- Parsa, M., Carranza, E. J. M., and 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.
- Ehteram, M., Khozani, Z. S., Soltani-Mohammadi, S., and Abbaszadeh, M. (2023). “The Necessity of Grade Estimation”. In: Estimating Ore Grade Using Evolutionary Machine Learning Models, Singapore: Springer Nature Singapore, 1-6.
- Dumakor-Dupey, N. K., and Arya, S. (2021). “Machine Learning—A Review of Applications in Mineral Resource Estimation”. Energies, 14(14): 4079.
- Ahmadi, R., and 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.
- Bastante, F., Ordóñez, C., Taboada, J., and Matías, J. (2008). “Comparison of indicator kriging, conditional indicator simulation and multiple-point statistics used to model slate deposits”. Engineering Geology, 98(1-2): 50-59.
- Pardo-Igúzquiza, E., Dowd, P. A., Baltuille, J., and Chica-Olmo, M. (2013). “Geostatistical modelling of a coal seam for resource risk assessment”. International Journal of Coal Geology, 112: 134-140.
- Wu, X., and Zhou, Y. (1993). “Reserve estimation using neural network techniques”. Computers & Geosciences, 19(4): 567-575.
- Jafrasteh, B., Fathianpour, N., and Suárez, A. (2018). “Comparison of machine learning methods for copper ore grade estimation”. Computational Geosciences, 22: 1371-1388.
- Chudasama, B. (2022). “Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations”. MethodsX, 9: 101629.
- Ziaii, M., Doulati Ardejani, F., Ziaei, M., and Soleymani, A. A. (2012). “Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry”. Applied Geochemistry, 27(3): 663-676.
- Tenorio, V. O., Bandopadhyay, S., Misra, D., Naidu, S., and Kelley, J. (2015). “Support vector machines applied for resource estimation of underwater glacier-type platinum deposits”. Application of Computers and Operations Research in the Mineral Industry, 889-902.
- Mahmoudabadi, H., Izadi, M., and Menhaj, M. B. (2009). “A hybrid method for grade estimation using genetic algorithm and neural networks”. Computational Geosciences, 13: 91-101.
- Moeini, H., and 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.
- Soltani-Mohammadi, S., Hoseinian, F. S., Abbaszadeh, M., and 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.
- Sun, T., Li, H., Wu, K., Chen, F., Zhu, Z., and Hu, Z. (2020). “Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China”. Minerals, 10(2): 102.
- Ghezelbash, R., Maghsoudi, A., and 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.
- Bazdar, H., Fattahi, H., and Ghadimi, F. (2015). “Hybrid ANN with Invasive Weed Optimization Algorithm, a New Technique for Prediction of Gold and Silver in Zarshuran Gold Deposit, Iran”. Journal of Tethys, 3(3): 273-286.
- Fattahi, H., and 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.
- Tahmasebi, P., and Hezarkhani, A. (2012). “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation”. Computers & Geosciences, 42: 18-27.
- Ghezelbash, R., Maghsoudi, A., and Carranza, E. J. M. (2020). “Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm”. Computers & Geosciences, 134: 104335.
- Chen, Y., and An, A. (2016). “Application of ant colony algorithm to geochemical anomaly detection”. Journal of Geochemical Exploration, 164: 75-85.
- Gu, Y., Bao, Z., Song, X., Patil, S., and 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.
- Roshanravan, B., Aghajani, H., Yousefi, M., and 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.
- Ghezelbash, R., Daviran, M., Maghsoudi, A., Ghaeminejad, H., and Niknezhad, M. (2023). “Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran”. Applied Geochemistry, 148: 105538.
- Fister, I., Fister Jr, I., Yang, X.-S., and Brest, J. (2013). “A comprehensive review of firefly algorithms”. Swarm and Evolutionary Computation, 13: 34-46.
- Das, S., Maity, S., Qu, B.-Y., and Suganthan, P. N. (2011). “Real-parameter evolutionary multimodal optimization—A survey of the state-of-the-art”. Swarm and Evolutionary Computation, 1(2): 71-88.
- Yang, X.-S. (2010). “Nature-inspired metaheuristic algorithms”. Luniver Press, pp. 148.
- Zhang, Y., and Wu, L. (2012). “A novel method for rigid image registration based on firefly algorithm”. International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC), 2(2): 141-146.
- Dutta, R., Ganguli, R., and Mani, V. (2011). “Exploring isospectral spring–mass systems with firefly algorithm”. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2135): 3222-3240.
- Apostolopoulos, T., and Vlachos, A. (2011). “Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem”. International Journal of Combinatorics, 2011: 523806.
- Horng, M.-H., and Liou, R.-J. (2011). “Multilevel minimum cross entropy threshold selection based on the firefly algorithm”. Expert Systems with Applications, 38(12): 14805-14811.
- Basu, B., and Mahanti, G. K. (2011). “Fire fly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna”. Progress in Electromagnetics Research, 32: 169-190.
- Zaman, M. A., and Abdul Matin, M. (2012). “Nonuniformly spaced linear antenna array design using firefly algorithm”. International Journal of Microwave Science and Technology, 2012: 1-8.
- Giannakouris, G., Vassiliadis, V., and Dounias, G. (2010). “Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization”. In: Hellenic Conference on Artificial Intelligence, Springer, 101-111.
- Yang, X.-S., Deb, S., and Fong, S. (2011). “Accelerated particle swarm optimization and support vector machine for business optimization and applications”. In: Networked DigitalTechnologies (NDT2011), Communications in Computer and Information Science, Springer, 136: 53-66.
- Gholizadeh, S., and Barati, H. (2012). “A comprative study of three metaheuristics for optimum design of trusses”. International Journal of Optimization in Civil Engineering, 3: 423-441.
- Jakimovski, B., Meyer, B., and Maehle, E. (2010). “Firefly flashing synchronization as inspiration for self-synchronization of walking robot gait patterns using a decentralized robot control architecture”. In: International Conference on Architecture of Computing Systems, Springer, 61-72.
- Nayak, J., Naik, B., Pelusi, D., and Krishna, A. V. (2020). “A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks”. In: Nature-Inspired Computation in Data Mining and Machine Learning, X.-S. Yang and X.-S. He (Eds.), Cham: Springer International Publishing, 137-159.
- Lin, N., Chen, Y., Liu, H., and Liu, H. (2021). “A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity”. Minerals, 11(2): 159.
- Nabavi, M. (1984). “An introduction to the geology of Iran, Geological Survey of Iran”. Tehran University Publications, Tehran.
- Calagari, A., Siahcheshm, K., and 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.
- Poli, R., Kennedy, J., and Blackwell, T. (2007). “Particle swarm optimization”. Swarm intelligence, 1(1): 33-57.
- TSai, P.-W., Pan, J.-S., Liao, B.-Y., and Chu, S.-C. (2009). “Enhanced artificial bee colony optimization”. International Journal of Innovative Computing, Information and Control, 5(12): 5081-5092.
- Yousefi, A., and Amirshahi, B. (2015). “A hybrid meta-heuristic algorithm based on ABC and Firefly algorithms”. Journal of Advances in Computer Engineering and Technology, 1(4): 53-58.
- Khaze, S. R., Maleki, I., Hojjatkhah, S., and Bagherinia, A. (2013). “Evaluation the efficiency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem”. International Journal on Computational Sciences & Applications (IJCSA), 3(4): 23-35.
- Wang, G.-G., Guo, L., Duan, H., and Wang, H. (2014). “A new improved firefly algorithm for global numerical optimization”. Journal of Computational and Theoretical Nanoscience, 11(2): 477-485.
- Shamshirband, S., Esmaeilbeiki, F., Zarehaghi, D., Neyshabouri, M., Samadianfard, S., Ghorbani, M. A., Mosavi, A., Nabipour, N., and 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.
- Mohammadzadeh, M., Nasseri, A., Mahboubiaghdam, M., and 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.
- Ghezelbash, R., and 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.
- Saaty, R. W. (1987). “The analytic hierarchy process—what it is and how it is used”. Mathematical Modelling, 9(3): 161-176.
- Carranza, E. J. M. (2008). “Geochemical anomaly and mineral prospectivity mapping in GIS”. Elsevier.
- Riahi, S., Bahroudi, A., Abedi, M., Aslani, S., and 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.
- Boadi, B., Sunder Raju, P. V., and 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.
|