Cause Of Urbanization In Hyderabad

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Urbanization in Hyderabad
The main cause of above mentioned flooding in Hyderabad is unprecedented rate of urban growth i.e. urbanization or urban sprawl. The city experienced a huge population shift or migration from the coastal areas, Rayalseema, and other parts of Telangana after declaration of the capital city of Andhra Pradesh. The enormous population pressure leads to the improper urban development planning. The haphazard growth had an inverse effect in the communities. When the heavy rainfall takes places, inundation occurs in the low-lying areas and the drainage system did not have the capacity to drain the run off quickly to prevent floods. It affects the whole city life badly. Table 3: Increasing Trend of population size in Hyderabad
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GIS data represents real objects (such as roads, land use, altitude, trees, waterways etc.) with digital data determining the mix. ArcGIS Desktop is comprised of a set of integrated applications.

Terrset is an integrated geospatial software system for monitoring and modelling the earth system for sustainable development. It incorporates the IDRISI GIS Analysis and IDRISI Image Processing tools along with a constellation of vertical applications. The full Terrset constellations include:
• The GIS Analysis System
• Image Processing System
• Land Change Modeller (LCM)
• Habitat and Biodiversity Modeller (HBM)
• Ecosystem Service Modeller (ESM)
• Earth Trends Modeller ( ETM)
• Climate Change Adaption Modeller ( CCAM)

Land Change Modeller –
LCM is an innovative land planning and decision support software tool for land change analysis and prediction with a special facility for REDD (Reducing Emission for deforestation and forest degradation) project level modelling. It is a vertical application that analyses land cover change, empirically modelling explanatory variables and predicting future changes.

Figure- 2:
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Because land use maps are fundamental prerequisite for modelling future growth. Individual land use classes were extracted for the remotely sensed images for each timestamp. The land use maps were initially classified based on the unsupervised classification into four classes- Water, Vegetation, Built-up and Others. In unsupervised classification, image-processing software classifies an image based on natural groupings of the spectral properties of the pixels, without the user specifying how to classify any portion of the image. Conceptually, unsupervised classification is similar to cluster analysis where observations (in this case, pixels) are assigned to the same class because they have similar values. Common clustering algorithm includes Kmean, Isodata clustering. In this study Isodata, clustering has been used by taking 200