The present study was carried out to find the groundwater quality of coastal aquifer along Manavalakurichi coast. For this study, a total of 30 groundwater samples were collected randomly from open wells and borewells. The concentration of major ions and other geochemical parameters in the groundwater were analyzed in the laboratory by adopting standard procedures suggested by the American Public Health Association. The order of the dominant cations in the study area was found to be Na ? [ Ca 2? [ Mg 2? [ K ? , whereas the sequence of dominant anions was Cl À [ HCO À 3 [ SO 2À 4. The hydrogeochemical facies of the groundwater samples were studied by constructing piper trilinear diagram which revealed the evidence of saltwater intrusion into the study area. The obtained geochemical parameters were compared with the standard permissible limits suggested by the World Health Organization and Indian Standard Institution to determine the drinking water quality in the study area. The analysis suggests that the groundwater from the wells W25 and W26 is unsuitable for drinking. The suitability of groundwater for irrigation was studied by calculating percent sodium, sodium absorption ratio and residual sodium carbonate values. The Wilcox and USSL plots were also prepared. It was found that the groundwater from the stations W1, W25 and W26 is unfit for irrigation. The Gibbs plots were also sketched to study the mechanisms controlling the geochemical composition of groundwater in the study area.

This study was made to find the ground water quality for samples of the town located in the southern most end of India. The study was carried out to evaluate the major ion chemistry, the factors controlling water composition, and suitability of water for both drinking and irrigation purposes. Totally, 21 ground water samples were collected randomly from bore wells and hand pumps throughout the Nagercoil town and its surroundings. The collected samples were analyzed for major ions and the analytical data were interpreted according to published guide lines. The spatial maps show that the concentration of the chemical constituent in ground water varies spatially and temporarily. Sodium is the most dominant cation with Cland HCO 3 as the dominant anion. The abundant of the major is as follows: Na ? [ Cl-[ Mg 2? [ K ? which is equal to HCO 3-[ Cl-[ SO 4. Only one-third of the samples best fit for both consumption and agricultural purposes. The spatial maps show high contamination along the southern region of the study area. Total hardness of the collected samples lies between 60 and 490 mg/l reveals that the 33 % groundwater samples exceeds the safe limit of 300 mg/l. Total dissolved solids (TDS) in the study area ranges between 67 and 2,086 mg/l with a mean value of 523 mg/l. High total hardness and TDS in few places identified that the ground water is unsuitable for drinking and irrigation. In these places, the aquifers are subject to contamination from sewage effluents and excess use of fertilizer and pesticides in agriculture. Such areas require adequate drainage and introduction of alternative salt tolerance cropping.

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the nonlinearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks (ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The singlelayer feed-forward neural network with the back propagation algorithm is chosen as one of the wellsuited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken for training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region (78 7 0 30"E and 8 48 0 45"N), Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model

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The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.