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Prediction of stream-flow by using Artificial Neural Network model with special reference to pre-processing of raw data. The model is based on daily stream-flow records of many years.
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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