ashu reach08

8/3/2019 Ashu REACH08

1/42

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


2/42

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


3/42

Hydrologic CycleHydr

ologic Cycle

(Source: http://saturn.geog.umb.edu/wdripps/Hydrology/Hydrology%20Fall%202004/precipitation.ppt)


4/42

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


5/42

Indian ScenarioIndian Scenario

Water availability in IndiaWater availability in India

is highly uneven withis highly uneven withrespect to bothrespect to both spacespace andand

timetime


6/42



7/42



8/42

Kanpur ScenarioKanpur Scenario

Dainik Jagran: 2 May 2007Dainik Jagran: 2 May 2007


9/42


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


10/42

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


11/42

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


12/42

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.


13/42

A Runoff HydrographA Runoff Hydrograph


14/42

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


15/42

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


16/42

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


17/42

Integrated R-R ModelsIntegrated R-R Models

Innovative Integrated approachesInnovative Integrated approaches

Conceptual + ANNConceptual + ANN

Decomposition + AggregationDecomposition + Aggregation

Time Series + ANNTime Series + ANN


18/42

IntegratedIntegrated

Rainfall-RunoffRainfall-Runoff

Model-1Model-1


19/42


Conceptual ModelConceptual Model


20/42


ANN/Black Box ModelANN/Black Box Model


21/42


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.


22/42



23/42

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


24/42

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.


25/42

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


26/42

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)


27/42

ANN Model Results (Summer)ANN Model Results (Summer)


28/42

Integrated Model-1 Results (Summer)Integrated Model-1 Results (Summer)


29/42



Model-2Model-2


30/42

Decomposition + AggregationDecomposition + Aggregation

Figure 1: Decomposition of a Flow Hydrograph

R1

R2

F1

F2

F3

Time

Flow


31/42

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)


32/42

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


33/42

Scatter Plot from Model-VScatter Plot from Model-V


34/42

Results-Model-V: Drought Year 1988Results-Model-V: Drought Year 1988


35/42



Model-3Model-3


36/42


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.


37/42


ANN1 Raw DataANN1 Raw Data

ANN2 Detrended DataANN2 Detrended Data ANN3 Detrended andANN3 Detrended andDeseasonalized DataDeseasonalized Data


38/42


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


39/42


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


40/42


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.


41/42


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.


42/42

Thank YouThank You

ashu reach08

Documents