIT 6.1 MAGNITUDE OF RESIDUALS AND WEIGHTED RESIDUALS, 93 6.2 IDENTIFY SYSTEMATIC MISFIT, 94 6.3 MEASURES OF OVERALL MODEL FIT, 94 6.3.1 OBJECTIVE-FUNCTION VALUE, 95 67 93 IMAGE 4 X CONTENTS 6,3.2 CALCULATED ERROR VARIANCE AND STANDARD ERROR, 95 6.3.3 AIC, AIC C , AND EIC STATISTICS, 98 6.4 ANALYZING MODEL FIT GRAPHICALLY AND RELATED STATISTICS, 99 6.4.] USING GRAPHICAL ANALYSIS OF WEIGHTED RESIDUALS 10 DETECT MODEL ERROR, 100 6.4.2 WEIGHTED RESIDUALS VERSUS WEIGHTED OR UNWEIGHTED SIMULATED VALUES AND MINIMUM, MAXIMUM, AND AVERAGE WEIGHTED RESIDUALS, 100 6.4.3 WEIGHTED OR UNWEIGHTED OBSERVATIONS VERSUS SIMULATED VALUES AND CORRELATION COEFFICIENT R, 105 6.4.4 GRAPHS AND MAPS USING INDEPENDENT VARIABLES AND THE RUNS STATISTIC, 106 6.4,5 NORMAL PROBABILITY GRAPHS AND CORRELATION COEFFICIENT R~, 108 6.4.6 ACCEPTABLE DEVIATIONS FROM RANDOM, NORRNALLY DISTRIBUTED WEIGHTED RESIDUALS, 111 6.5 EXERCISES, 113 EXERCISE 6.1: STATISTICAL MEASURES OF OVERALL FIT, 113 EXERCISE 6.2: EVALUATE GRAPH MODEL FIT AND RELATED STATISTICS, 115 7 EVALUATING ESTIMATED PARAMETER VALUES AND PARAMETER UNCERTAINTY 7.1 REEVALUATING COMPOSITE SCALED SENSITIVITIES, ]24 7.2 USING STATISTICS FROM THE PARAMETER VARIANCE-COVARIANCE MATRIX. 125 7.2, I FIVE VERSIONS OF THE VARIANCE-COVARIANCE MATRIX, 125 7.2.2 PARAMETER VARIANCES, COVARIANCES, STANDARD DEVIATIONS, COEFFICIENTS OF VARIATION, AND CORRELATION COEFFICIENTS, 126 7.2.3 RELATION BETWEEN SAMPIE AND REGRESSION STATISTICS, 127 7,2.4 STATISTICS FOR LOG-TRANSFORRNED PARAMETERS, 130 7.2,5 WHEN TO USE THE FIVE VERSIONS OF THE PARAMETER VARIANCE-COVARIANCE MATRIX, 130 7,2.6 SOME ALTERNATE METHODS: EIGENVECTORS, EIGENVALUES, AND SINGULAR VALUE DECOMPOSITION, 132 7.3 IDENTIFYING OBSERVATIONS IMPORTANT TO ESTIMATED PARAMETER VAL LIES, 132 7.3.] LEVERAGE STATISTICS, 134 7,3,2 LNFLUENCE STATISTICS, 134 7.4 UNIQUCNESS AND OPTIMALITY OF THE ESTIMATED PARAMETER VALUES, ]37 7.5 QUANTIFYING PARAMETER VALUE UNCERTAINTY, 137 124 IMAGE 5 CONTENTS 7.5.1 INFERENTIAL STATISTICS, 137 7.5.2 MONTE CARLO METHODS, 140 7.6 CHECKING PARAMETER ESTIMATES AGAINST REASONABLE VALUES, 140 7.7 TESTING LINEARITY, 142 7.8 EXERCISES, 145 EXERCISE 7.1: PARAMETER STATISTICS, 145 EXERCISE 7.2: CONSIDER ALL THE DIFFERENT CORRELATION COEFFICIENTS PRESENTED, 155 EXERCISE 7.3: TEST FOR LINEARITY, 155 8 EVALUATING MODEL PREDICTIONS, DATA NEEDS, AND PREDICTION UNCERTAINTY 8.1 SIMULATING PREDICTIONS AND PREDICTION SENSITIVITIES AND STANDARD DEVIATIONS, 158 8.2 USING PREDICTIONS TO GUIDE COLLECTION OF DATA THAT DIRECTLY CHARACTERIZE SYSTEM PROPERTIES, 159 8.2.1 PREDICTION SCALED SENSITIVITIES (PSS) , 160 8.2.2 PREDICTION SCALED SENSITIVITIES USED IN CONJUNCTION WITH COMPOSITE SCALED SENSITIVITIES, 162 8.2.3 PARAMETER CORRELATION COEFFICIENTS WITHOUT AND WITH PREDICTIONS, 162 8.2.4 CORNPOSITE AND PREDICTION SCALED SENSITIVITIES USED WITH PARAMETER CERRELATION COEFFICIENTS, 165 8.2.5 PARAMETER-PREDICTION (PPR) STATISTIC, 166 8.3 USING PREDICTIONS TO GUIDE COLLECTION OF OBSERVATION DATA, 170 8.3.1 USE OF PREDICTION, COMPOSITE, AND DIMENSIONLESS SCA1ED SENSITIVITIES AND PARAMETER CORRELATION COEFFICIENTS, 170 8.3.2 OBSERVATION-PREDICTION (OPR) STATISTIC, 171 8.3.3 INSIGHTS ABOUT THE OPR STATISTIC FROM OTHER FIT-INDEPENDENT STATISTICS, 173 8.3.4 IMPLICATIONS FOR MONITARING NETWORK DESIGN, 174 8.4 QUANTIFYING PREDICTION UNCERTAINTY USING INFERENTIAL STATISTICS, 174 8.4.1 DEFINITIONS, 175 8.4.2 LINEAR CONFIDENCE AND PREDICTION INTERVALS ON PREDICTIONS, 176 8.4.3 NONLINEAR CONFIDENCE AND PREDICTION INTERVALS, 177 8.4.4 USING THE THEIS EXAMPLE TO UNDERSTAND LINEAR AND NONLINEAR CONFIDENCE INTERVA1S, 181 8.4.5 DIFFERENCES AND THEIR STANDARD DEVIATIONS, CONFIDENCE INTERVALS, AND PREDICTION INTERVALS, 182 XI 158 IMAGE 6 XII CONTENTS 8.4.6 USING CONFIDENCE INTERVALS TO SERVE THE PURPOSES OF TRADITIANAL SENSITIVITY ANALYSIS, 184 8.5 QUANTIFYING PREDICTION UNCERTAINTY USING MONTE CARLO ANALYSIS, 185 8.5.1 ELEMENTS OF A MONTE CAR10 ANALYSIS, 185 8.5.2 RELATION BETWEEN MONTE CARLO ANALYSIS AND LINEAR AND NONLINEAR CONFIDENCE INTERVALS, 187 8.5.3 USING THE THEIS EXAMP1E TO UNDERSTAND MONTE CARLO METHODS, 188 8.6 QUANTIFYING PREDICTION UNCERTAINTY USING ALTERNATIVE MODELS, 189 8.7 TESTING MODEL NONLINEARITY WITH RESPECT TO THE PREDICTIONS, 189 8.8 EXERCISES, 193 EXERCISE 8.1: PREDICT ADVECTIVE TRANSPORT AND PERFARM SENSITIVITY ANALYSIS, 195 EXERCISE 8.2: PREDICTION UNCERTAINTY MEASURED USING INFERENTIAL STATISTICS, 207 9 CALIBRATING TRANSIENT AND TRANSPORT MODELS AND RECALIBRATING EXISTING MODELS 9.1 STRATEGIES FOR CALIBRATING TRANSIENT MODELS, 213 9.1.1 INITIAL CONDITIONS, 213 9.1.2 TRANSIENT OBSERVATIONS, 214 9.1.3 ADDITIONAL MODEL INPUTS, 216 9.2 STRATEGIES FOR CALIBRATING TRANSPORT MODELS, 217 9.2.1 SELECTING PROCESSES TO INCLUDE, 217 9.2.2 DEFINING SOURCE GEOMETRY AND CONCENTRATIONS, 218 9.2.3 SCALE ISSUES, 219 9.2.4 NUMERICAL ISSUES: MODEL ACCURACY AND EXECUTION TIME, 220 9.2.5 TRANSPORT OBSERVATIONS, 223 9.2.6 ADDITIONAL MODEL INPUTS, 225 9.2.7 EXAMPLES OF OBTAINING A TRACTABLE, USEFUL MODEL, 226 9.3 STRATEGIES FOR RECALIBRATING EXISTING MODELS, 227 9.4 EXERCISES (OPTIONAL), 228 EXERCISES 9.1 AND 9.2: SIMULATE TRANSIENT HYDRAULIC HEADS UND PERFORM PREPARATORY STEPS, 229 EXERCISE 9.3: TRANSIENT PARAMETER DEFINITION, 230 213 IMAGE 7 CONTENTS EXERCISE 9.4: OBSERVATIONS FOR THE TRANSIENT PROBLEM, 231 EXERCISE 9.5: EVALUATE TRANSIENT MODEL FIT USING STARTING PARAMETER VALUES, 235 EXERCISE 9.6: SENSITIVITY ANALYSIS FOR THE INITIAL MODEL, 235 EXERCISE 9.7: ESTIMATE PARAMETERS FOR THE TRANSIENT SYSTEM BY NONLINEAR REGRESSION, 243 EXERCISE 9.8: EVALUATE MEASURES OF MODEL FIT, 244 EXERCISE 9.9: PERFARM GRAPHICAL ANALYSES OF MODEL FIT AND EVALUATE RELATED STATISTICS, 246 EXERCISE 9.10: EVALUATE ESTIMATED PARAMETERS, 250 EXERCISE 9.11: TEST FOR LINEARITY, 253 EXERCISE 9.12: PREDICTIONS, 254 10 GUIDELINES FOR EFFECTIVE MODELING 10.1 PURPOSE OF THE GUIDELINES, 263 10.2 RELATION TO PREVIOUS WORK, 264 10.3 SUGGESTIONS TOR EFFECTIVE IMPLEMENTATION, 264 11 GUIDELINES 1 THROUGH 8-MODEL DEVELOPMENT GUIDELINE 1: APPLY THE PRINCIPLE OF PARSIMONY, 268 G1.1 PROBLEM, 269 G1.2 CONSTRUCTIVE APPROACHES, 270 GUIDELINE 2: USE A BROAD RANGE OF SYSTEM INFORMATION TO CONSTRAIN THE PROBLEM, 272 02.1 DATA ASSIMILATION, 273 02.2 USING SYSTEM INFORMATION, 273 02.3 DATA MANAGEMENT, 274 02.4 APPLICATION: CHARACTERIZING A FRACTURED DOLOMITE AQUIFER, 277 GUIDELINE 3: MAINTAIN A WELL-POSED, COMPREHENSIVE REGRESSION PROBLEM, 277 03.1 EXAMPLES, 278 03.2 EFFECTS OF NONLINEARITY ON THE CSS AND PEE, 281 GUIDELINE 4: INCLUDE MANY KINDS OF DATA AS OBSERVATIONS IN THE REGRESSION, 284 04.1 INTERPOLATED OBSERVATIONS , 284 G4.2 CLUSTERED OBSERVATIONS, 285 04.3 OBSERVATIONS THAT ARE INCONSISTENT WITH MODEL CONSTRUCTION, 286 XIII 260 268 IMAGE 8 XIV CONTENTS 04.4 APPLICATIONS: USING DIFFERENT TYPES OF OBSERVATIONS TO CALIBRATE GROUNDWATER FLOW AND TRANSPORT MODELS, 287 GUIDELINE 5: USE PRIOR INFORMATION CAREFULLY, 288 05. I USE OF PRIOR INFORMATION COMPARED WITH OBSERVATIONS, 288 05.2 HIGHLY PARAMETERIZED MODELS, 290 05.3 APPLICATIONS: GEOPHYSICAL DATA, 291 GUIDELINE 6: ASSIGN WEIGHTS THAT REFLECT ERRORS, 291 G6.L DETERMINE WEIGHTS, 294 06.2 ISSUES OF WEIGHTING IN NONLINEAR REGRESSION, 298 GUIDELINE 7: ENCOURAGE CONVERGENCE BY MAKING THE MODEL MORE ACEURATE AND EVALUATING THE OBSERVATIONS, 306 GUIDELINE 8: CONSIDER ALTERNATIVE MODELS, 308 G8. I DEVELOP ALTERNATIVE MODELS, 309 08.2 DISCRIMINATE BETWEEN MODELS, 310 G8.3 SIMULATE PREDICTIONS WITH ALTERNATIVE MODELS, 312 08.4 APPLICATION, 313 12 GUIDELINES 9 AND LO-MODEL TESTING GUIDELINE 9: EVALUATE MODEL FIT, 316 G9.1 DETERMINE MODEL FIT,. 316 09.2 EXARNINE FIT FOR EXISTING OBSERVATIONS IMPORTANT TO THE PURPOSE OF THE MODEL, 320 G9.3 DIAGNOSE THE CAUSE OF POOF MODEL FIT, 320 GUIDELINE 10: EVALUATE OPTIRNIZED PARAMETER VALUES, 323 GI O. I QUANTIFY PARAMETER-VALUE UNCERTAINTY, 323 G10.2 USE PARAMETER ESTIMATES TO DETECT MODEL ERROR, 323 G10.3 DIAGNOSE THE CAUSE OF UNREASONABLE OPTIMAL PARAMETER ESTIMATES, 326 GIO.4 IDENTIFY OBSERVATIONS IMPORTANT TO THE PARAMETER ESTIMATES, 327 GIO.5 REDUCE OR INCREASE THE NUMBER OF PARAMETERS, 328 13 CUIDELINES 11 AND 12- POTENTIAL NEW DATA GUIDELINE 11: IDENTIFY NEW DATA TO IMPROVE SIMULATED PROCESSES, FEATURES, UND PROPERTIES, 330 GUIDELINE LZ: IDENTIFY NEW DATA TO IMPROVE PREDICTIONS, 334 G 12.1 POTENTIAL NEW DATA TO IMPROVE FEATURES AND PROPERTIES GOVERNING SYSTEM DYNARNICS, 334 G 12.2 POTENTIAL NEW DATA TO SUPPORT OBSERVATIONS, 335 315 329 IMAGE 9 CONTENTS 14 GUIDELINES 13 AND 14-PREDICTION UNCERTAINTY GUIDELINE 13: EVALUATE PREDICTION UNCERTAINTY AND ACCURACY USING DETERMINISTIC METHODS, 337 GL3.1 USE REGRESSION TO DETERMINE WHETHER PREDICTED VALUES ARE CONTRADIETED BY THE CALIBRATED MODEL, 337 G13.2 USE ORNITTED DATA AND POSTAUDITS, 338 GUIDELINE 14: QUANTIFY PREDICTION UNCERTAINTY USING STATISTICAL METHODS, 339 G14.1 INFERENTIAL STATISTICS, 341 G14.2 MONTE CARLO METHODS, 34] 15 USING AND TESTING THE METHODS AND GUIDELINES 15.1 EXECUTION TIME ISSUES, 345 15.2 FIE1D APPLICATIONS AND SYNTHETIC TEST CASES, 347 15.2.1 THE DEATH VALLEY REGIONAL FLOW SYSTEM, CALIFORNIA AND NEVADA, USA, 347 15.2.2 GRINDSTED LANDFILL, DENMARK, 370 APPENDIX A: OBJECTIVE FUNCTION ISSUES A.1 DERIVATION OF THE MAXIMUM-LIKELIHOOD OBJECTIVE FUNCTION, 375 A2 RELATION OF THE MAXIMURN-LIKELIHOOD AND LEAST-SQUARES OBJECTIVE FUNCTIONS, 376 A3 ASSUMPTIONS REQUIRED FOR DIAGONAL WEIGHTING TO BE CORRECT, 376 A4 REFERENCES, 381 APPENDIX B: CALCULATION DETAILS OF THE MODIFIED GAUSS-NEWTON METHOD B.1 VECTORS AND MATRICES FOR NONLINEAR REGRESSION, 383 B.2 QUASI-NEWTON UPDATING OF THE NORMAL EQUATIONS, 384 B.3 CALCULATING THE DAMPING PARAMETER, 385 BA SOLVING THE NORMAL EQUATIONS, 389 B.5 REFERENCES, 390 APPENDIX C: TWO IMPORTANT PROPERTIES OF LINEAR REGRESSION AND THE EFFECTS OF NONLINEARITY C.] IDENTITIES NEEDED FOR THE PROOFS, 392 C.1.1 TRUE LINEAR MODEL, 392 C.1.2 TRUE NONLINEAR MODEL, 392 XV 337 345 374 383 391 IMAGE 10 XVI CONTENTS CL.3 LINEARIZED TRUE NONLINEAR MODEL, 392 CIA APPROXIMATE LINEAR MODEL, 392 CI.5 APPROXIMATE NONLINEAR MODEL, 393 C.1.6 LINEARIZED APPROXIMATE NONLINEAR MODEL, 393 C.L .7 THE IMPORTANCE OF X AND X, 394 CI .8 CONSIDERING MANY OBSERVATIONS, 394 C.1.9 NORMAL EQUATIONS, 395 C.L. I0 RANDOM VARIABLES, 395 C.I. I I EXPECTED VALUE, 395 C.1.12 VARIANCE-COVARIANCE MATRIX OF A VECTOR, 395 C.2 PROOF OF PROPERTY 1: PARAMETERS ESTIMATED BY LINEAR REGRESSION ARE UNBIASED, 395 C.3 PROOF OF PROPERTY 2: THE WEIGHT MATRIX NEEDS TO BE DEFINED IN A PARTICULAR WAY FOR EQ. (7.1) TO APPLY AND FOR THE PARAMETER ESTIMATES TO HAVE THE SMALLEST VARIANCE, 396 CA REFERENCES, 398 APPENDIX D: SELECTED STATISTICAL TABLES 0.1 REFERENCES, 406 REFERENCES INDEX 399 407 427
any_adam_object 1
author Hill, Mary C.
Tiedeman, Claire R.
author_facet Hill, Mary C.
Tiedeman, Claire R.
author_role aut
aut
author_sort Hill, Mary C.
author_variant m c h mc mch
c r t cr crt
building Verbundindex
bvnumber BV022531764
callnumber-first G - Geography, Anthropology, Recreation
callnumber-label GB1001
callnumber-raw GB1001.72.M35
callnumber-search GB1001.72.M35
callnumber-sort GB 41001.72 M35
callnumber-subject GB - Physical Geography
classification_rvk RB 10354
classification_tum BAU 672f
ctrlnum (OCoLC)62728602
(DE-599)DNB 2005036657
dewey-full 551.4901/5118
dewey-hundreds 500 - Natural sciences and mathematics
dewey-ones 551 - Geology, hydrology, meteorology
dewey-raw 551.4901/5118
dewey-search 551.4901/5118
dewey-sort 3551.4901 45118
dewey-tens 550 - Earth sciences
discipline Geologie / Paläontologie
Bauingenieurwesen
Geographie
format Book
fullrecord <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01964nam a2200493zc 4500</leader><controlfield tag="001">BV022531764</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20070906 </controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">070726s2007 xxuabd| |||| 00||| eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a">2005036657</subfield></datafield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">GBA652597</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">047177636X</subfield><subfield code="c">cloth</subfield><subfield code="9">0-471-77636-X</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780471776369</subfield><subfield code="9">978-0-471-77636-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)62728602</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB 2005036657</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29</subfield><subfield code="a">DE-91</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GB1001.72.M35</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">551.4901/5118</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10354</subfield><subfield code="0">(DE-625)142220:12705</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BAU 672f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hill, Mary C.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Effective groundwater model calibration</subfield><subfield code="b">with analysis of data, sensitivities, predictions, and uncertainty</subfield><subfield code="c">Mary C. Hill ; Claire R. Tiedeman</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley-Interscience</subfield><subfield code="c">2007</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVIII, 455 S.</subfield><subfield code="b">Ill., graph. Darst., Kt.</subfield><subfield code="c">24 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (p. 407-426) and index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematisches Modell</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Groundwater</subfield><subfield code="x">Mathematical models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hydrologic models</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Grundwasser</subfield><subfield code="0">(DE-588)4022369-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Grundwasser</subfield><subfield code="0">(DE-588)4022369-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Mathematisches Modell</subfield><subfield code="0">(DE-588)4114528-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tiedeman, Claire R.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="u">http://www.loc.gov/catdir/toc/ecip065/2005036657.html</subfield><subfield code="3">Table of contents only</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">OEBV Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&amp;doc_library=BVB01&amp;local_base=BVB01&amp;doc_number=015738347&amp;sequence=000001&amp;line_number=0001&amp;func_code=DB_RECORDS&amp;service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-015738347</subfield></datafield></record></collection>
id DE-604.BV022531764
illustrated Illustrated
indexdate 2024-12-23T20:07:42Z
institution BVB
isbn 047177636X
9780471776369
language English
lccn 2005036657
oai_aleph_id oai:aleph.bib-bvb.de:BVB01-015738347
oclc_num 62728602
open_access_boolean
owner DE-29
DE-91
DE-BY-TUM
owner_facet DE-29
DE-91
DE-BY-TUM
physical XVIII, 455 S. Ill., graph. Darst., Kt. 24 cm
publishDate 2007
publishDateSearch 2007
publishDateSort 2007
publisher Wiley-Interscience
record_format marc
spellingShingle Hill, Mary C.
Tiedeman, Claire R.
Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty
Mathematisches Modell
Groundwater Mathematical models
Hydrologic models
Mathematisches Modell (DE-588)4114528-8 gnd
Grundwasser (DE-588)4022369-3 gnd
subject_GND (DE-588)4114528-8
(DE-588)4022369-3
title Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty
title_auth Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty
title_exact_search Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty
title_full Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty Mary C. Hill ; Claire R. Tiedeman
title_fullStr Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty Mary C. Hill ; Claire R. Tiedeman
title_full_unstemmed Effective groundwater model calibration with analysis of data, sensitivities, predictions, and uncertainty Mary C. Hill ; Claire R. Tiedeman
title_short Effective groundwater model calibration
title_sort effective groundwater model calibration with analysis of data sensitivities predictions and uncertainty
title_sub with analysis of data, sensitivities, predictions, and uncertainty
topic Mathematisches Modell
Groundwater Mathematical models
Hydrologic models
Mathematisches Modell (DE-588)4114528-8 gnd
Grundwasser (DE-588)4022369-3 gnd
topic_facet Mathematisches Modell
Groundwater Mathematical models
Hydrologic models
Grundwasser
url http://www.loc.gov/catdir/toc/ecip065/2005036657.html
http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015738347&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv AT hillmaryc effectivegroundwatermodelcalibrationwithanalysisofdatasensitivitiespredictionsanduncertainty
AT tiedemanclairer effectivegroundwatermodelcalibrationwithanalysisofdatasensitivitiespredictionsanduncertainty