Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 1, Volume 53, Issue 1, January and February 2025, Page 1-22 PDF (1.56 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2024.326709.1374 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ashraf abdallah  1; Bara Al-MISTAREHI2; Amir SHTAYAT3
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| 1Faculty of Engineering, Aswan University, Aswan, Egypt | ||||
| 2Department of Civil Engineering, Jordan University of Science and Technology, Irbid, Jordan | ||||
| 3Department of City Planning and Design, Jordan University of Science and Technology, Irbid, Jordan | ||||
| Abstract | ||||
| Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution. | ||||
| Keywords | ||||
| DEM; GNSS-RTK; Agricultural; Machine Learning; Egypt | ||||
| References | ||||
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