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001-es BibID:	BIBFORM134107
Első szerző:	Daoud, Abazar Mohamed Ahmed (földtudományi kutató, geológus, geográfus)
Cím:	Comprehensive hazard susceptibility assessment in Port Sudan city usingAHP: emphasizing flash flood risk, soil moisture, and salinity dynamics / Daoud, Abazar M. A. ; Abir, Mohammed Noor A. M. ; Kazem, Mahmoud M. ; Satti, Albarra M. N. ; Shebl, Ali ; Mohamed, Musaab A. A. ; Agyemfra, George Joel ; Elrasheed, Abdelmajeed A. ; Csámer, Árpád ; Rózsa, Péter
Dátum:	2025
ISSN:	1947-5705 1947-5713
Megjegyzések:	Natural hazards threaten ecosystems, societies, and infrastructure, especially in rapidly urbanizing areas. Port Sudan City, on the Red Sea coast near the Red Sea Hills, is vulnerable to flash floods, soil collapse from salinity, and variable soil moisture, affecting sustainable land use. This study develops a hazard susceptibility assessment using the Analytic Hierarchy Process (AHP) and geospatial analysis. Factors including elevation, slope, curvature, geology, land use/cover (LULC), drainage density, salinity, and soil moisture were integrated into an AHP-based multi-criteria framework. Satellite data (Landsat 8 OLI, Sentinel?2) provided indices such as NDVI, LSE, LST, and SMI. Four factor groups were analyzed: (i) hydro-meteorological factors driving floods; (ii) soil-related factors causing infrastructure damage; (iii) terrain factors increasing rockfall risk; and (iv) LULC and geology-related factors. An integrated hazard map was validated with field surveys and lab analyses. Flood risk had the highest AHP weight, followed by salinity and soil moisture. Southern areas, including the airport and Bashair Terminal, are highly flood-prone; the 2024 Arbaat Dam collapse increased northern flood risk. Shoreline areas face salinity and moisture hazards, while western steep terrain is prone to rockfalls. Results highlight the need for updated hazard maps, better drainage, and sustainable land-use planning.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Analytic hierarchy process multi-hazard susceptibility mapping flash flood soil salinity soil moisture Port Sudan
Megjelenés:	Geomatics, Natural Hazards and Risk. - 16 : 1 (2025), p. 1-32. -
További szerzők:	Abir, Mohammed Noor A. M. M. Kazem, Mahmoud (1993-) (geológus, Phd hallgató) Satti, Albarra Shebl, Ali (1992-) (geológus) Mohamed, Musaab Adam Ahmed (1990-) (geologist) Agyemfra, George Joel Abdelmajeed, Adam Elrasheed Ali (1988-) (Geologist) Csámer Árpád (1976-) (geológus) Rózsa Péter (1956-) (petrográfus)
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001-es BibID:	BIBFORM131916
035-os BibID:	(Scopus)105013101506 (WoS)001559258400001
Első szerző:	Daoud, Abazar Mohamed Ahmed (földtudományi kutató, geológus, geográfus)
Cím:	Machine Learning-Based Lithological Mapping and Mineral Prospecting Using Hyperspectral and Multispectral Remote Sensing in Wadi Halfa, North Sudan / Abazar M. A. Daoud, Ali Shebl, Mutwakil Nafi, Abdelmajeed A. Elrasheed, Arpád Csámer, Péter Rozsa
Dátum:	2025
ISSN:	1464-343X
Megjegyzések:	At present, the global demand for mineral resources is critical, leading nations to focus on exploration. Remote sensing is a cost-effective tool, especially in harsh terrains. This study conducted lithological mapping in Wadi Halfa, North Sudan, using algorithm-based remote sensing, field observations, and petrographical analysis to detect iron ore and barite deposits. Multisensor optical datasets (L9, L8, and S2) were integrated to effectively delineate the lithological units. In addition, PRISMA hyperspectral data, with its detailed spectral signatures, improved spatial distribution patterns of barite and iron oxides across the study area. Image processing techniques (false colour composites, principal component analysis, minimum noise friction, band ratios) detected hydroxyl-bearing minerals, ferric, and ferrous oxides. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Mahalanobis Distance Classifier (MDC) achieved overall accuracies of 95.51 %, 94.59 %, and 98.99 %, respectively. The study helped interpret the spatial relationship between barite and iron oxides. Four types of iron ore with more than three distinct layers were identified, including (a) oolitic ironstone, (b) ferruginous sandstone, (c) ferruginous ironstone, and (d) Banded Iron Formation (BIF) during field investigations, petrographic examinations, and chemical analysis validated remote sensing findings, revealing iron ore (62.7 % Fe) and barite (63.9 % Ba) concentrations. An economic assessment confirmed the presence of economic reserves suitable for exploitation. This research is recommended for broader application, particularly in machine learning for delineating iron ore and barite deposits in complex sedimentary sequences. The realization of machine learning algorithms emphasizes their potential to enhance lithological mapping in sedimentary sequences, suggesting a promising direction for future research.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Support vector machine Artificial neural network Mahalanobis Iron ore Barite Economic reserves
Megjelenés:	Journal Of African Earth Sciences. - 232 (2025), p. 1-22. -
További szerzők:	Shebl, Ali (1992-) (geológus) Nafi, Mutwakil Abdelmajeed, Adam Elrasheed Ali (1988-) (Geologist) Csámer Árpád (1976-) (geológus) Rózsa Péter (1956-) (petrográfus)
Pályázati támogatás:	Stipendium Hungaricum Scholarship Program Egyéb
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001-es BibID:	BIBFORM116157
035-os BibID:	(WoS)001078595200001 (Scopus)85171617945
Első szerző:	Shebl, Ali (geológus)
Cím:	PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert : Evaluating the support vector machine, random forest, and XG boost machine learning algorithms / Ali Shebl, David Abriha, Amr S. Fahil, Hanna A. El-Dokouny, Abdelmajeed A. Elrasheed, Arpád Csámer
Dátum:	2023
ISSN:	0169-1368
Megjegyzések:	In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk PRISMA Lithological mapping Arabian Nubian Shield RF XGB SVM
Megjelenés:	Ore Geology Reviews. - 161 (2023), p. 1-16. -
További szerzők:	Abriha Dávid (1995-) (geográfus) Fahil, Amr S. El-Dokouny, Hanna A. Abdelmajeed, Adam Elrasheed Ali (1988-) (Geologist) Csámer Árpád (1976-) (geológus)
Pályázati támogatás:	Stipendium Hungaricum scholarship Egyéb
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