Efficiency Evaluation of Multi-Scale Algorithms and Simplifying Structure of Spatial Data in Linear Features Analysis (Case Study: Hydrographic Network)

Natural phenomena could be recognize through the selection and then produce the appropriate scale. The critical subject in this way that have been less studied in Iran is identifying the multi-scale algorithms and distinguish them from simplification algorithms. In this research, operation of each multi-scale algorithms at certain scales have supposed, then implementation of structural simplifying algorithms- low pass filter and simple average- in order to identify the flood process using three Digital Elevation Models at 1:25000, 1:50000, and 1:1000000 scales in ArcMap software and HEC-HMS model have evaluated. Results demonstrated that different approaches of map production make different results as the generalization and the best approximation of spatial data. Comparing the result of flood simulation proved sensitivity of such algorithms with respect to scale of base map. It has been specified that the effect of generalization algorithms do not necessarily conform the hierarchical structure of scale of input data. Finally, we suggested to investigate the flood phenomena of Jamash watershed, we should use low pass filter and simple average algorithms to simplify DEMs at 1:25000 and 1:1000000 scales but using these algorithms at 1:50000 scale have not suggested.