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  • 标题:Envirnomental impact assessment using Neural Network Model: A case study of the Jahani ,konarsiah and Kohe Gach salt plugs, SE Shiraz, Iran
  • 本地全文:下载
  • 作者:Mohammad Hasan Tayebi ; Majid Hashemi Tangestani ; Hasan Roosta
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2010
  • 卷号:XXXVIII - Part 7B
  • 页码:557-562
  • 出版社:Copernicus Publications
  • 摘要:This study employs Multi-Layer Perceptron (MLP) to estimate environmental impact of salt plugs using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). VNIR and SWIR datasets of ASTER were assessed in mapping and detecting Jahani, Konarsiah, and Kohe Gach salt plugs and the affected areas located at SE Shiraz, Iran. PC color composite and geological map of the region were used to select training areas. Three datasets including, IARR, PCA and MNF were used as input to the MLP. The results of each input were compared with the ground truth and the geological map to determine the accuracy and therefore to select the more appropriate dataset to be input to MLP approach input. The results demonstrated a number of the polluted sites and the main polluted tributaries that convey the water as well as the salt plug materials into the Firouzabad River. It is also indicated that the MNF input (with 85% overall accuracy) can obtain a slightly more accurate estimation than the IARR (79%) and PCA inputs (82%). It is concluded that the result of MNF input to MLP is more applicable to effective environmental impact assessment and sustainable water resources management at salt plug-affected areas
  • 关键词:Salt plug; Environmental impact; MLP neural network; ASTER
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