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http://hdl.handle.net/10603/474251
Title: | Optimal route analysis for solid waste transportation using gis and ann |
Researcher: | Vishnuvardhan K |
Guide(s): | Elangovan K |
Keywords: | Solid Waste Management Geographic Information System Artificial Neural Network |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | The rapid increase in economy and world population along with the expectations of a luxurious lifestyle has expanded the municipal waste generation, causing its management to be a leading worldwide problem. The issue is considerably more alarming in the urban areas as ill-advised waste management prompts disintegrating the quality of soil, water, and environment, which make general health hazards. These issues of waste removal and management were generally surveyed by conventional techniques, which require heaps of data. General health of occupants has been influenced by the expansion of unstable waste management in the urban areas of agricultural nations. Solid waste management has become a broad consideration from the municipalities. A reasonable arrangement and plan of solid waste management of a city require precise forecasting of the solid waste generated and gathered by the city experts in the landfill for conclusive removal and other environmentally friendly power alternatives. newlineUrbanization has taken forward a few illnesses enduring to mankind, other than getting economic and social development its crease. Due to weight of urbanization, the vast majority of the urban community zones are developing quickly. Modernization and progress have had a lot of inconveniences and one of the primary concerns is the contamination it is causing to the earth be it land, air or water. In the non-industrial nations, the deficiency of fundamental waste data is a critical impediment for municipal solid waste management. To assess a powerful waste management plan, recognizable proof of affecting financial elements and projection of municipal solid waste generation (MSWG) assumes an essential role. However, a few forecasting techniques have been used to measure future MSWG. newline |
Pagination: | xviii,139 |
URI: | http://hdl.handle.net/10603/474251 |
Appears in Departments: | Department of Civil Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 142.21 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 4.27 MB | Adobe PDF | View/Open | |
03_content.pdf | 197.22 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 474.96 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 330.58 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 355.28 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 381.16 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 2.09 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 451.91 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.01 MB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 3.41 MB | Adobe PDF | View/Open | |
12_chapter 8.pdf | 974.3 kB | Adobe PDF | View/Open | |
13_annexures.pdf | 269.13 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 473.71 kB | Adobe PDF | View/Open |
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