Stream flow Forecasting by

ANN Modeling with Preprocessing Techniques

for Time Series Data

PROPOSAL FOR Ph.D. Thesis at N.I.T.K. ,Surathkal,

By Aniruddha Banhatti,Part Time Ph.D. Student,

Registration Number: AM08P05

Importance of Stream Flow Forecasting

Hydrologic StructuresIrrigationFlood ControlHydrologic PlanningFlood Relief

Nature of Stream flow Data

Time Series DataShow following characteristics:TrendSeasonalityCyclic NatureIrregular Fluctuations – Outliers

and Noise

Basics of Artificial Neural NetworksANN is a massively parallel information

processing system.It resembles biological neural networks of

human brain.Processing occurs at large number of

single elements called Nodes or Neurons.Signals are passed between neurons using

LinksEach link has a weight associated with it.Each link applies a nonlinear

transformation called an Activation Function to its net input to determine its output signal.

Schematic representation of ANN

connectionsneurons

INPUTLAYER

HIDDENLAYER

OUTPUTLAYER

neurons

Each connection is associated with a particular weight between 0 and 1

6

Structure of ANN

7

wk1

x1

wk2

x2

wk

m

xm

...

... S

Bias bk

j(.) vk

Input Signal

Synaptic

Weights

Summing Junction

Activation Function

Output yk

xwv j

m

jkjk å

=

=0

)( vy kkj=

Algorithms for ANNs

Various algorithms can be used such as :Back Propagation AlgorithmConjugate Gradient AlgorithmsRadial Basis FunctionCascade Correlation AlgorithmRecurrent ANNsSelf Organizing feature Maps

Back Propagation Algorithm

is found to be best suited for

Time Series Data

and most of the

Hydrologic Modeling Problems.

Schematic of BP Algorithm

Use of ANNs in HydrologyRainfall – Runoff ModelingModeling StreamflowsWater quality ModelingGroundwater StudiesEstimating PrecipitationOther Uses

Characteristics of Hydrologic Time SeriesNon-stationaryAuto correlatedCross relatedChronological dependance These characteristics manifest as

TrendSeasonalityCyclic natureIrregular fluctuations

Data Pre-processing Techniques

Raw Values – for control groupNormalization – De-trendingLogarithmic transformLogarithmic plus First DifferenceLogarithmic plus Second

Difference

Problem Identification

An investigation is proposed to use different data pre- processing techniques for multistep lead time forecasting using different ANN architectures to develop best model by evaluating various performance criteria and make the data more adaptable than the raw data for ANN modeling, so as to forecast streamflow more realistically and also to improve the performance of the ANN model.

Study AreaGauging station at Pandu along

Brahmaputra River at Guwahati is taken as the study area.

Daily stream flow data for ten year period

1st January 1990 to 31st December 1999 will be used for the present study.

Map of Study Area

Map of Study Area

Plan Of Research Work

Plotting and Visual Observation of Data

Identification of Features Specific to the Data

Applying Pre-Processing Techniques

Preparation of Data Sets

Data Sets No. of lagged terms

Dataset Lagged terms Data Matrix

Input Output

1 Raw values Log Log + first difference

yt = xt

 yt = log xt

 yt = log xt + first diff.

y1

y2

y3

…..yt

y2

y3

y4

…..yt-1

2 Raw values Log Log + first difference

yt = xt

 yt = log xt

 yt = log xt + first diff.

y1, y2

y2, y3

.…. yt-1, yt

y3

y4

.…. 

yt-2

3 Raw values Log Log + first difference

yt = xt

 yt = log xt

 yt = log xt + first diff.

y1, y2,

y3

y2, y3,

y4

…..…..yt-2, yt-1,

yt

y4

y5

…..….. yt-3

Architectures of ANN

According to Activating FunctionAccording to Number of Neurons According to Algorithm Used

Different Activating Functions

Architectures Used

According to Activation FunctionSigmoidTansigLogsigAccording to number of input neurons 1 to 10 Input Neurons will be used

Number of TrialsNine DatasetsThree ArchitecturesTen Input Methods

Thus there will be 9 X 3 X 10 = 270 Model Trials

Data Partitioning

Analysis

Evaluation and Plotting of 270 Trials

Evaluation Criteria RMSEMAPER-Squared

Schedule

Month May2011

Jun2011

Jul2011

Aug2011

Sep2011

Oct2011

Nov2011

Dec2011

Making Datasets Preliminary Trials

               

Progress Monitoring with Guide

               

Completion of All TrialsPlotting of Results

               

Preparation of Thesis