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
Abstract:Fire is one of the most destructive threats faced by our forests. Fire is good servant but a bad master. The fire season starts in March/April continues up to June. Wildfires destroy not only flora (tree, herbs, grassland, forbs, etc.) and their diversity but also considerable long term negative impact on fauna including wild endangered species. Repeated fires can convert some shrub-lands to grass and fire exclusion converts some grassland to shrub-land and forest. Fires affect animals mainly through effects on their habitat. The extent of fire effects on animal communities generally depends on the extent of change in habitat structure and species composition caused by fire. Fire can also influence a physico-chemical property of soil including texture, color, bulk density, pH, porosity, organic matter, nutrient availability and soil biota. Drought, disease, insect infestation, overgrazing or a combination of these factors may increase the impact of fire on an individual plant species or communities. Common effects include plant mortality, increase flowering, seed production and numerous communal affects. Fire affected area showed reduction in species diversity both in flora and fauna. In a social context, fire directly affects people, property and infrastructure, thereby directly affecting the health and livelihood of individuals and communities.
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