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Sujatha E.R.,SASTRA University | Selvakumar R.,SASTRA University | Rajasimman U.A.B.,Bharathidasan University | Victor R.G.,Sairam Group of Institutions
Geomatics, Natural Hazards and Risk | Year: 2015

Morphometric analysis is a key to understand the hydrological process and hence is a prerequisite for the assessment of hydrological characteristics of surface water basin. Morphometric analysis to determine the drainage characteristics of Palar sub-watershed, a part of Shanmukha watershed in the Amaravati sub-catchment is done using Advanced Space-borne Thermal Emission and Reflection Global Digital Elevation Model (ASTER GDEM) data, and is supplemented with topographical maps in geographical information systems platform. This study uses ASTER GDEM data to extract morphometric features of a mountain stream at micro-watershed level. The sub-watershed is divided into six micro-watersheds. The sub-watershed includes a sixth-order stream. Lower stream orders, in particular first-order streams, dominate the sub-watershed. Development of stream segments is controlled by slope and local relief. Drainage pattern of the sub-watershed and micro-watersheds is dendritic in general. The mean bifurcation ratio of the sub-watershed is 3.69 but its variation between the various stream orders suggests structural control in the development of stream network. The shape factors reveal the elongation of the sub-watershed and micro-watersheds.The relief ratio reveals the high discharge capability of the sub-watershed and meagre groundwater potential. This study is a useful tool for planning strategies in control of soil erosion and soil conservation. © 2013, Taylor & Francis.

Ramani S.E.,SASTRA University | Pitchaimani K.,Indian Institute of Astrophysics | Gnanamanickam V.R.,Sairam Group of Institutions
Journal of Mountain Science | Year: 2011

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area. An attempt is made to map the landslide susceptibility in Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis. Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map, which is used to build the spatial model of landslide susceptibility. The model describes the relationship between the dependent variable (presence and absence of landslide) and the independent variables selected for study (predictor variables) by the best fitting function. A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis. An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable. Relief, slope, aspect, plan curvature, profile curvature, land use, soil, topographic wetness index, proximity to roads and proximity to lineaments are taken as independent variables. The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in the study area. The model shows that the most significant parameter contributing to landslides is slope. The other significant parameters are profile curvature, soil, road, wetness index and relief. The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %. © 2011 Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg.

Thanigavelan DR. V.,Sairam group of Institutions | Lakshmanakumar V.,Sairam group of Institutions | Kaliyamurthi V.,Sairam group of Institutions | Rajamanickam G.V.,Sairam group of Institutions
Journal of Applied Pharmaceutical Science | Year: 2011

Background: Vediuppu Chendhuram (VC) is a traditional Siddha mineral formulation applied to treat Urinary tract dysfunction such as burning micturation and retention of urine. It is synthesized through special oxidation of Vediuppu as narrated in the text Anubhoga Vaithiya Navaneetham. Physicochemical characterization of VC has been carried out using qualitative compound analysis and modern techniques such as Fourier transform infra-red spectroscopy, inductively coupled plasma analysis and scanning electron microscopy. Such study reveals the presence of heavy metals like arsenic, cadmium, mercury and lead are present below the detection limit and the presence of sodium, potassium, sulphur, phosphorus and calcium under acceptable limits. The primary objective of this work is to validate the safety of VC through animal model. Methods: The raw Vediuppu are procured from country drug store at Nagercoil, Tamilnadu and purified by the traditional procedure by soaking in Cow's urine until it dried. The test drug VC is prepared by the process of Pudam (Oxidation) described in Anuboga Vaithiya Navaneetham 3rd part, pg no. 76-77. The safety profile is evaluated by doing acute oral toxicity and repeated oral toxicity studies under OECD guidelines on Albino wistar rats. Results: Animals were found to be safe upto 300mg/kg body weight in acute oral toxicity study. Repeated toxicity study of VC has revealed that upto 200mg/kg body weight; all the treated animals have survived throughout the dosing period of 28 days. But at the dose of 400mg/kg, exhibits mortality on 21st day of treatment. No significant changes in the body weight, food and water intake have been observed. Complete urine, haematology, biochemical analyses, gross necropsy and histopathological examination at the end of treatment did not reveal any abnormalities. Conclusion: Vediuppu Chendhuram is the safest drug under intended human adult dosages (520 mg-1040 mg) as illustrated in the literature.

Kumar R.V.,Parisutham Institute of Technology and Science | Rajamanickam V.G.,SaiRam Group of Institutions
International Journal of Oceans and Oceanography | Year: 2012

A Study has been made to evaluate the changes occurred along the coastal stretch in Vanagiri village, Sirkazhi taluk, Nagapattinam district, Tamilnadu using satellite imageries supplemented with GIS. The study area is accommodated between 11̊ 06' and11̊8'45' N latitudes and 79̊49' and 79̊51'30' E longitudes. IRS 1A (LISS II), IRS 1C (LISS III), merged data of IRS P6 and IRS 1D (LISS IV and PAN) and IRS P6 (LISS III) satellite imageries in the scale of 1:50000 were availed, respectively for the years 1992, 1997, 2004 and 2006 and were interpreted visually for land use and were categorized according to NRSA land use classification. The spatial database of the land use maps interpreted was created using Arc Map GIS software. The spatial database of the land use created for the study area for years 1992, 1997, 2004 and 2006 were subjected to intersection analysis and the land use changes were derived. The reasons pertaining to these changes were discussed in this paper. The land use changes seen after Tsunami were also discussed. This study will be used to create the basis for conducting sustainable land use planning or setting up a sustainable development strategy in the study area. Since the study is carried out at village level, it will be useful for any researchers, scientists, planners and all the government organizations for any type of subsequent application in planning and further research in near future. © Research India Publications.

Sujatha E.R.,SASTRA University | Victor Rajamanickam G.,Sairam Group of Institutions | Kumaravel P.,Indian Institute of Astrophysics
Journal of Earth System Science | Year: 2012

This paper reports the use of a GIS based Probabilistic Certainty Factor method to assess the geo-environmental factors that contribute to landslide susceptibility in Tevankarai Ar sub-watershed, Kodaikkanal. Landslide occurrences are a common phenomenon in the Tevankarai Ar sub-watershed, Kodaikkanal owing to rugged terrain at high altitude, high frequency of intense rainfall and rapidly expanding urban growth. The spatial database of the factors influencing landslides are compiled primarily from topographical maps, aerial photographs and satellite images. They are relief, slope, aspect, curvature, weathering, soil, land use, proximity to road and proximity to drainage. Certainty Factor Approach is used to study the interaction between the factors and the landslide, highlighting the importance of each factor in causing landslide. The results show that slope, aspect, soil and proximity to roads play important role in landslide susceptibility. The landslide susceptibility map is classified into five susceptible classes - low, very low, uncertain, high and very high - 93.32% of the study area falls under the stable category and 6.34% falls under the highly and very highly unstable category. The relative landslide density index (R index) is used to validate the landslide susceptibility map. R index increases with the increase in the susceptibility class. This shows that the factors selected for the study and susceptibility mapping using certainty factor are appropriate for the study area. Highly unstable zones show intense anthropogenic activities like high density settlement areas, and busy roads connecting the hill town and the plains. © Indian Academy of Sciences.

Sujatha E.R.,SASTRA University | Rajamanickam G.V.,Sairam Group of Institutions
Human and Ecological Risk Assessment | Year: 2015

ABSTRACT: Natural hazards like landslides, earthquakes, and floods are a major deterrent to the development of mountain regions of the world. Recently, there has been a rise in the number of landslides in Western Ghats, India. Several factors cause landslides and they depend on the local geo-environmental set-up of the region. This study attempts to map the spatial distribution of landslide hazard and analyze the risk for a typical hill town, Kodaikkanal in the Western Ghats, India, facing rapid urbanization and infra-structure growth, using a weighted linear combination model in a geographic information system platform. Landslides in the region are triggered by rainfall and it is fairly uniform throughout the area. Hence, the susceptibility map is used as the hazard map. Validation of the weighted linear combination model using landslide hazard index shows that landslide density increases with the hazard class. The risk assessment matrix (RAM) is used to evaluate risk based on the land use and landslide hazard category. The land use map is reclassified by assigning damage potential for each land use feature. The risk map is classified into low, moderate, and high risk categories. Suitable control measures are suggested for various risk categories. © 2015, Copyright © Taylor & Francis Group, LLC.

Ramani Sujatha E.,SASTRA University | Kumaravel P.,Indian Institute of Astrophysics | Rajamanickam G V.,Sairam Group of Institutions
Journal of the Indian Society of Remote Sensing | Year: 2012

Rapid urbanization, intense infra-structure development and increased tourism related activities have resulted in the change of landscape of the Kodaikkanal town and its surrounding, a popular hill town in Tamilnadu, South India. As an after effect, the numbers of landslides and rock-falls have increased steadily in the past decade. Landslide susceptibility analysis is carried out for this area using conditional probability analysis. The geo-spatial database for mapping landslide susceptibility consists of the factors - Relief, Slope, Aspect, Curvature, Weathering, Land use, Topographic Wetness Index and Proximity to road. Two sampling strategies - point and seed-cell are compared for landslide susceptibility mapping. The Landslide Susceptibility map developed using conditional probability method is verified using R index for both sampling strategies. The study shows that both the sampling strategies perform with good accuracy, seed cell technique excels slightly over point sampling. 86.11% of the landslides fall in the high and critical susceptible zones. The results show that conditional probability technique provides a simple tool for susceptibility analysis. The method can be used at regional scale and is a valuable input for planning purpose. © 2012 Indian Society of Remote Sensing.

Sujatha E.R.,SASTRA University | Kumaravel P.,Indian Institute of Astrophysics | Rajamanickam G.V.,Sairam Group of Institutions
Bulletin of Engineering Geology and the Environment | Year: 2013

This study aims to demonstrate the application of a Bayesian probability-based weight of evidence model to map landslide susceptibility in the Tevankarai stream watershed, Kodaikkanal, India. Slope gradient, relief, aspect, curvature, land use, soil, lineament density, flow accumulation and proximity to roads were the landslide conditioning factors we considered in order to assess susceptibility. The weight of evidence model uses the prior probability of occurrence of a landslide event to identify areas prone to landslides based on the relative contributions of landslide conditioning factors. A pair-wise test of conditional independence was performed for the above factors, allowing the combination of conditioning factors to be analyzed. The contrast (difference of W+ and W-) was used as weight for each factor's type. The best observed combination consisted of the relief, slope, curvature, land use and distance to road factors, showing an accuracy of 86.1 %, while the accuracy of the map with all factors was 83.9 %. © 2013 Springer-Verlag Berlin Heidelberg.

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