18 results on '"Zarzoura, Fawzi"'
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2. Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya.
3. Spatiotemporal analysis of Urban Heat Island and land use land cover changes using Landsat images and CA-ANN machine learning techniques: a case study of Dakahlia government, Egypt.
4. Persistent scatterer interferometry (PSI) technique for road infrastructure monitoring: a case study of the roadway network of the Nile Delta (Egypt).
5. New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques.
6. Assessing the influence of differential code bias and satellite geometry on GNSS ambiguity resolution through MANS-PPP software package.
7. Smart monitoring of road pavement deformations from UAV images by using machine learning.
8. Implementing SRIF filter with MANS-PPP software package for GNSS precise point position solution accuracy enhancement.
9. PRECISION EVALUATION FOR DIFFERENT GNSS RINEX 3 USING ONLINE SERVICES AND OPEN-SOURCE SOFTWARE PPP-MANS.
10. Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements.
11. Computational intelligence approaches for estimating the unconfined compressive strength of rocks.
12. APPROXIMATION OF GRAVITATIONAL CALCULATION BASED ON ANALYTICAL MODELS.
13. Assessment and prediction of land-use/land-cover change around Blue Nile and White Nile due to flood hazards in Khartoum, Sudan, based on geospatial analysis.
14. An Investigation on the Performance of Soft Computing Techniques for Point Displacement Modeling for Suspension Bridge Using GNSS Technique.
15. Performance of soft computing techniques for GNSS data processing and point displacement modeling for suspension bridge.
16. Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge.
17. Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms.
18. Predicting lake wave height based on regression classification and multi input–single output soft computing models.
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