LAND FORM CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORKS (SELF-ORGANIZATION MAP) (CASE STUDY: BASIN GAVKHONI)

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MOKARAM MARZIE; NEGAHBAN SAEED

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Journal: QUATERNARY JOURNAL OF IRAN Year:2015 | Volume:1 | Issue:3 Page(s): 225-238

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Information Journal Paper

Title

LAND FORM CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORKS (SELF-ORGANIZATION MAP) (CASE STUDY: BASIN GAVKHONI)

Author(s)

MOKARAM MARZIE | NEGAHBAN SAEED | Issue Writer Certificate

Keywords

CLASSIFICATION OF LANDFORMSQ2

SELFQ2

ORGANIZING NEURAL NETWORKS (SOM)Q1

TOPOGRAPHIC POSITION INDEX (TPI)Q2

GAVKHONI BASINQ2

Abstract

Introduction system of landform classification for soil mapping has been desired by soil scientists in Canada for a long time. The Canada Soil Survey Committee (CSSC) adopted a system at a meeting held at the University of Guelph in February 1976. Many aspects of the system came from mapping schemes used by the Geological Survey of Canada for mapping surficial geology. The system also embodies concepts developed initially by R.J. Fulton and later by N.F. Alley while doing terrain mapping in British Columbia. However, the needs of the soil scientist for a terrain or landform classification system are not necessarily compatible with those of the geologist. Relief analysis is a tool to analyse a landscape based on a Digital Elevation Model (DEM). One of the simplest parameters might be the elevation itself, or slope or the exposition of a given point in a landscape. Moore et al. (1991) state that the spatial distribution of topographic attributes can often be used as an indirect measure of the spatial variability of hydrological, geomorphologic and biological processes. The advantage compared to other information such as soil parameters or biomass production estimates is based on the relatively simple and fast techniques to model processes in large areas and the complex spatial patterns of environmental systems as seen by Moore et al. (1993b). Another relief parameter relevant for this work is landforms or relief units. Each of these contains certain characteristic physical, chemical, and biological processes and parameters (see Dehn et al., 2001). Milne (1936) was one of the first scientists, who recognised the catena principle of soil formation in a hilly terrain (Ruhe, 1960).Material And Methods Materials are classified according to their essential properties within a general framework of their mode of formation. Four groups (components) of materials have been recognized to facilitate further characterization of the texture and the surface expression of the materials. They are unconsolidated mineral, organic, consolidated, and ice components. These groups and the classes established within them are presented below. This research is trying to classify landforms on the basis of SELF-organizing neural network algorithm (SOM) in the watershed Gavkhoni pay to use the SOM algorithm is used to classify landforms of 6 parameters that includes orientation (aspect), height (elevation), tilt (slope), the longitudinal and transverse profiles (plan, profile) and curvature (curvature) is.Generally, The aim ofthisstudyis theclassificationof landformsin thebasinGavkhoni. Classification methodstohelpmajorlandformsvisitthe field, usingtopographic mapsandaerial photos, which requires experience. Theautomaticmethodbased ondigital elevation model(DEM)can beusedto classifylandformsBasinGavkhoni. Result And discussion The results of the CLASSIFICATION OF LANDFORMS using SOM algorithm showed that 6 cluster (class) in the study area there as clusters 1 and 5 includes landforms that are at high altitudes and cluster 3 includes landforms that are located at the lowest height. The rest of the cluster, including the landforms that the average height of the watershed studied. So the algorithm can be used to predict the landforms of the study area. The results showed that6isthe maximumdatain SOM algorithm. Also, at leastinthishexdatais zero, which indicates thatthere areno numbersinthislocation. The results ofprincipal component analysisshowedhigh densityanddistributiondata. According to theabove resultsshow thatthelandformsinput datain Figure6 classeshave beendistributedin the study area.Conclusion In this research was used SOM (SOM) to classify landforms. In order to use algorithms for CLASSIFICATION OF LANDFORMS of 6 parameters were used in the watershed Gavkhoni, The results of the CLASSIFICATION OF LANDFORMS using SOM algorithm showed that 6 cluster (class) in the study area ther. as clusters 1 and 5 includes landforms that are at high altitudes and cluster 3 includes landforms that are located at the lowest height. While cluster 3 includes landforms that are the lowest height. The rest of the cluster, including the landforms that the average height of the watershed studied. In general, using the SOM algorithm can be 6 classes to classify landforms in the study area predicted. Using the results of the SOM algorithm to manage watershed management approaches should be considered 6.

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APA: Copy

MOKARAM, MARZIE, & NEGAHBAN, SAEED. (2015). LAND FORM CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORKS (SELF-ORGANIZATION MAP) (CASE STUDY: BASIN GAVKHONI). QUATERNARY JOURNAL OF IRAN, 1(3), 225-238. SID. https://sid.ir/paper/255929/en

Vancouver: Copy

MOKARAM MARZIE, NEGAHBAN SAEED. LAND FORM CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORKS (SELF-ORGANIZATION MAP) (CASE STUDY: BASIN GAVKHONI). QUATERNARY JOURNAL OF IRAN[Internet]. 2015;1(3):225-238. Available from: https://sid.ir/paper/255929/en

IEEE: Copy

MARZIE MOKARAM, and SAEED NEGAHBAN, “LAND FORM CLASSIFICATION USING SELF-ORGANIZING NEURAL NETWORKS (SELF-ORGANIZATION MAP) (CASE STUDY: BASIN GAVKHONI),” QUATERNARY JOURNAL OF IRAN, vol. 1, no. 3, pp. 225–238, 2015, [Online]. Available: https://sid.ir/paper/255929/en

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