For past few years there has been a continuous increase in the temperature of Tamil Nadu which led to increase in global warming and reduction in the rainfall days. Tamil Nadu receives rainfall two times a year – southwest monsoon, from July to September and Northeast monsoon, from October to December.

According to Tamil Nadu State Action Plan for Climatic Change (TNSAPCC), the trend in the past decade has shown the southwest monsoon has been decreasing from 48% to 24%, and the northeast monsoon has been increasing from 34% to 63%. Thus average rainfall in Tamil Nadu may not vary but the distribution changes. Due to the climatic change there has been increase in the intensity of carbon dioxide, soil erosion, rise in sea levels which led to cyclones, drastic increase in pests and diseases ²⁹.

Weather- which is always changing- is comprised of the following elements:

i) Air temperature - the degree of hotness or coldness of the air.

ii) Air pressure – the force of air above an area.

iii) Humidity– a measure of the amount of the water vapour in the air.

iv) Precipitation or rainfall - any form of water, either liquid or solid, (rain or snow), that falls from cloud and reaches the ground.

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. Human beings have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using scientific understanding of atmospheric processes to project how the atmosphere will change.

The state of Tamil Nadu is situated in the southern part of the country between north latitude between 8° 5' and 13° 35' east longitude between 76° 15' and 80° 20' 1 . The total area of the state is 1,30,058 sq km making it the eleventh largest state in the country (Figure 1). Tamil Nadu has 32 districts. Tamil Nadu is one of the important Coastal States having a coastline of 1076 Kms. To its east is the Bay of Bengal and at its southernmost tip is the town of Kanyakumari, which is the meeting point of the Arabian Sea, the Bay of Bengal, and the Indian Ocean.

Figure 1. Tamil Nadu – Study area map

Tamil Nadu can be divided broadly into two natural divisions (a) the coastal plains and (b) the hilly western areas. The average temperature in the hilly areas varies between a minimum of 21.27o C to a maximum of 35.86o C. The average temperature in the plains varies between a minimum of 10.46o C to 24.86o C.

Out of 15 agro-climatic zones of India as per Planning Commission of India, Tamil Nadu comes under two zones, namely Southern Plateau and Hills region and East Coast and Hills region . Under National Agricultural Research Project of ICAR, with in the broad classification of Planning Commission’s 15 agro-climatic zones, Tamil Nadu has been divided in to seven agro climatic sub-zones and those are

North western zone

North eastern zone,

Western zone,

Cauvery Delta zone,

Southern zone,

High rainfall zone and

High Altitude and

Hilly zone

Wavelet transform plays major role in analysing time series data efficiently. Event related potential determination can be easily done through this transformation more clearly. The spatial and temporal information will provide the real time data strategy in eagle’s eye. Advantages over the conventional time-frequency analysis lies in decompose the signal of any time-frequency multiresolution functions ³⁰. The admissibility condition is as follows

ψ (t) ϵ L² (R) satisfies certain admissibility conditions as

(1)

ψ(t) is called wavelet; (ω) is fourier transform of

ψ(t)_. The Continuous Wavelet Transform (CWT) is defined as the sum over all time of the signal multiplied by scaled, shifted versions of the wavelet function,

(2)

(3)

CWT uses discrete sampling of data in order to obtain the finer details depends on the scaling parameter of the wavelet. Finer resolution will obtain from increased computational time and more memory required calculating the coefficients ³¹. Multiresolution analysis (MRA) is formed on the basis of orthonormal supported wavelet. Its applications is rapidly developing in various fields of science since the representation of particular information gives more clear picture than other transformation algorithms like Fourier and Short Term Fourier Transform (STFT). Wavelet has been applied many areas of research including flood forecast analyses and river flow models ^{32, 33, 34, 35}. Figure 2 shows Wavelet coefficients of multiresolution scalogram 2004-2017 of winter season. This reveals that the premonsoon season prevails more rainfall randomly. It is not consistent. Thus the intensity is maximum at various portions of this spectrogram. Figure 3, Figure 4 and Figure 5 represents the multiresolution scalogram of Pre Monsoon, Monsoon and Post monsoon respectively.

Figure 2. Multiresolution wavelet spectrogram for Winter season in Tamil Nadu (2004-2017)

Figure 3. Multiresolution wavelet spectrogram for Pre Monsoon season in Tamil Nadu (2004-2017)

Figure 4. Multiresolution wavelet spectrogram for Monsoon season in Tamil Nadu (2004-2017).

Figure 5. Multiresolution wavelet spectrogram for Post Monsoon season in Tamil Nadu (2004-2017)

Neural networks are simplified imitations of the central nervous system, and obviously therefore, have been motivated by the kind of computing performed by the human brain. The structural constituents of a human brain termed neurons are the entities, which perform computations such as cognition, logical inference, pattern recognition and so on. Hence the technology, which has been built on a simplified imitation of computing by neurons of a brain, has been termed Artificial Neural Systems (ANS) technology or Artificial Neural Networks (ANN) or simply Neural Networks. It has been called by various other names viz., Connectionist Networks, Neuro-Computers, Parallel Distributed Processors etc. Also neurons are referred to as neurodes, Processing Elements (PEs), and nodes.

A human brain develops with time and this, in common parlance is known as experience. Technically, this involves the ‘development’ of neurons to adapt themselves to their surrounding environment, thus, rendering the brain plastic in its information processing capability. When this plastic nature be applied to the rainfall parameters, this human brain based networks succumb the adaptive solution for the problem, thus renders the user to attain maximum likelihood estimation on most of the undetermined non-linear problems. Further, ANN also can maintain the stability i.e. the adaptive capability of an ANN in the face of changing environments. Thus, the stability-plasticity issue is of great importance to ANN architectures. This is so since ANN systems essentially being learning systems need to preserve the information previously learnt but at the same time, need to be receptive to learning new information. Training with more number of synthetic datasets will help the ANN to estimate the output model parameters that are very much to determine the characteristics of inverse problems in atmospheric and geosciences ^{36, 37, 38, 39, 40}. (Table 1)

We would like to express our sincere gratitude to the Management of Loyola College, Chennai and SDNB Vaishnav College, Chennai for providing permission to publish this article.