Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 12 2 2020 07 22 The combination of attraction model and genetic algorithm to predict stream networks The combination of attraction model and genetic algorithm to predict stream networks 83 94 96992 10.52547/gisj.12.2.83 FA M Shaygan Assistant Professor, Dept. of Remote Sensing & GIS, Tarbiat Modares University, Tehran, Iran M Mokarram Associate Professor, Department of Range and Watershed Management, College of Journal Article 2020 11 22 The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds. The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds. https://gisj.sbu.ac.ir/article_96992_5d1e4124be70f2ceb53948be8d694273.pdf