Final End Sem

8/2/2019 Final End Sem

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Project Guide : Shri S.K. Shrivastava

Dr. Saibal Chatterjee

Presented By:

Ram Prakash

(DE/08/AE/04)

Subu Tado (DE/08/AE/06)

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ABBREVIATIONS

ABBREVIATIONS NAMES

WT Wavelet Transform

DWT Discrete Wavelet Transform

DB3 Daubechies

SYM3 Symlets

COIF1 Coiflets

FT Fourier Transform

RMSE Root Mean Square Error

A4 4th Level Approximate Value

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INTRODUCTION OBJECTIVE

REVIEW OF

LITERATURE

STUDY AREAMATERIALS ANDMETHODS

RESULT ANDDISCUSSION

CONCLUSION END OF SLIDES


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INTRODUCTION Climate change is a change in the statistical distribution of weather over periods

of time that range from decades to millions of years.

It can be a change in the average weather or a change in the distribution of

weather events around an average (for example, greater or fewer extreme weather

events).

Climate change may be limited to a specific region, or may occur across the

whole Earth.

Climate change is already happening and represents one of the greatest

environmental, social and economic threats facing the planet.

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate

Change (IPCC) shows that the Earth's average surface temperature has risen by

0.76 C since 1850.

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The debate on climate variability and climate change relies heavily on the

detection of trends (or lack thereof) in instrumental records of hydro climatic

variables such as air temperature, precipitation and stream flow.

Long-term trends in climate are not easy to detect because of the normally wide

swings from day to day, and the periodic occurrence of record extremes.

Thus, our memories of past weather conditions are not a reliable indicator of

whether climate is getting progressively better or worse.

This climate change is also affecting Arunachal Pradesh because of the rapidgrowth in Industrialization and growing population.

This study is taken up district wise to identify trends in two climatic parameters

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Wavelet Transform is an analysis tool well suited to the study of multi-scale,

non-stationary processes occurring over finite spatial and temporal domains.

By decomposing a time series into timefrequency space, one is able to

determine both the dominant modes of variability and how those modes

vary in time.

The wavelet transform allows one, on the one hand, to provide temporal and

frequential parameter residual signals, and on the other hand, to determine

and quantify systematic signals such as periodicity and tendency.

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AUTHOR

(Year)JOURNAL,Vol. (Issue),

Page no.

CONTENT

Bradshawet al. (2003)

Journal ofHydrology,88(6): 40-55

They introduced methodology of utilizing wavelet analysis for thepurpose of identifying and isolating inferred climatic componentsof the hydrologic record as methods which quantify and relateinformation of temporal and spatial patterns across scales .Theychoose filter dimension for signal decomposition and found trendof stream to be correlated with SOI at the same frequency.

Jimnez et.al. (2005)

Journal ofHydrology,

324 (1-4): 24-39

Rainfall, temperature and outflow of a karst spring obtained fromgauging stations in the south of the Iberian peninsula wereanalysed by means of spectral and correlation analyses andcontinuous wavelet analyses. Annual periodicity of the rainfall andtemperature distributions was constant over more than 100 years.And No long-term trends in the distribution of precipitation andtemperature were detected

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AUTHOR

(Year)JOURNAL,Vol.(Issue),Page no.

CONTENT

Bessissi etal. (2009)

Journal ofgeographical science,14(11): 230-244

They relates the residual time series analysis of DORIS station

coordinates using the wavelet transform. The wavelet transformallows one, on the one hand, to provide temporal and frequentialparameter residual signals, and on the other hand, to determineand quantify systematic signals such as periodicity and tendencyso based on this context they determined the systematic signalsby wavelet analysis of time series of DORIS station coordinates,and on the other hand, in applying the denoising signal tothe wavelet packet. The goal was to detect the contribution ofwavelet analysis on DORIS data.

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AUTHOR

(Year)JOURNAL,Vol. (Issue),

Page no.

CONTENT

Ross et. al(2009)

Journal ofHydrology,377 (3-4):237-244

They identified the principal modes of variability in hydrologictime-series data for the Mississippi River for 19501975 andcompared them to climate fluctuations and anthropogenicchanges in the watershed. Continuous wavelet analysis wasused here to investigate the temporal and frequency changes in

hydro-meteorological and suspended load series. TheirWavelet analysis of stream-flow and suspended-sedimentconcentration showed common spectral structures dominatedby intra-annual and annual fluctuations. The analysis alsoindicates a loss of energy in the suspended-sediment loadsignal (decrease in loading) which can be attributed to

anthropogenic changes. By parameterizing a sinusoidal modelbased on the observed spectral composition, they were able toremove the effect of this energy loss and estimate whatsuspended-sediment load would be without the anthropogenicchanges

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Study area: Stationslocated in various districts ofArunachal Pradesh

Data used:Monthly rainfall and temperature data from1991 to 2007 and 1991-2006 respectively.

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TAWANG PASIGHAT(E.SIANG)

BOMDILA ALONG(W.SIANG)

SEPPA(E. KAMENG) ZERO (L.SUBANSIRI)

YINGKIONG(U.SIANG) ITANAGAR (PAPUMPARE)

KHONSA(TIRAP) CHANGLANG

TEZU(LOHIT) ROING(L. DEBANG VALLEY)

DOIMUKH(PAPUMPARE)

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MATERIALS AND METHODS

MATLAB is a high-performance language for technical computing.

It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed infamiliar mathematical notation.

Typical uses include:

Math and computation

Algorithm development

Data acquisition Modeling, simulation, and prototyping

Data analysis, exploration, and visualization

Scientific and engineering graphics

Application development, including graphical user interface buildingNEXT


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What is a WAVELET ?

As the name suggest a wavelet is a small wave . A small wave grows anddecays essentially in a limited time period.

Everywhere around us are signals that can be analyzed. For example, thereare seismic tremors, human speech, engine vibrations, medical images,financial data, music, and many other types of signals. Wavelet analysis is anew and promising set of tools and techniques for analyzing these signals.

Wavelet Toolbox is a collection of functions built on the MATLAB TechnicalComputing Environment. It provides tools for the analysis and synthesis ofsignals and images, and tools for statistical applications, using wavelets and

wavelet packets within the framework of MATLAB.

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Types of waveletsSample Wavelet Function Syntax

Haar wfilters('haar')

Daubechies wfilters('db4')

Symlets wfilters('sym3')

Coiflets wfilters('coif1')

Biorthogonal wfilters('bior3.1')

Reverse Biorthogonal wfilters('rbio3.1')

Discrete Meyer wfilters('dmey')

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Fourier Analysis

Signal analysts already have at their disposal an impressive arsenal of tools.Perhaps the most well known of these is Fourier analysis, which breaks downa signal into constituent sinusoids of different frequencies. Another way tothink of Fourier analysis is as a mathematical technique for transforming our

view of the signal from time-based to frequency-based.

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For many signals, Fourier analysis is extremely useful because the signal'sfrequency content is of great importance.

So why do we need other techniques, like wavelet analysis?

Fourier analysis has a serious drawback. In transforming to the frequencydomain, time information is lost. When looking at a Fourier transform of asignal, it is impossible to tell when a particular event took place.

If the signal properties do not change much over time -- that is, if it is what

is called a stationary signal -- this drawback isn't very important. However,most interesting signals contain numerous nonstationary or transitorycharacteristics: drift, trends, abrupt changes, and beginnings and ends ofevents. These characteristics are often the most important part of the signal,and Fourier analysis is not suited to detecting them.

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Wavelet Analysis

It is a windowing technique with variable-sized regions. Wavelet analysisallows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequencyinformation.

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RESULT AND DISCUSSION


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Sample Plot of Itanagar rainfall data (yearly)

using Original and 4th level approximate


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Final End Sem

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