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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.

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 | 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.

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