Topographic Analysis for Landform Classification Using Geographic Information System: A Case Study of Kirkuk City, Iraq

Al-Jubouri, Jalal Abdul Rahman; Hassan, Nihad

doi:10.33899/earth.2023.142994.1139

	Topographic Analysis for Landform Classification Using Geographic Information System: A Case Study of Kirkuk City, Iraq
Iraqi National Journal of Earth Science (INJES)
Article 5, Volume 25, Issue 1, January 2025, Pages 49-69 PDF (2.27 M)
Document Type: Research Paper
DOI: 10.33899/earth.2023.142994.1139
Authors
Jalal Abdul Rahman Al-Jubouri^* ; nihad hassan
Department of Surveying Engineering Techniques, Technical College of Kirkuk, Northern Technical University, Kirkuk, Iraq.
Abstract
Landform classification is the process of identifying and grouping different types of landforms based on their physical characteristics. This study investigated the use of Digital Elevation Models (DEMs) to classify landforms in Kirkuk City, Iraq. The study used two different DEMs: the Shuttle Radar Topography Mission (SRTM), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM). It used three different landform classification models: the Topographic Position Index (TPI), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results of this study showed that the CNN model was the most effective at classifying landforms. The CNN model achieved an overall accuracy (OA) of 88.91% and a kappa of 0.883. The SVM model was the second most effective model, with an OA of 79.81% and a kappa coefficient of 0.781. The TPI model was the least effective model, with an OA of 67.12% and a kappa of 0.658. The field verification results showed that the CNN model was also the most accurate in terms of field mapping. The results of this study suggest that the CNN model is a promising tool for landform classification.
Keywords
Landform,,; ,،Topographic Position Index,,; ,،Convolutional Neural Network,,; ,،Digital Elevation Model

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