Envirnomental Impact Assessment Using Neural Network Model: a Case Study of the Jahani, Konarsiah and Kohe Gach Salt Plugs, Se Shiraz, Iran

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. * Corresponding author. Dept. of Earth Sciences, Faculty of Sciences, Shiraz University, 71454 Shiraz, Iran. Tel: +9809177173319 ; fax: +982284572. [email protected].