Handbook of HydroInformatics

419 Pages·2023·42.101 MB·English

by  Saeid Eslamian, Faezeh Eslamian

Handbook of HydroInformatics
Handbook
of HydroInformatics
Volume II: Advanced Machine
Learning Techniques
Edited by
Saeid Eslamian
DepartmentofWaterEngineering,CollegeofAgriculture,IsfahanUniversityofTechnology,Isfahan,Iran
Faezeh Eslamian
McGill University, Montreal, QC, Canada
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–
To Late George Edward Pelham Box (British Statistician: 1919 2013)
“All models are wrong, but some are useful.”
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Contents
Contributors xiii 3. Computations of probable
AbouttheEditors xvii maximum precipitation estimates
Preface xix
NorElizaAlias,SheikhHefzulBari,
RasnaviParamasivam,and
1. Analyzingspatiotemporalvariation FarizulNizamAbdullah
of land use and land cover data 1. Introduction 35
1.1 BackgroundandimportanceofPMP
DinagarapandiPandi,
SaravananKothadaraman, estimations 35
MohanKuppusamy,andSaeidEslamian 2. MethodologyofPMPestimation 36
2.1 Physicalmethod 36
1. Introduction 1
2.2 Statisticalmethod 37
2. Datapreparation 3
2.3 Multifractalapproach 38
3. Visualinterpretations 4
3. StatisticalPMPestimates:
4. LULCdistribution 5
Acase-study 40
5. LULCchangedetection 5
3.1 HershfieldPMPestimatesin
6. Imageinterpretation 10
Malaysia 40
7. LAImodel 10
4. Conclusions 44
8. Comparethevisualinterpretationvs
References 45
imageinterpretation 12
9. Conclusions 12
4. Deep learning: Long short-term
Acknowledgment 13
memoryinhydrologicaltimeseries
References 13
TaesamLeeandVijayP.Singh
2. Artificial Intelligence-based model 1. Introduction 49
fusion approach in hydroclimatic 2. Modeldescriptionoflongshort-term
studies memory(LSTM) 49
2.1 Neuralnetwork 49
VahidNourani,ElnazSharghi, 2.2 Recurrentneuralnetwork 51
NazaninBehfar,FahreddinSadikoglu,
2.3 LSTM 52
andSaeidEslamian
3. Trainingnetworkand
1. Introduction 15 backpropagation 53
2. Mathematicalconcepts 16 3.1 Feedforwardandbackward
2.1 Ensembletechniques 16 propagation 53
2.2 Hybridtechniques 21 3.2 Gradientdescentmethod 54
3. Someapplications 22 3.3 BackpropagationofRNN 54
3.1 Ensembletechniques 24 3.4 Backpropagationthroughtimeof
3.2 Hybridtechniques 27 RNN 55
4. Conclusions 30 3.5 Backpropagationthroughtimefor
References 31 LSTM 57
vii
viii Contents
4. VariantsofLSTM 59 2.1 Forestcanopywaterbalance 78
4.1 PeepholeLSTM 59 2.2 Waterbalanceintropicalforested
4.2 Gatedrecurrentunit 60 watersheds 81
4.3 MultiplicativeLSTM 60 2.3 Soilmoistureinforestareas 82
4.4 Sequence-to-sequence(seq2seq) 2.4 Geochemistryintropicalforests 84
LSTM 60 3. Measurementsoftherainfall
4.5 BidirectionalLSTM 61 interceptioncomponents 85
5. Normalizationandhyperparameter 3.1 Standardmeasurements 85
selection 62 3.2 Exsitumethodsforindividual
5.1 Normalization 62 trees 88
5.2 Estimationofhyperparameters 62 3.3 Forestparameters 89
6. LSTMapplicationsin 4. Rainfallinterceptionmodeling 90
hydrometeorologicalvariables 63 4.1 Conceptualmodels 90
6.1 LSTManditsvariantsfor 4.2 Statisticalandmachinelearningtools
prediction 63 forecohydrologicaldatahandling 93
6.2 HybridLSTM 63 5. Conclusions 96
6.3 SimulationmodelingwithLSTM 64 References 96
7. Employeddeeplearningprogramsfor
LSTM 64 7. Emotional artificial neural
7.1 TensorflowandKeraswith network: A new ANN model in
Python 64 hydroinformatics
7.2 Matlab 65
8. Conclusions 65 VahidNourani,IbrahimKhalilUmar,
Hu€seyinGo€kc¸ekus¸,andSaeidEslamian
References 66
1. Introduction 105
5. Dimensionality reduction of 2. Mathematicalconceptsofemotional
correlated meteorological artificialneuralnetwork 106
variables by Bayesian network- 2.1 Feedforwardneuralnetwork
based graphical modeling (FFNN) 106
2.2 Emotionalartificialneuralnetwork
KironmalaChandaandPrabalDas
(EANN) 107
1. Introduction 69 2.3 DifferencebetweenFFNNand
2. Studyareaanddataused 70 EANN 108
2.1 Studyarea 70 2.4 Datapreprocessingandperformance
2.2 Dataused 70 evaluation 108
3. Methodology 71 2.5 Dominantinputsselection 109
4. Resultsanddiscussions 72 3. SomeapplicationsofEANN 111
4.1 Directedacyclicgraphsobtainedfrom 4. Conclusions 113
HCandMMHCalgorithms 72 References 113
4.2 Utilityoftheconditionaldependence
8. Exploringnature-basedadaptation
structure 73
5. Conclusions 75 solutions for urban ecohydrology:
References 75 Definitions, concepts, institutional
framework, and demonstration
6. The ecohydrological function of
MarioJ.AlSayah,Pierre-AntoineVersini,
the tropical forest rainfall
andDanielSchertzer
interception: Observation and
modeling 1. Introduction 117
2. Nature-basedadaptationsolutions
CarlosR.Mello,Andre(cid:1) F.Rodrigues, (NBaS):Conceptualframeworkand
MarcelaC.N.S.Terra,VanessaA.Mantovani,
position 118
Jose(cid:1) A.JunqueiraJunior,andJose(cid:1) M.Mello
2.1 NBaS:Conceptsandterminology 118
1. Introduction 77 2.2 NBaSintheinternational,regional,
2. Canopywaterbalance:conceptsand andnationalframeworks 120
generalaspectsofthemonitoring 78 3. NbaSandecohydrology 122
Contents ix
4. Theneedforphysically-based 3.5 Modeluncertainties 164
evidence 124 3.6 Regionalizationmethodsin
4.1 Potentialandlimitationsofgreen rainfall-runoffmodeling
roofs 127 (caseofungaugedbasins) 165
4.2 Greenroofs:Ameansforhybridizing 4. Conclusions 165
thegray 128 References 166
5. Conclusions 128 Furtherreading 167
5.1 Adaptbylearningandlearningto
adapt 129 11. Identification of soil erosion sites
5.2 AreNBaSthewayforward? 129
in semiarid zones: Using GIS,
Acknowledgments 130
remote sensing, and PAP/RAC
References 130
model
9. Fuzzy-based large-scale KhadijaDiani,SaidEttazarini,
teleconnection modeling of YoussefHahou,HichamElBelrhiti,
WafaeAllaoui,KaoutarMounir,and
monthly precipitation
AbdelAliGourfi
VahidNourani,HessamNajafi,ElnazSharghi,
1. Introduction 169
andSaeidEslamian
2. Materialsandmethod 170
1. Introduction 137 2.1 Studyarea 170
2. Materialsandmethods 138 2.2 PAP/RACmodelapplication 171
2.1 Teleconnection’simpacton 2.3 Theaccuracytest 173
hydroclimatologicsystems 138 3. Resultsanddiscussion 173
2.2 Proposedmethodology 139 3.1 Predictivephase 173
2.3 Associationrule 139 3.2 Descriptivephase 178
2.4 Fuzzylogic 141 3.3 Integrationphase 180
2.5 Efficiencycriteria 141 3.4 Globaldiagnosisofthetestaccuracy
2.6 Studyareaanddata 142 assessment 180
3. Resultsanddiscussion 144 4. Conclusions 182
4. Conclusions 151 References 182
References 151
12. Metrics of the water performance
10. Hydrologic models classification,
engineering modeling
calibration, and validation
VahidNourani,ElnazSharghi,
RimCherif,MarouaBouteffeha, NardinJabbarianPaknezhad,and
EmnaGargouri-Ellouze,andSaeidEslamian SaeidEslamian
1. Introduction 155 1. Introduction 185
2. Hydrologicalmodelingforintegrated 2. Typesofhydro-climatologicalmodeling
watermanagement 156 andmetrics 186
2.1 Classificationofhydrological 2.1 Pointprediction 186
models 157 2.2 Predictionintervalsofmodeling 193
2.2 Descriptionofsomecommon 2.3 Binaryclassification 193
hydrologicalmodeltypes 158 2.4 Inputselectionmethodsin
2.3 Rainfallrunofftransformation modeling 194
methodsinphysicalmodels 159 2.5 Decisionmakingmodels 195
2.4 Hydrologicmodelcomponents 159 2.6 Hydrographsinhydrological
3. Modelcalibrationandvalidation 161 modeling 196
3.1 Modelparameterization 161 2.7 Flow-durationcurves 197
3.2 Modelcalibration 161 2.8 Informationtheory 197
3.3 Optimizationmethodsformodel 3. Someapplicationsofthemetrics 198
calibration 162 3.1 Applicationofthepointpredictionin
3.4 Modelvalidation 164 hydro-climatologicalmodeling 198
x Contents
3.2 ApplicationofthePIsinhydro- 2.8 Generalizedleastsquares(GLS)
climatologicalmodeling 199 regressionwithARerrors 229
3.3 Applicationofthebinary 2.9 T-testfordifferencebetween
classificationinhydro-climatological means 229
modeling 199 3. Casestudies 230
3.4 Applicationoftheinputselectionin 4. Conclusions 234
hydro-climatologicalmodeling 199 References 235
3.5 Applicationofthedecisionmakingin
hydro-climatologicalmodeling 199 15. Voting-based extreme learning
3.6 Applicationofthehydrographsin machine: Potential of linking soil
hydro-climatologicalmodeling 200 moisture content to soil
3.7 Applicationoftheflow-duration temperature
curvesinhydro-climatological
modeling 200 SalimHeddam
3.8 Applicationoftheinformationtheory
1. Introduction 239
inhydro-climatologicalmodeling 200
2. Materialsandmethods 240
4. Agendaforfuturestudies 200
2.1 Studyareaandavailabledata 240
References 201
2.2 Performancemetricsofthe
models 241
13. Outlier robust extreme learning
2.3 Adaptivenetworkbasedfuzzy
machine: Predicting river water
inferencesystem(ANFIS) 243
temperature in the absence of air 2.4 Randomvectorfunctionallinkneural
temperature networks(RVFL) 244
2.5 Voting-basedextremelearning
SalimHeddam
machines(VELM) 244
1. Introduction 205 3. Resultsanddiscussion 245
2. Studyareaanddata 206 4. Conclusions 251
3. Materialsandmethods 207 References 251
3.1 Outlierrobustextremelearning
machine(ORELM) 209 16. Prediction of reference crop
3.2 Performanceassessmentofthe evapotranspiration: Empirical and
models 210 machine learning approaches
4. Resultsanddiscussion 211
T.R.Jayashree,N.V.SubbaReddy,
5. Conclusions 216
U.DineshAcharya,andSaeidEslamian
References 220
1. Introduction 253
14. Parametric and nonparametric 2. Anoverviewofvariousempirical
methods for analyzing the trend of methodsforreference
extreme events evapotranspirationestimation 254
2.1 FAO-56Hargreavesmethod 254
MohammadJamaliandSaeidEslamian
2.2 FAO-24Blaney-Criddle
1. Introduction 223 (temperature-based)method 254
2. Trendcalculationmethods 224 2.3 FAO-24radiationmethod 254
2.1 Mann-Kendalltest(MK) 224 2.4 Priestley-Taylormethod 254
2.2 Mann-Kendalltestwithtrend-free 2.5 Turcradiation-basedmethod 255
prewhitening(MK2) 225 2.6 FAO-56panevaporationmethod 255
2.3 ModifiedMann-Kendalltests 2.7 FAO-24Penmancombination
(MK3) 225 type 255
2.4 Mann-KendalltestconsideringLTP 2.8 FAO-56Penman-Monteith
(MK4) 226 method 255
2.5 Spearman’srhotest 227 3. EstimationofET0usingempirical
2.6 Linearregressionmethod 227 models 256
2.7 Quantileregressionmethod 228 4. Machinelearningtechniquesusedfor
estimationofevapotranspiration 256