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The CMDA administers the Chennai Metropolitan Region, spread over an area of 5,904 km 2 (2,280 sq mi) and covers the districts of Chennai, Thiruvallur, Chengalpattu, Ranipet and Kancheepuram. [1] It was set up for the purposes of planning, co-ordination, supervising, promoting and securing the planned development of the Chennai Metropolitan ...
The Chennai Metropolitan Development Authority (CMDA) is the nodal agency that handles town planning and development within the metro area. In 1974, an area encompassing 1,189 km 2 (459 sq mi) around the city was designated as the metropolitan area which was subsequently expanded to 5,904 km 2 (2,280 sq mi) in 2022.
Chennai is predicted to face a deficit of 713 mld of water by 2026 as the demand is projected at 2,248 mld and supply estimated at 1,535 mld. [263] The city's sewer system was designed in 1910, with some modifications in 1958.
Information flow in the CLUE-S /Dyna-CLUE model (overview) [9] The Dyna-CLUE (dynamic conversion of land use and its effects) model is the adapted version of CLUE-S model, built upon the combination of the top-down approach of spatial allocation of land-use change and bottom-up approach of specification of conversions for specific land-use alterations.
As it works through the process to establish a new comprehensive plan that will guide the town through 2035, Irmo is considering a future land-use map to strategize how more than 6,000 surrounding ...
Manali Pudhunagar was among the first areas to receive desalinated water from the Minjur desalination plant. [1]Though Manali Pudhunagar was created as a satellite township by CMDA in the 1980s, CMDA has yet to hand it over to the local bodies concerned with the better development of civic amenities.
Land use capability maps are maps created to represent the potential uses of a "unit" of land. They are measured using various indicators, although the most common are five physical factors ( rock type , soil type , slope, erosion degree and type, and vegetation).
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.