ashu reach08
TRANSCRIPT
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Integrated ApproachesIntegrated Approaches
for Runoff Forecastingfor Runoff Forecasting
Ashu JainAshu Jain
Department of Civil EngineeringDepartment of Civil Engineering
Indian Institute of Technology KanpurIndian Institute of Technology Kanpur
Kanpur-UP, INDIAKanpur-UP, INDIA
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OutlineOutline
Hydrologic CycleHydrologic Cycle
Global Water FactsGlobal Water Facts
Indian Scenario & Possible SolutionsIndian Scenario & Possible Solutions
Rainfall-Runoff ModellingRainfall-Runoff Modelling
Existing ApproachesExisting Approaches Integrated Approaches (3)Integrated Approaches (3)
ConclusionsConclusions
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Hydrologic CycleHydr
ologic Cycle
(Source: http://saturn.geog.umb.edu/wdripps/Hydrology/Hydrology%20Fall%202004/precipitation.ppt)
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Global Water FactsGlobal Water Facts
Total water 1386 Million Kilometer^3Total water 1386 Million Kilometer^3
97% in oceans & 1% on land is saline97% in oceans & 1% on land is saline
=> only 35 MKm3 on land is fresh=> only 35 MKm3 on land is fresh Of which 25 MKm3 is solidOf which 25 MKm3 is solid
Only 10 MKm3 is fresh liquid waterOnly 10 MKm3 is fresh liquid water
Availability is CONSTANTAvailability is CONSTANT Water Demands are INCREASING (2050!)Water Demands are INCREASING (2050!)
Optimal use of existing WR is neededOptimal use of existing WR is needed
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Indian ScenarioIndian Scenario
Water availability in IndiaWater availability in India
is highly uneven withis highly uneven withrespect to bothrespect to both spacespace andand
timetime
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Indian ScenarioIndian Scenario
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Indian ScenarioIndian Scenario
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Kanpur ScenarioKanpur Scenario
Dainik Jagran: 2 May 2007Dainik Jagran: 2 May 2007
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Indian ScenarioIndian Scenario
We depend on rainfall for meeting most of ourWe depend on rainfall for meeting most of ourwater requirementswater requirements
Most of the rainfall in majority of the countryMost of the rainfall in majority of the countryis concentrated in monsoon season (June-is concentrated in monsoon season (June-
September)September)
The uneven spatio-temporal distribution ofThe uneven spatio-temporal distribution ofwater and uncertain nature of rainfall patternswater and uncertain nature of rainfall patterns
call for innovative methods for watercall for innovative methods for water
utilization and forecastingutilization and forecasting
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Possible SolutionsPossible Solutions
Solutions of water problems in India lieSolutions of water problems in India lie
in its root causesin its root causes
Space => InterlinkingSpace => Interlinking
Time => Rainwater HarvestingTime => Rainwater Harvesting
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Possible SolutionsPossible Solutions
Other solutions includeOther solutions include
Optimal Management of Existing WROptimal Management of Existing WR
Runoff ForecastingRunoff Forecasting
Technological AdvancementsTechnological Advancements
Innovative Integrated ApproachesInnovative Integrated Approaches
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Runoff ConceptsRunoff Concepts
Amount of water at any timeAmount of water at any timemeasured in m3/sec at any locationmeasured in m3/sec at any location
in a river is called runoff.in a river is called runoff.
A graph showing runoff as aA graph showing runoff as a
function of time is called a runofffunction of time is called a runoffhydrograph.hydrograph.
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A Runoff HydrographA Runoff Hydrograph
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Runoff ConceptsRunoff Concepts
Runoff at any time depends onRunoff at any time depends on
Catchment characteristicsCatchment characteristics Storm characteristicsStorm characteristics
Climatic characteristicsClimatic characteristics
Geo-morphological characteristicsGeo-morphological characteristics
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Rainfall Runoff ModellingRainfall Runoff Modelling
Physical processes involved inPhysical processes involved inhydrologic cyclehydrologic cycle
Extremely complexExtremely complex
DynamicDynamic
Non-linearNon-linear FragmentedFragmented
Not clearly understoodNot clearly understood
Very difficult to modelVery difficult to model
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Rainfall Runoff ModelsRainfall Runoff Models
Conceptual or DeterministicConceptual or Deterministic
Systems Theoretic or Black Box TypeSystems Theoretic or Black Box Type
RegressionRegression
Time SeriesTime Series
ANNsANNsIntegratedIntegrated
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Integrated R-R ModelsIntegrated R-R Models
Innovative Integrated approachesInnovative Integrated approaches
Conceptual + ANNConceptual + ANN
Decomposition + AggregationDecomposition + Aggregation
Time Series + ANNTime Series + ANN
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IntegratedIntegrated
Rainfall-RunoffRainfall-Runoff
Model-1Model-1
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Conceptual + ANNConceptual + ANN
Conceptual ModelConceptual Model
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Conceptual + ANNConceptual + ANN
ANN/Black Box ModelANN/Black Box Model
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Conceptual + ANNConceptual + ANN
AnAn integrated/hybridintegrated/hybridmodel capable ofmodel capable of
exploiting the advantages ofexploiting the advantages of
conceptual and ANN techniques mayconceptual and ANN techniques may
be able to provide superiorbe able to provide superior
performance in runoff forecasting.performance in runoff forecasting.
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Conceptual + ANNConceptual + ANN
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Data Employed: Kentucky RiverData Employed: Kentucky River
Spatially aggregated daily rainfall (mm)Spatially aggregated daily rainfall (mm)
Average daily river flow (m3/s)Average daily river flow (m3/s)
Total length of data 26 yearsTotal length of data 26 years
First 13 years for training/calibrationFirst 13 years for training/calibration
Next 13 years for testing/validationNext 13 years for testing/validation
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Integrated R-R Model-1Integrated R-R Model-1
Conceptual:Conceptual: Base flow, infiltration, continuous soilBase flow, infiltration, continuous soilmoisture accounting, and the evapotranspirationmoisture accounting, and the evapotranspiration
processes are modelled using conceptual/ deterministicprocesses are modelled using conceptual/ deterministic
techniquestechniques
ANN:ANN: Complex, dynamic, and non-linear nature of theComplex, dynamic, and non-linear nature of theprocess of transformation of effective rainfalls intoprocess of transformation of effective rainfalls into
runoff in a watershed are modelled using ANNsrunoff in a watershed are modelled using ANNs Training:Training: ANN training is carried out using GA.ANN training is carried out using GA.
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Integrated R-R Model-1 ResultsIntegrated R-R Model-1 Results
Model A AR E R
D uring Training
C onceptual 23.57 0.9363A NN 54.45 0.9770
Integrated 21.58 0.9773
D uring T esting
C onceptual 24.68 0.9332A NN 66.78 0.9700
Integrated 23.09 0.9704
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Integrated R-R Model-1 ResultsIntegrated R-R Model-1 Results
Observed and Predicted Runoff in 1986 (Dry Year)Observed and Predicted Runoff in 1986 (Dry Year)
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ANN Model Results (Summer)ANN Model Results (Summer)
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Integrated Model-1 Results (Summer)Integrated Model-1 Results (Summer)
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IntegratedIntegrated
Rainfall-RunoffRainfall-Runoff
Model-2Model-2
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Decomposition + AggregationDecomposition + Aggregation
Figure 1: Decomposition of a Flow Hydrograph
R1
R2
F1
F2
F3
Time
Flow
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Integrated Model-2 DetailsIntegrated Model-2 DetailsTable 1: Details of Neural Network Models
________________________________________________________________________________________________
Model Portion Architecture Number Statistics Input Variables
of Data ( x , )
________________________________________________________________________________________________
Model-I 5-4-1 4747 (146.7, 238.8) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Model-II Rising 5-4-1 1783 (233.5, 330.3) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Falling 3-3-1 2963 (94.4, 135.7) P(t), Q(t-1), and Q(t-2)
Model-III Rising 5-4-1 1783 (233.5, 330.3) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Falling Recession 2963 (94.4, 135.7) Q(t-1), and Q(t-2)
Model-IV Rising 5-4-1 1783 (233.5, 330.3) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Falling-I 3-3-1 1189 (198.5, 164.4) P(t), Q(t-1), and Q(t-2)
Falling-II Recession 1774 (25.3, 20.1) Q(t-1), and Q(t-2)
Model-V Rising-I Inverse Recession 182 (8.2, 2.1) Q(t-1), and Q(t-2)
Rising-II 5-4-1 1601 (259.0, 339.4) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Falling-I 3-3-1 1189 (198.5, 164.4) P(t), Q(t-1), and Q(t-2)
Falling-II Recession 1774 (25.3, 20.1) Q(t-1), and Q(t-2)
SOM(3) High 5-4-1 693 (537.8, 384.2) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Medium 3-3-1 1061 (195.5, 127.6) P(t), Q(t-1), and Q(t-2)
Low 4-3-1 2993 (38.8, 50.9) P(t), P(t-1), Q(t-1), and Q(t-2)
SOM(4) High 5-4-1 409 (678.9, 426.3) P(t), P(t-1), P(t-2), Q(t-1), and Q(t-2)
Medium-I 4-3-1 704 (280.4, 157.4) P(t), P(t-1), Q(t-1), and Q(t-2)
Medium-II 3-3-1 1089 (136.7, 104.4) P(t), Q(t-1), and Q(t-2)
Low 3-3-1 2545 (28.4, 34.3) P(t), Q(t-1), and Q(t-2)
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Integrated Model-2 ResultsIntegrated Model-2 Results
Model AARE R AARE RD uring T rainin g D uring T esting
Model-I 54.97 0.9770 65.71 0.9700
Model-II 61.28 0.9764 72.28 0.9696
Model-III 31.66 0.9607 36.45 0.9571
Model-IV 31.90 0.9777 39.56 0.9684Model-V 23.85 0.9780 21.63 0.9678
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Scatter Plot from Model-VScatter Plot from Model-V
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Results-Model-V: Drought Year 1988Results-Model-V: Drought Year 1988
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IntegratedIntegrated
Rainfall-RunoffRainfall-Runoff
Model-3Model-3
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Time Series + ANNTime Series + ANN
Basic Steps in Time Series ModellingBasic Steps in Time Series Modelling DetrendingDetrending
DeseasonalizationDeseasonalization
Auto-correlationAuto-correlation
ANN modelling involves presenting rawANN modelling involves presenting rawdata as inputsdata as inputs
Time series steps can be carried out beforeTime series steps can be carried out beforepresenting data to ANN as inputs.presenting data to ANN as inputs.
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Time Series + ANNTime Series + ANN
ANN1 Raw DataANN1 Raw Data
ANN2 Detrended DataANN2 Detrended Data ANN3 Detrended andANN3 Detrended andDeseasonalized DataDeseasonalized Data
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Time Series + ANNTime Series + ANN
Data EmployedData Employed
Monthly runoff from Colorado River @Monthly runoff from Colorado River @
Lees Ferry, USA for 62 yearsLees Ferry, USA for 62 years
Past four months lagPast four months lag
50 Years for training50 Years for training
12 years for testing12 years for testing
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Time Series + ANNTime Series + ANN
Lag 2 Results Lag 4 Results
AARE R AARE R
Time Series 92.78 0.48 88.52 0.51
ANN1 44.51 0.62 44.01 0.68
ANN2 19.55 0.77 17.67 0.80
ANN3 12.55 0.86 9.62 0.89
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ConclusionsConclusions
Runoff forecasting is important for efficientRunoff forecasting is important for efficient
management of existing water resources.management of existing water resources.
An individual modelling technique providesAn individual modelling technique providesreasonable accuracy in runoff forecasting.reasonable accuracy in runoff forecasting.
Neural network based solutions can beNeural network based solutions can be
better than those obtained usingbetter than those obtained usingconventional methods.conventional methods.
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ConclusionsConclusions
Integrated modelling approaches have theIntegrated modelling approaches have the
potential for producing higher accuracy inpotential for producing higher accuracy in
runoff forecasts.runoff forecasts. Innovative integrated approaches dependentInnovative integrated approaches dependent
on the nature of problem are needed in orderon the nature of problem are needed in order
to develop hybrid forecast models capableto develop hybrid forecast models capableof exploiting the strengths of the availableof exploiting the strengths of the available
individual techniques.individual techniques.
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Thank YouThank You