Ahmadi, R., Sadat Koodehi, S.M., 2018. Classification and reserve estimation of Robat Arregije Pb-Zn deposit, Khomein Township, Markazi Province, using geostatistical methods. New Findings in Applied Geology 12(24), 39-53. https://doi.org/10.22084/nfag.2018.15657.1296
Al-Thanoon, N.A., Qasim, O.S., Algamal, Z.Y., 2019. A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics. Chemometrics and Intelligent Laboratory Systems 184, 142-152. https://doi.org/10.1016/j.chemolab.2018.12.003
Assareh, E., Behrang, M.A., Assari, M.R., Ghanbarzadeh, A., 2010. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35(12), 5223-5229. https://doi.org/10.1016/j.energy.2010.07.043
Bastante, F.G., Ordóñez, C., Taboada, J., Matías, J.M., 2008. Comparison of indicator kriging, conditional indicator simulation and multiple-point statistics used to model slate deposits. Engineering Geology 98(1), 50-59. https://doi.org/10.1016/j.enggeo.2008.01.006
Bazdar, H., Fattahi, H., Ghadimi, F., 2015. Hybrid ANN with Invasive Weed Optimization Algorithm, a New Technique for Prediction of Gold and Silver in Zarshuran Gold Deposit, Iran. Tethys 3(3), 273-285. https://jtethys.journals.pnu.ac.ir/article_2865_7f601223c226ec0095dc9790e30f7987.pdf
Boadi, B., Sunder Raju, P.V., Wemegah, D.D., 2022. Analysing multi-index overlay and fuzzy logic models for lode-gold prospectivity mapping in the Ahafo gold district – Southwestern Ghana. Ore Geology Reviews 148, 105059. https://doi.org/10.1016/j.oregeorev.2022.105059
Buyuk, E., Karaman, A. 2021. Modeling Magnetotelluric Data from Tuzla-Çanakkale Hydrothermal System with Particle Swarm Optimization to Image Cap Rock Structure. 82nd EAGE Annual Conference & Exhibition, Amesterdam, Netherland https://doi.org/10.3997/2214-4609.202112599
Calagari, A.A., Hosseinzadeh, G., 2006. The mineralogy of copper-bearing skarn to the east of the Sungun-Chay river, East-Azarbaidjan, Iran. Journal of Asian Earth Sciences 28(4), 423-438. https://doi.org/10.1016/j.jseaes.2005.11.009
Carranza, E.J.M. 2009. Data-Driven Modeling of Mineral Prospectivity. In Hale, M., (Ed.), Handbook of Exploration and Environmental Geochemistry. Elsevier Science B.V., PP. 249-310. https://doi.org/10.1016/S1874-2734(09)70012-9
Carranza, E.J.M. 2009. Knowledge-Driven Modeling of Mineral Prospectivity. In Carranza, E.J.M., (Ed.), Handbook of Exploration and Environmental Geochemistry. Elsevier Science B.V., Martin Hale, pp. 189-247. https://doi.org/10.1016/S1874-2734(09)70011-7
Chen, Y., An, A., 2016. Application of ant colony algorithm to geochemical anomaly detection. Journal of Geochemical Exploration 164, 75-85. https://doi.org/10.1016/j.gexplo.2015.11.011
Chudasama, B., 2022. Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations. MethodsX 9, 101629. https://doi.org/10.1016/j.mex.2022.101629
Dong, H., Jian, G., 2015. Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm. Cybernetics and Information Technologies 15(3), 140-149. https://doi.org/10.1515/cait-2015-0047
Dumakor-Dupey, N.K., Arya, S., 2021. Machine Learning—A Review of Applications in Mineral Resource Estimation. Energies 14(14), 4079. https://doi.org/10.3390/en14144079
Ehteram, M., Khozani, Z.S., Soltani-Mohammadi, S., Abbaszadeh, M., 2023. Estimating Ore Grade Using Evolutionary Machine Learning Models. Springer Nature Singapore, P. 101. https://doi.org/10.1007/978-981-19-8106-7_1
Fattahi, H., Ghadimi, F., 2016. A hybrid artificial neural network with particle swarm optimization for estimation of heavy metals of rainwater in the industrial region-a case study. Journal of Tethys 4(2), 154-168. https://journals.pnu.ac.ir/article_3112_986fd323f0ab2f577c0f6acb23e4bc56.pdf
Ghezelbash, R., Maghsoudi, A., 2018. Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran. Comptes Rendus Geoscience 350(4), 180-191. https://doi.org/10.1016/j.crte.2018.02.003
Ghezelbash, R., Maghsoudi, A., Carranza, E.J.M., 2020. Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm. Computers & Geosciences 134, 104335. https://doi.org/10.1016/j.cageo.2019.104335
Ghezelbash, R., Maghsoudi, A., Carranza, E.J.M., 2020. Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran. Ore Geology Reviews 120, 103394. https://doi.org/10.1016/j.oregeorev.2020.103394
Gu, Y., Bao, Z., Song, X., Patil, S., Ling, K., 2019. Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization. Journal of Petroleum Science and Engineering 179, 966-978. https://doi.org/10.1016/j.petrol.2019.05.032
Jafrasteh, B., Fathianpour, N., Suárez, A., 2018. Comparison of machine learning methods for copper ore grade estimation. Computational Geosciences 22(5), 1371-1388. https://doi.org/10.1007/s10596-018-9758-0
Kennedy, J., Eberhart, R. 1995, 27 Nov.-1 Dec. 1995. Particle swarm optimization. Proceedings of ICNN'95 International Conference on Neural Networks, Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968
Li, X.-l., Li, L.H., Zhang, B.-l., Guo, Q.J., 2013. Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing 118, 179-190. https://doi.org/10.1016/j.neucom.2013.03.002
Lin, N., Chen, Y., Liu, H., Liu, H., 2021. A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity. Minerals 11(2), 159. https://doi.org/10.3390/min11020159
Mahmoudabadi, H., Izadi, M., Menhaj, M.B., 2009. A hybrid method for grade estimation using genetic algorithm and neural networks. Computational Geosciences 13(1), 91-101. https://doi.org/10.1007/s10596-008-9107-9
Miranvari, A.S., Calagari, A., Siahcheshm, K., Sohrabi, G., 2019. Geochemical study of alteration zones around Au-bearing silicic veins at Zailic, East of Ahar, East-Azarbaidjan Province. Iranian Journal of Crystallography and Mineralogy 27(2), 347-360. https://doi.org/10.29252/ijcm.27.2.347
Moeini, H., Torab, F.M., 2017. Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area, east of Iran. Journal of Geochemical Exploration 180, 15-23. https://doi.org/10.1016/j.gexplo.2017.05.008
Mohammadzadeh, M., Nasseri, A., Mahboubiaghdam, M., Jahangiri, M., 2021. Mineral prospectivity mapping of Cu-Au by integrating AHP technique with ARAS and WASPAS models in the Sonajil area, E-Azerbaijan. Zeitschrift der Deutschen Gesellschaft für Geowissenschaften, 171-186. https://doi.org/10.1127/zdgg/2021/0256
Nabavi, M., 1984. An introduction to the geology of Iran, Geological Survey of Iran. Tehran University Publications. Tehran.
Pardo-Igúzquiza, E., Dowd, P.A., Baltuille, J.M., Chica-Olmo, M., 2013. Geostatistical modelling of a coal seam for resource risk assessment. International Journal of Coal Geology 112, 134-140. https://doi.org/10.1016/j.coal.2012.11.004
Parsa, M., Carranza, E.J.M., Ahmadi, B., 2022. Deep GMDH Neural Networks for Predictive Mapping of Mineral Prospectivity in Terrains Hosting Few but Large Mineral Deposits. Natural Resources Research 31(1), 37-50. https://doi.org/10.1007/s11053-021-09984-5
Poli, R., Kennedy, J., Blackwell, T., 2007. Particle swarm optimization. Swarm Intelligence 1(1), 33-57. https://doi.org/10.1007/s11721-007-0002-0
Riahi, S., Bahroudi, A., Abedi, M., Aslani, S., Lentz, D.R., 2022. Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods. Geophysical Prospecting 70(2), 421-437. https://doi.org/10.1111/1365-2478.13169
Roshanravan, B., Aghajani, H., Yousefi, M., Kreuzer, O., 2019. Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data. Natural Resources Research 28(2), 309-325. https://doi.org/10.1007/s11053-018-9385-4
Saaty, R.W., 1987. The analytic hierarchy process—what it is and how it is used. Mathematical Modelling 9(3), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8
Shahrestani, S., Mokhtari, A.R., Fatehi, M., 2020. The use of unmixing technique in stream sediment geochemical exploration. Journal of Geochemical Exploration 208, 106339. https://doi.org/10.1016/j.gexplo.2019.106339
Shamshirband, S., Esmaeilbeiki, F., Zarehaghi, D., Neyshabouri, M., Samadianfard, S., Ghorbani, M.A., Mosavi, A., Nabipour, N., Chau, K.W., 2020. Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Engineering Applications of Computational Fluid Mechanics 14(1), 939-953. https://doi.org/10.1080/19942060.2020.1788644
Soltani-Mohammadi, S., Hoseinian, F.S., Abbaszadeh, M., Khodadadzadeh, M., 2022. Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability. Computers & Geosciences 159, 104981. https://doi.org/10.1016/j.cageo.2021.104981
Tahmasebi, P., Hezarkhani, A., 2012. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences 42, 18-27. https://doi.org/10.1016/j.cageo.2012.02.004
Tang, X., Hong, H., Shu, Y., Tang, H., Li, J., Liu, W., 2019. Urban waterlogging susceptibility assessment based on a PSO-SVM method using a novel repeatedly random sampling idea to select negative samples. Journal of Hydrology 576, 583-595. https://doi.org/10.1016/j.jhydrol.2019.06.058
Tenorio, V.O., Bandopadhyay, S., Misra, D., Naidu, S., Kelley, J., 2015. Support vector machines applied for resource estimation of underwater glacier-type platinum deposits. Application Of Computers an d Operations Research in the Mineral Industry, 18, 309-323.
Tsai, P.W., Pan, J.S., Liao, B.Y., Chu, S.C., 2009. Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control 5(12), 5081-5092. https://www.scirp.org/reference/referencespapers?referenceid=1120166
Whitney, D.L., Evans, B.W., 2010. Abbreviations for names of rock-forming minerals. American mineralogist 95(1), 185-187
Wu, X., Zhou, Y., 1993. Reserve estimation using neural network techniques. Computers & Geosciences 19(4), 567-575. https://doi.org/10.1016/0098-3004(93)90082-G
Xi, M., Sun, J., Liu, L., Fan, F., Wu, X., 2016. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine. Computational and Mathematical Methods in Medicine 2016, 3572705. https://doi.org/10.1155/2016/3572705
Yang, X.S., 2010. Nature-inspired metaheuristic algorithms. Luniver press. United Kingdom.
Zhang, Z., Zhang, J., Wang, G., Carranza, E.J.M., Pang, Z., Wang, H., 2020. From 2D to 3D Modeling of Mineral Prospectivity Using Multi-source Geoscience Datasets, Wulong Gold District, China. Natural Resources Research 29(1), 345-364. https://doi.org/10.1007/s11053-020-09614-6
Ziaii, M., Doulati Ardejani, F., Ziaei, M., Soleymani, A.A., 2012. Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry. Applied Geochemistry 27(3), 663-676. https://doi.org/10.1016/j.apgeochem.2011.12.020
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