Document Type : Original Research Paper


1 Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt

2 Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo, Egypt

3 Mining, Petroleum, and Metallurgical Engineering Department, Faculty of Engineering, Cairo University, Cairo, Egypt


The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO2%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.


Main Subjects

[1]. Dumakor-Dupey, N. K., & Arya, S. (2021). Machine learning—a review of applications in mineral resource estimation. Energies, 14(14), 4079.
[2]. Galetakis, M., Vasileiou, A., Rogdaki, A., Deligiorgis, V., & Raka, S. (2022). Estimation of mineral resources with machine learning techniques. Materials Proceedings, 5(1), 122.
[3]. Tariq, A., Yan, J., Ghaffar, B., Qin, S., Mousa, B. G., Sharifi, A., ... & Aslam, M. (2022). Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data. Water, 14(19), 3069.
[4]. Jinchuan, Ke. (2002). Neural-network modelling of placer ore grade spatial variability. Dissertation, University of Alaska, Fairbanks.
[5]. Yasrebi, A. B., Hezarkhani, A., Afzal, P., Karami, R., Tehrani, M. E., & Borumandnia, A. (2020). Application of an ordinary kriging–artificial neural network for elemental distribution in Kahang porphyry deposit, Central Iran. Arabian Journal of Geosciences, 13(15).
[6]. Mahmoudabadi, H., Mohammad, I., & Mohammad, B.M. (2009) A hybrid method for grade estimation using genetic algorithm and neural networks. Comput Geosci, 13:91–101.
[7]. Chiroma, H., Noor, A. S. M., Abdulkareem, S., Abubakar, A. I., Hermawan, A., Qin, H., ... & Herawan, T. (2017). Neural networks optimization through genetic algorithm searches: a review. Appl. Math. Inf. Sci11(6), 1543-1564.
[8]. Tahmasebi, P., & Hezarkhani, A., (2010). Comparison of optimized neural network with fuzzy logic for ore grade estimation. Aust J Basic Appl Sci, 4(5):764–772.
[9]. Tahmasebi, P., & Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic- genetic algorithm for grade estimation. Comput Geosci, 42, 18–27.
[10]. Li, XL., Li, LH., Zhang, BL., & Guo, QJ. (2013). Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing 118. 179–190.
[11] Dutta, S., Bandopadhyay, S., Ganguli, R., & Misra, D. (2010). Machine learning algorithms and their application to ore reserve estimation of sparse and imprecise data. Journal of Intelligent Learning Systems and Applications2(02), 86-96.
[12]. Embaby, A., Ismael, A., Ali, F. A., Farag, H. A., Mousa, B. G., Gomaa, S., & Elwageeh, M. (2023). An approach based on Machine Learning Algorithms, Geostatistical Technique, and GIS analysis to estimate phosphate ore grade at the Abu Tartur Mine, Western Desert, Egypt. Min. Depos, 17(1).
[13]. Afzal, P., Farhadi, S., Konari, M. B., Meigoony, M. S., & Saein, L. D. (2022). Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling. Geopersia, 12(1), 191–199.
[14]. Farhadi, S., Afzal, P., Konari, M. B., Saein, L. D., & Sadeghi, B. (2022). Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals 2022, Vol. 12, Page 689, 12(6), 689.
[15]. Mostafaei, Kamran., & Ramazi, H. (2019a). Investigating the applicability of induced polarization method in ore modelling and drilling optimization: a case study from Abassabad, Iran. Near Surface Geophysics, 17(6), 637–652.
[16]. Waqas, H., Shang, J., Munir, I., Ullah, S.; Khan, R., Tayyab, M., Mousa, B.G., Williams, S. (2022). Enhancement of the Energy Performance of an Existing Building Using a Parametric Approach. J. Energy Eng, 149, 04022057.
[17]. Park, Y. S., & Lek, S. (2016). Artificial neural networks: Multilayer perceptron for ecological modeling. In Developments in environmental modelling (Vol. 28, pp. 123-140). Elsevier.
[18]. Misra, D., Samanta, B., Dutta, S., & Bandopadhyay, S. (2007). Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data. International Journal of Mining, Reclamation and Environment, 21(4), 282-294.
[19]. Mostafaei, K., & Ramazi, H. (2019). Mineral resource estimation using a combination of drilling and IP-Rs data using statistical and cokriging methods. Bulletin of the mineral research and exploration, 160(160), 177-195.
[20]. Nezamolhosseini, S. A., Mojtahedzadeh, S. H., & Gholamnejad, J. (2017). The application of artificial neural networks to ore reserve estimation at choghart iron ore deposit. روش های تحلیلی و عددی در مهندسی معدن, 6(ویژه نامه انگلیسی), 73-83.‎.
[21]. Mostafaei, K., & Ramazi, H. R. (2018). 3D model construction of induced polarization and resistivity data with quantifying uncertainties using geostatistical methods and drilling (case study: Madan Bozorg, Iran. Journal of Mining and Environment, 9(4), 857–872.
[22]. Mostafaei, Kamran., & Ramazi, H. (2018). Compiling and verifying 3D models of 2D induced polarization and resistivity data by geostatistical methods. Acta Geophysica, 66(5), 959–971.
[23]. Dwarapudi, S., & Rao, S. M. (2007). Prediction of iron ore pellet strength using artificial neural network model. ISIJ international, 47(1), 67-72.
[24]. Abdelhamid, M. M. A., Mousa, B. G., Waqas, H., Elkotb, M. A., Eldin, S. M., Munir, I., ... & Galal, A. M. (2022). Artificial thermal quenching and salt crystallization weathering processes for the assessment of long-term degradation characteristics of some sedimentary rocks, Egypt. Minerals, 12(11), 1393.
[25]. Gouda, M.A. (1996). Trend Analysis and Simulation Modeling of El-Gidida Iron Ore Deposits. Unpublished Ph.D .Thesis, Mining and Petroleum Engineering Department, AL-Azhar University.
[26]. Badr Hussein, K., Ibrahim, M., & Mousa, B. G. (2023). Modeling and Analysis of Shoreline Change in the Sidi Abdel Rahman Coast Area, Egypt. NAŠE MORE: znanstveni časopis za more i pomorstvo, 70(1), 23-37.
[27]. Mousa, B.G., Embaby, A.K., & Osman, M.E. (2015). GIS Technology for El-Gedida Iron Ore to satisfy the Requirements of Egyptian Blast Furnace. International Journal of Scientific & Engineering Research, 6(9), 8–14.
[28]. El-Araf, M.M., & Lotfy, Z.H. (1989). Genetic Karst Significance of the Iron Ore Deposits of El Bahariya Oasis, Western Desert, Egypt . Reprint from the Annals of the Geological Survey of Egypt, 15:1-30.
[29]. Galal, El-Habaak.,  Mohamed, Askalany., Mohamed, Faraghaly., & Mahmoud, Abdel-Hakeem. (2016). Application of Microscopy Coupled With Image Analysis Technique In Ore Dressing: A Case Study from El-Gedida Iron Mine,  El-Bahariya Oasis, Western Desert, Egypt. International Journal of Basic and Applied Science, Vol. 04, No. 03, pp. 33-41.
[30]. El Aref, M., Abd El-Rahman, Y., Zoheir, B., Surour, A., Helmy, H. M., Abdelnasser, A., & et al. (2020). Mineral Resources in Egypt (I). Metallic Ores, 521–587.
[31]. El Bassyony, A A. (2005). Bahariya teetotumensis n.gen.n.sp. from the Middle Eocene of Egypt. Rev. Paléobiol, 24, p.319-329.
[32]. Kennedy, K. H. 2009. Introduction to 3D Data. John Wiley & Sons, Inc., Hoboken, New Jersey.
[33]. Mousa, B.G., Embaby, A.E., & Osman, M.E.(2016). Applications of GIS in Operations of Open Cast Mining. LAP Lambert Academic Publishing, Germany, ISBN 3659882410.
[34]. Liu, H., & Wu, C. ) 2019). Developing a Scene-based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data. Forests, 11, 28.
[35]. Mousa, B.G., Embaby, A. Kh., and Osman, M.E. (2014). Creating Data Base for Um Salamah-El Sibaiyyah- East Nile Valley Phosphate Ore by using Geographic Information System to Assist in Mining Process Management. Journal of Al-Azhar University Engineering Sector (JAUES), 9(1): 1548-1556.
[36]. Sadawy, M. M., & M, A. El ashkar, (2012).Prediction and modeling of corrosion in steel oil storage tank from non-destructive inspection. Journal of Al Azhar University Engineering Sector, 7(4), 42-53.
[37]. Sadawy, M. M.,  Ismael, A. F., & Goud, M. A. (2015).Geostatistical Analysis for Corrosion in Oil Steel Tank. American Journal of Science and Technology, Vol. 2, No. 2, pp. 38-42.
[38]. Taany, R.A., Tahboub, B., & G, A. (2009). Saffarini, Geostatistical analysis of spatiotemporal variability of groundwater level fluctuations in Amman-Zarqa basin, Jordan: A case study, Environ. Geo. l57, 525-535.
[39]. Mosaad, M. S., & Eltohamy, R. Elsharkawy.(2013). Prediction and Modelling of Corrosion in Steel Storage Tank using Non-destructive Inspection. Journal of Materials Science and Engineering, B 3 (12). pp.785-792
[40] Corte, A.F., Calaforra, J.M., Espinos, R. J., &  Martos, F.S.(2006). Geostatistical spatiotemporal analysis of air temperature as an aid to delineating thermal stability zones in a potential show cave: Implications for environmental management. Jour. of Envir. Manag, 81, 371-383.
[41] Mousa, B.G., Shu, H., Freeshah, M., & Tariq, A. (2020). A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio. remote sensing, 12, 3804.
[42]. Gomah, M.E., Li, G., Khan, N.M., Sun, C., Xu, J., Omar, A.A., Mousa, B.G., Abdelhamid, M.M.A., & Zaki, M.M.(2022). Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite using Multivariate Statistics and Machine Learning Techniques. Mathematics, Vol. 10, Page 4523 2022, 10, 4523.
[43]. Mousa, B.G., & Shu, H. (2020). Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques. Earth Sp. Sci,7, 1–16.
[44.] Abdelhamid, M.M.A., & Mousa, B.G. (2023). prediction method for abrasion loss rate of some Egyptian carbonate rocks due to cyclic salt crystallization weathering using physico-mechanical deterioration: insights from laboratory investigations. Acta Geod. Geophys, 1–18.
[45]. Freeshah, M., Zhang, X., Şentürk, E., Adil, M. A., Mousa, B. G., Tariq, A., ... & Refaat, M. (2021). Analysis of atmospheric and ionospheric variations due to impacts of super typhoon Mangkhut (1822) in the Northwest Pacific Ocean. Remote Sensing13(4), 661.