TY - JOUR ID - 7409 TI - Assessment of Spatial Multi-Criteria Decision-Making with Process of the Artificial Neural Networks Method to Site Selection of the Wastewater Treatment Plant (Case Study: Qeshm Island) JO - International Journal of Advanced Biological and Biomedical Research JA - IJABBR LA - en SN - 2383-2762 AU - Fallah, Mahdi AU - Vagharfard, Hassan AU - Farajzadeh, Manouchehr AU - Nick kheslat, Ali AD - Graduate student GIS and Remote Sensing, University of Hormozgan, Qeshm, Iran AD - Assistant Professor, Department of Natural Resources, University of Hormozgan, Bandar Abbas, Iran AD - Associate Professor, Department of GIS and Rs, University of Tarbiat Modarres, Tehran, Iran AD - Master of water and wastewater, utility company of Qeshm, Qeshm, Iran Y1 - 2014 PY - 2014 VL - 2 IS - 6 SP - 2061 EP - 2066 KW - Wastewater treatment KW - Site Selection KW - ANN KW - Qeshm Island KW - GIS DO - N2 - Wastewater treatment technology in the cyclic nature of the process that takes a long time. But man tries to rush to their needs with experience and understanding of the natural processes of interaction, and using technology to build their Industrial development is authorized. Sewage treatment reed have been born from the vision of man's increasing need to water daily decreases the natural resources provide. Location of the place is one of the main uses of GIS and GIS Nowadays many ignorant people are familiar with the location. But what is remembered today as the location of the equivalent site selection, the order of analysis that will lead to the best place or places to be for a specific user. Therefore, using multiple and very diverse, the various layers of spatial data according to the criteria considered, together are usually the places where the best places are introduced, and the resulting method. This study is the first layer and standards were prepared from different sources of information, then based on the opinions of experts using analytic hierarchy process weight classes, each benchmark was performed. For network training algorithm of back-propagation and a sigmoidal activation function was used, the results indicate that it is a very high correlation coefficient of the neural network was able to identify suitable areas. Finally, about 104 km Qeshm Island area were suitable for the construction of wastewater treatment plant that requires ground visits is the expert. UR - https://www.ijabbr.com/article_7409.html L1 - https://www.ijabbr.com/article_7409_26e82569ac1bd75b7a62cce3071cb3c1.pdf ER -