1
Dr. Dimitri P. SolomatineProfessor of Hydroinformatics
“Programme with a difference”
HydroinformaticsModelling and information systems for integrated water management
D.P. Solomatine. Introduction to Hydroinformatics 2
Water
Water is an important constituent of the meteorological cyclePressure on water resourcesConsequences of climate changeNeed for conservation and sustainability of potable water resourcesNeed for better information and predictions - to understand and to manage water
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Managing water resources
water-related decisions is difficult to test on large-scale experiments, hence importance of computer-based modelling and forecastingcontrol of water resources must be based on optimal solutionsmanagement of water needs a lot of data and informationfrom various sourcesneed for Computer-based modelling, Information and Communication Technology (ICT) tools
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Hydroinformatics
modelling, information and communication technology, computer sciences
applied to problems of aquatic environment
with the purpose ofproper management
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Modelling
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Modelling
Computer-based model isa simplified description of realityan encapsulation of knowledge about a particular physical or social process in electronic form
Hydroinformatics integratesdata,
models, people
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Generations of modelling: a bit of history…
1. Computers used as calculation devices of analytical expressions – 1950s
no friendly interfaces
2. Mainframe computers used to solve differential equations numerically – 1960-70s
custom-built models
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Generations of modelling
3. Production of modelling packages/systems for wide class of problems – 1980-90s
developed user interfaces“production lines” of models (modelling shells)refinement of solution methodspromotion of standardsmore clients more profits more enhancements
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Generations of modelling
4. “Mass” production of modelling systems for PCs – 1990sprovision of products, not projectsaccess by non-specialistshigh standards of robustness and consistencyease of use via the sophisticated user interfacesinvolvement of software engineering and IT specialistsintegration with supporting tools and facilities
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Generations of modelling. The fifth generation as we understand it now
5. Hydroinformatics systems – 1990s-Modelling as a central interface between
domain data (monitoring stations, weather radars, remote sensing)and human decision maker
Domain knowledge encapsulatorsIntegration of various types of modelsAlternative, non-process-based modelling paradigms (data-driven modelling)Potential of integration with artificial intelligence
5th generation modelling = CH AND AI(but this has not happen yet)
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Hydroinformatics system: typical architecture
Data, information, knowledgePhysically-based
models
Data-drivenmodels
Decision support systems for management
Real world
Observations, Communication
User interface
Fact engines
Judgement engines
Knowledge-basesystems
Knowledge inference engines
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Position of Hydroinformatics
Water EngineeringHydrology
Environment
Management
ICTComputingAI
Modelling
Systemssciences,
Optimisation
Instrumentation
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Encapsulation of knowledge related to water
Tacit (implicit) knowledge embedded within a personWords, texts, images
printedstored in electronic media
Mathematical modelsformulasalgorithmsalgorithms encapsulated in computer programs (software)
Integrated systems encapsulating all of above –Hydroinformatics systems
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Hydroinformatics system: flow of information
Earth observation, monitoring
Numerical Weather Prediction Models
Data modelling, integration with hydrologic and hydraulic models
Access to modellingresults
Data Models Knowledge Decisions
Decision support
Map of flood probability
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Hydroinformatics systems in flood management
Data Models Knowledge Decisions
Map of flood probability
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Models are indispensable in dealing with floods
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Sobek modelling software (Delft Hydraulics) in designing urban master plan
visulaizing potential floodings:
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Hydroinformatics systems for flood warning: MIKE FloodWatch
MIKE Flood Watch (Danish Hydraulic Institute), a decision support system for real-time flood forecasting:
advanced time series data base MIKE 11, for hydrodynamic modelingMIKE 11 FF, real-time forecasting system, ArcView, Geographical Information System (GIS)
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Hydroinformatics systems for flood warning: MIKE FloodWatch
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Flood warning systems: Piemonte case study
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Architecture of a hydroinformatics system for flood warning in Athens, Greece (IHE project)
DSSMAIN
INTERFACE
Connector toTelemetricdata storage
Connector toHydrologic/Hydraulic
Model
CommunicationModule
(Email, FTP)
GISModule Database
Module
DecisionTree
Module
Post floodevaluation anddocumentation
module
Trigger
MeteorologicalModels
Hydrologic/HydraulicModels
TelemetricData
Internet, telephone lines
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Flood warning system interface, developed by Hydroinformatics participant
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Warragamba Dam, Australia
Warragamba Dam - 65 km west of Sydney in the Burragorang Valley
provides the major water supply for SydneyWarragamba River flows through a 300-600 m wide gorge, about 100 m deep before opening out into a large valley. This allows a relatively short and high dam to impound a vast quantity of water.
A dam break of the WarragambaDam would be a major disaster. SOBEK (Delft Hydraulics) software was used for simulation
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Warragamba Dam, AustraliaSimulation of the dam break with SOBEK, Delft
HydraulicsThe animation shows the simulation results. They may be used for disaster management, evacuation planning, flood damage assessment, urban planning
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Use of 2-dimensional modellingin Jamuna bridge project, Bangladesh
construction of a 4 km bridge and several river training works for guiding the flow to pass under the bridgeDanish Hydraulic Institute (DHI) carried out a study of the river morphology to enable the contractor to take the preventive or remedial measures (MIKE 21 modelling system was used)
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Eutrophication modelling of a tidal lagoon in Bali, Indonesia
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Example: Eutrophication modelling of a tidal lagoon in Bali, Indonesia
Turtle Island is an enlargement of the existing SeranganIsland at the entrance to Benoa Bay; three artificial lagoons are planned for leisure crafts and beachesat the order of Penta Ocean Construction Co. LTD, DHI performed modelling for water quality in terms of rooted benthic vegetation, macroalgae, concentrations of phytoplankton, nutrients and oxygen MIKE 21 EU (eutrophication) model was used; various scenarios were analysed. The concentration field of Chlorophyll is shown; the area is strongly influenced by tidal flushing and dries out during each tidal cycle
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Example of Integrated Modelling:a Case Study for Sonso Lake, Colombia
(Masters study of Mr. Carlos Velez performed together with the experts from Delft Hydraulics)
Problem: 70% of the surface area of this shallow lake is covered by an invasive macrophite Water HyacinthCauses:
Nutrients pollution from agricultural use of landLack of sustainable management of the lake
Methodology:Integrated modelling of Water Hyacinth growth (ecological – water flow model)
Results: the developed model makes it possible to analyse alternatives to manage the Water Hyacinth infestation
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Ecosystem Integrated Model: a Case Study for Sonso Lake, Colombia
Ecosystem
Hydrodynamic
Water Quality
Sobek Rural1D2D
Sobek RuralDELWAQ
Water Hyacinth Model (coded using SOBEK RURAL Open
Process Library)
Water HyacinthNH4
NO3
Norg Porg
PO4
Organic Matter Settled
9 10
13
12
Solar Radiation
11
14
1615
7
8
6
5Water Volume
2 3
1
4VelocityWater Depth
Flow
SEDIMENT
WATER SURFACE56
9
1. Input / Output2. Rainfall3. Evapotranspiration4. Advection/Dispersion
5. Input / Output6. Input / Output7. Sedimentation8. Resuspension
13. Photosynthesis14. Respiration15. Mortality16. Losses
9. Resuspension10. Hydrolysis11. Oxidation 12. Uptake/Growth
PROCESSES
Developed by Carlos Velez. Supervisors: A. Mynett, L. Postma, A. v. Griensven
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Importance of modelling
reduces complexityencapsulates knowledgeprovides a laboratory experiencerefines tacit knowledgeenables reasoned intervention by humansfacilitates communicationassists education and training
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Decision making process
Modelling
Archiving and analysis
Decision making
Acquisition of
information
Feedback and control
Application and
Evaluation
“Side effect”: Knowledge discovery
Objectives
World of water
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Beyond physically-based models:
Computational intelligenceOptimisation and integrationInternet-based computing
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Case study SIEVE: flood management problem
mountaneous catchment in Southern Europearea of 822 sq. km
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SIEVE: visualization of data
variables for building a decision tree model were selected on the basis of cross-correlation analysis and average mutual information:
inputs: rainfalls REt, REt-1, REt-2, REt-3, flows Qt, Qt-1
outputs: flows Qt+1 or Qt+3
FLOW1: effective rainfall and discharge data
0
100
200
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400
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600
700
800
0 500 1000 1500 2000 2500
Time [hrs]
Discharge [m3/s]
0
2
4
6
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18
20
Discharge [m3/s]Eff.rainfall [mm]
Effective rainfall [mm]
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Process (physically-based) modelling of flow:river modelling context
Available data:rainfalls Rt lateral inflows QLcatchment and river physical properties (soil, geometry, roughnesses)initial and boundary conditions for flows Q 0(x,t)
Inputs: QL(x,t), Qup(t), Q 0(x,t) , system propertiesOutput: flow Q (x, t)Model:
Q (x, t)=F (QL(x,t), Qup(t), Q 0(x,t) , system properties)Questions:
are the physical properties of the catchment known?is F good enough ?
QQtt
QQttupup
RRtt
0=+thb
xQ
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2
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D.P. Solomatine. Introduction to Hydroinformatics 36
Using data-driven methods in rainfall-runoff modelling
Available data:rainfalls Rt
runoffs (flows) Qt
Inputs: lagged rainfalls Rt Rt-1 Rt-2 …Output to predict: Qt+T
Model: Qt+T = F (Rt Rt-1 … Qt Qt-1 …Qtup Qt-1
up …)(routing)
Questions: how to find the appropriate lags? (lags embody the physical properties of the catchment)how to build non-linear regression function F ?
QQtt
QQttupup
RRtt
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Artificial neural network: a universal function approximator
u F a a x
j= ,..., N
j oj ij ii
N
hid
inp
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1
1
y F b b u
k= ,..., N
k ok jk ji
N
out
hid
= +⎛
⎝⎜⎜
⎞
⎠⎟⎟
=∑
1
1
There are (Ninp+1)Nhid + (Nhid+1)Nout weights to be identified
Hidden layer
a ij
Inputs
x 1x 2 x 3
x nOutputs
y1y2y3
ym
u 1x
u s
b jkweights weights
x
f(x)1
0 Binary Sigmoid : F(x) = 1/ (1 + e-x)
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Neural network tool in predicting flows
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ANN verification RMSE=11.353NRMSE=0.234COE=0.9452
MT verificationRMSE=12.548NRMSE=0.258COE=0.9331
SIEVE: Predicting Q(t+3) three hours ahead
Prediction of Qt+3 : Verification performance
0
50
100
150
200
250
300
350
0 20 40 60 80 100 120 140 160 180t [hrs]
Q [m
3 /s]
ObservedModelled (ANN)Modelled (MT)
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MULTI-OBJECTIVE OPTIMIZATION
Finding variables’ values that bring the value of the “objective function” to a minimumIn water resources many problems require solving an optimization problem
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Floo
d D
amag
e
Costs
Urban drainage system rehabilitation:use of multi-objective optimization
rehabilitation: changing pipes, creating additional storagesoptimization by multi-objective genetic algorithm: find a compromise btw. min. cost and min. damage due to flooding
Wastewater System Pipe Network Model (MOUSE)
Optimization Procedure (GLOBE, NSGA-II)
Data Processor Data Processor
Compromise optimal solutions
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Using Random Search Global Optimization methods in water distribution network rehabilitation
Optimization of Networks with Predetermined Topology
Number and length of pipesDemand at every node (including pressure)Other hydraulic elementsCommercially available pipe sizes
Decision VariablesDiameter of each pipe in the network
Result: with optimal pipe diameters costs are 20% lower
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Computational intelligence in generating inundation maps, Yellow River Commission
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UNCERTAINTY
Uncertainties associated with climate change are very highDifferent IPCC scenarios lead to very different results of water modelsAny study exploring the impacts of CC needs powerful tools for analysing and predicting uncertainty
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Role of uncertaintyin water management
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60
70
80
1 11 21 31 41 51Ti me
Disc
harg
e
One est i mat eUpper boundLower bound
Alarm level
Prediction interval (uncertainty)
Deterministic forecast
Fore
cast
ed ri
ver d
isch
arge
So, issue a flood alarm or not?..
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Monte Carlo simulation of parametric uncertaintyy = M(x, s, θ) + εs + εθ + εx + εy
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750 775 800 825 8500
1000
2000
3000
4000
Time(day)
Obs
erve
d flo
w (m
3 /s)
90% prediction limitsObserved flow
Rainfall-Discharge plot
0
1000
2000
3000
4000
5000
6000
Jan-
88
May
-88
Sep-
88
Feb-
89
Jun-
89O
ct-8
9
Mar
-90
Jul-9
0N
ov-9
0
Apr
-91
Aug
-91
Jan-
92
May
-92
Sep-
92Fe
b-93
Jun-
93
Oct
-93
Mar
-94
Jul-9
4
Dec
-94
Apr
-95
Aug
-95
Time [days]
Run
off [
Cum
ec]
0
50
100
150
200
250
300
350
400
Prec
ipita
tion
[mm
]
Runoff [Cumec] Precipitation [mm]
Estimated prediction bounds: verification (Bagmati river basin, Nepal)
LZ
UZ
SM
RF
R
PERC
EA
Q=Q0+Q1Q1
Transformfunction
SP
Q0
SF
CFLUX
IN
SF – SnowRF – RainEA – EvapotranspirationSP – Snow coverIN – InfiltrationR – RechargeSM – Soil moistureCFLUX – Capillary transportUZ – Storage in upper reservoirPERC – PercolationLZ – Storage in lower reservoirQo – Fast runoff componentQ1 – Slow runoff componentQ – Total runoff
LZ
UZ
SM
RFRF
RR
PERCPERC
EAEA
Q=Q0+Q1Q1Q1
Transformfunction
SP
Q0Q0
SFSF
CFLUXCFLUX
ININ
SF – SnowRF – RainEA – EvapotranspirationSP – Snow coverIN – InfiltrationR – RechargeSM – Soil moistureCFLUX – Capillary transportUZ – Storage in upper reservoirPERC – PercolationLZ – Storage in lower reservoirQo – Fast runoff componentQ1 – Slow runoff componentQ – Total runoff
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Internet-based computing and knowledge management
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Access to knowledge via Internet
Support for Communities of Practice
Internet
Database andtransaction server
Web Application Server
Web-based platform for engineering services
Client's PC Mobile client's PC
Experts
Databases
Knowledgebases
Forum(bulleting board)
Authorization and support for E-commerce
MessagingComputer
conferencing
Documentbases
Access to data-,knowledge- anddocument base
Distance learning
Document base with intelligent searchCase studiesProjects descriptionsExpertise profiles
Fun
ctio
ns
Users Users
Software(modellingsystems)
Knowledgemaps
Experts' PCs
Conference tools
Intranet
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Examples of systems developed by MSc and PhD participants
modelling across Internet (Delphi, PHP, Java)
water distribution system modelling (exercise, Delphi)free-surface modelling across Internet (individual study, Java)distrbuted database for the water authority of Egypt (individual study, thin-client DB, Delphi)flood warning system (PhD study, Delphi)
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Example: water distribution modelling
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Example: web-based decision support system
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Hydroinformatics specialisation at IHE
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Hydroinformatics programme at IHE
Fundamentals, hydraulic, hydrologic and environmental processes Fundamentals, hydraulic, hydrologic and environmental processes
PhysicallyPhysically--based based simulation modelling simulation modelling
and toolsand tools
Information systems, GIS, communications, InternetInformation systems, GIS, communications, Internet
DataData--driven modelling driven modelling and computational and computational intelligence toolsintelligence tools
Integration of technologies, project managementIntegration of technologies, project management
Elective advanced topics
Systems analysis, Systems analysis, decision support, decision support,
optimizationoptimization
•• ArcGISArcGIS•• AccessAccess
•• SOBEKSOBEK•• RIBASIMRIBASIM•• Delft 3DDelft 3D•• SWATSWAT•• EPANETEPANET•• MOUSEMOUSE•• AquariusAquarius
•• MIKE 11MIKE 11•• HECHEC--RASRAS•• MIKE 21MIKE 21•• MIKE SHEMIKE SHE•• RIBASIMRIBASIM•• WEST++WEST++•• MODFLOWMODFLOW
•• LINGOLINGO•• GLOBEGLOBE•• BSCW BSCW •• AquaVoiceAquaVoice
•• NeuroSolutionsNeuroSolutions•• NeuralMachineNeuralMachine•• AFUZAFUZ•• WEKAWEKA
•• MatlabMatlab•• DelphiDelphiToolsTools
•• JAVA JAVA •• UltraDevUltraDev
with applications to:with applications to:-- River basin managementRiver basin management-- Flood managementFlood management-- Urban systemsUrban systems-- Coastal systemsCoastal systems-- Groundwater and Groundwater and catchment hydrologycatchment hydrology
-- Environmental systemsEnvironmental systems(options)(options)
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Hydroinformatics Study Modules
Introduction to Water science and EngineeringApplied HydraulicsGeo-information systemsComputational Hydraulics and Information ManagementModelling theory and applicationsComputational Intelligence and Control SystemsRiver Basin ModellingEnvironmental systems modellingFieldtrip to Florida, USASelective modelling subjects:
Flood risk managementUrban water systems modellingEnvironmental systems modelling
Hydroinformatics for Decision SupportGroupworkResearch proposal drafting and Special TopicsMSc research
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Examples of MSc topics
Flood modelling and forecasting for Awash river basin in EthiopiaHarmful Algal Bloom prediction, case study of Western Xiamen Bay, ChinaApplication of Neural Networks to rainfall-runoff modelling in the upper reach of the Huai river basin, ChinaHeihe River Basin Water Resources Decision Support SystemDecision Support System for Irrigation Management in VietnamHydroinformatics for real time water quality management and operation of distribution networks, case study Villavicencio, Colombia1D-2D Coupling Urban Flooding Model using radar data in BangkokUrban Flood Warning System with wireless technology, case Study of Dhaka City, BangladeshWater distribution modelling with intermittent supply: sensitivity analysis and performance evaluation for Bani-Suhila City, Palestine
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using neural network model to replicate the behaviour of a complex hydrodynamic modelusing fuzzy rule-based system to restore the missing rainfall datausing neural networks and fuzzy systems for controlling water levels in polder areasusing data mining and chaos theory to predict the surge in the coastal areas of The Netherlands for ship guidance
Note: around 50% of Hydroinformatics participants continue on with PhD programmes
Examples of MSc topics
Short courses (typically 3 weeks)www.ihe.nl/Education/Short-courses
River Basin ModellingIntroduction to River Flood ModellingFlood Modelling for Management (Online course)Flood Risk ManagementUrban Flood Modelling and Disaster Risk ManagementUrban Water Systems ModellingEnvironmental Systems Modelling (2 weeks)Decision Support Systems in River Basin ManagementNew data sources to support flood modelling (1 week)
Links:http://www.ihe.nl/Education/Short-courses/Regular-short-courses/Urban-Flood-Modelling-and-Disaster-Risk-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Urban-Water-Systems-Modellinghttp://www.ihe.nl/Education/Short-courses/Online-courses/Flood-Modelling-for-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Flood-Risk-Managementhttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Introduction-to-River-Flood-Modellinghttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/Environmental-Systems-Modellinghttp://www.ihe.nl/Education/Short-courses/Regular-short-courses/River-Basin-Modellinghttp://www.ihe.nl/Education/Short-courses/Online-courses/Decision-Support-Systems-in-River-Basin-Managementhttp://www.unesco-ihe.org/Education/Short-courses/Regular-short-courses/New-data-sources-to-support-flood-modelling
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Conclusion
Hydroinformatics is a unifying approach to water modelling and managementHydroinformatics is technology driven, so it uses the most modern technologies and research resultsSpecialists in hydroinformatics play an integrating role linking various specialists and managersHydroinformatics specialists:
this is what the water sector needs
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What Hydroinformatics alumni say...
the course opened the new horizons in my professional life