Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN
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J. Indian Soc. Remote Sens. (September 2009) 37:409–421
RESEARCH ARTICLE
Combined use of Socio Economic Analysis, Remote Sensing and GIS Data for Landslide Hazard Mapping using ANN S. Prabu . S.S. Ramakrishnan
Received: 22 September 2008 / Accepted : 26 March 2009
Keywords Landslide hazard mapping . Geographic information system . Socio economic impact . Artificial neural networks
Abstract The term landslide includes a wide range of ground movements, such as slides, falls, flows etc. mainly based on gravity with the aid of several conditioning and triggering factors. Particularly in the last two decades, there has been an increasing international interest in the landslide susceptibility, hazard or risk assessments. In this paper we present a combined use of socioeconomic, remote sensing and GIS data for developing a technique for landslide susceptibility mapping using artificial neural networks and then to
S. Prabu ( ) . S.S. Ramakrishnan Institute of Remote Sensing, College of Engineering, Guindy Anna University, Chennai – 636025, India
email : [email protected]
apply the technique to the selected study areas at Nilgiris district in Tamil Nadu and to analyze the socio economic impact in the landslide locations. Landslide locations are identified by interpreting the satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Then the landslide-related factors are extracted from the spatial database. These factors are then used with an artificial neural network to analyze landslide susceptibility. Each factor’s weight is determined by the back-propagation training method. Different training sets will be identified and applied to analyze and verify the effect of training. The landslide susceptibility index will be calculated by back propagation method and the susceptibility map will be created with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. In this research, GIS is used to analyze the vast amount of data very efficiently and an ANN to be an effective tool to maintain precision and accuracy. Finally, the artificial
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J. Indian Soc. Remote Sens. (September 2009) 37:409–421
neural network will prove it’s an effective tool for analyzing landslide susceptibility compared to the conventional method of landslide mapping. The socio-economic impact is analyzed by the questionnaire method. Direct survey was conducted with the people living in the landslide locations through different set of questions. This factor is also used as one of the landslide causing factors for the preparation of landslide hazard map.
Introduction Landslide risk is defined as the expected number of lives lost, persons injured, damage to property and disruption of economic activity due to a particular landslide hazard for a given area and reference period (Varnes, 1984). While dealing with physical losses, (specific) risk can be quantified as the product of vulnerability, cost or amount of the elements at risk and t
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