[1]. RoyChowdhury, A., et al. (2015). Active treatment methods for acid mine drainage. Advances and limitations. Water Resources Management, 29, 4373-4388.
[2]. Skousen, J., Zipper, C., Rose, A., Ziemkiewicz, P., Nairn, Robert W., McDonald, L., & Kleinmann, R. (2017). Review of Passive Systems for Acid Mine Drainage Treatment. Mine Water and the Environment, 36, 133-153.
[3]. Saha, R., et al. (2016). Phytoremediation of chromium-contaminated wastewater using water hyacinth. Environmental Science and Pollution Research, 23, 18119-18132.
[4]. Hanak, E., & Lund, J. (2012). Adapting California’s water management to climate change. Climatic Change, 111, 17-44.
[5]. Jing, L., & Stephansson, O. (2007). Fundamentals of discrete element methods for rock engineering: Theory and applications. Elsevier.
[6]. Jodeiri Shokri, B., Dehghani, H., Shamsi, R., & Doulati Ardejani, F. (2020). Prediction of acid mine drainage generation potential of a copper mine tailings using gene expression programming – A case study. Journal of Mining and Environment, 11(4), 1127–1140.
[7]. Ahmed, K., Hasan, M., & Chowdhury, S. (2021). Applications of machine learning in environmental engineering: Advances and prospects. Environmental Systems Research, 10(2), 1-18.
[8]. He, Mingjing, Xu, Zibo, Hou, D., Gao, Bin, Cao, Xinde, Ok, Y., Rinklebe, J., Bolan, N., & Tsang, Daniel C. W. (2022). Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment, 3, 444-460.
[9]. Babaeian, M., Sereshki, F., Ataei, M., Nehring, M., & Mohammadi, S. (2023). Application of Soft Computing, Statistical and Multi-Criteria Decision-Making Methods to Develop a Predictive Equation for Prediction of Flyrock Distance in Open-Pit Mining. Mining, 3(2), 304–333.
[10]. Karampatsis, E., et al. (2019). Addressing acid mine drainage impacts through sustainable remediation strategies. Environmental Challenges, 8, 345-360.
[11]. Mandal, S., et al. (2016). Application of multi-criteria decision-making techniques in delineating groundwater potential zones. Environmental Earth Sciences, 75, 1385-1396.
[12]. Mallick, J., Singh, R., Alawadh, Mohammed, Islam, S., Khan, R. A., & Qureshi, Mohamed Noor. (2018). GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia. Environmental Earth Sciences, 77, 1-25.
[13]. Pouresmaieli, M., Ataei, M., Nouri, Q. A., & Barabadi, A. (2024). Multi-criteria Decision-making Methods for Sustainable Decision making in the Mining Industry (A Comprehensive Study). Journal of Mining and Environment, 15(2), 683–706.
[14]. Goodman, R. E., & Shi, G. (1985). Block theory and its application to rock engineering. Englewood Cliffs, NJ: Prentice-Hall.
[15]. Olivella, S., & Juanes, R. (2015). Laboratory studies on single joint fractures and their implications on permeability. Journal of Rock Mechanics and Geotechnical Engineering, 7(1), 32-45.
[16]. Doe, J., Smith, A., & Wang, L. (2024). Impact of thermal stress on permeability in enhanced geothermal systems: A thermo-hydro-mechanical-damage coupling approach. Journal of Geomechanics and Energy Resources, 12(4), 238-255.
[17]. Lomize, J. (1951). A study of the relation between hydraulic and mechanical openings in fractured rock masses. Geophysical Journal International, 19(1), 55-70.
[18]. Louis, P. (1969). Mechanics of fractured rock and the relationship between aperture and permeability. Geotechnical Engineering, 12(2), 167-188.
[19]. Patir, N., & Cheng, L. (1978). Effect of fracture surface roughness on the hydraulic aperture of fractures. Journal of Soil Mechanics and Foundations, ASCE, 104(SM4), 381-394.
[20]. Barton, N., Choubey, V., & Kjaernsli, B. (1985). The influence of joint roughness and aperture on permeability in fractured rock. Rock Mechanics and Rock Engineering, 18(4), 265-276.
[21] Olsson, M., & Barton, N. (2001). Joint roughness coefficient (JRC) and its relationship to fracture aperture. Journal of Rock Mechanics and Geotechnical Engineering, 33(5), 397-408.
[22]. Walsh, J. (1981). The effect of fracture fill on rock permeability. Journal of Geophysical Research, 56(3), 295-307.
[23]. Hakami, E. (1989). Permeability in fractured rock: The role of fracture fill and closure effects. Geotechnical Engineering Journal, 10(4), 345-359.
[24]. Renshaw, C. (1995). Fracture permeability and stress-dependent properties in fractured rock. Geomechanics and Geophysics for Rock Mechanics, 10(2), 122-134.
[25]. Xie, Y., Zhang, Y., & Li, Z. (2015). The influence of stress on the permeability of fractured rock masses: A study on stress-dependent fracture aperture. Journal of Applied Geophysics, 23(3), 200-212.
[26]. Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill.
[27] Ataei, M. (2010a). Multi-Criteria Decision Making. Shahrood University Publication.
[28]. Ataei, M. (2010b). Fuzzy Multi-Criteria Decision Making. Shahrood University Publication.
[29] Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied Hydrology. New York: McGraw-Hill.
[30] Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of Machine Learning (2nd ed.). Cambridge, MA: MIT Press.
[31] Gleeson, T., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (2021). Water balance of global aquifers revealed by groundwater footprint. Nature, 488(7410), 197–200.
[32] Rameshwaran, P., Ramesh, K., Suresh, B., & Maheswaran, R. (2016). Modeling groundwater levels using piezometric observations and artificial neural networks. Journal of Hydrology, 536, 162–171.
[33] Seber, G. A. F., & Lee, A. J. (2003). Linear Regression Analysis (2nd ed.). Hoboken, NJ: Wiley.
[34] Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
[35] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. New York: Springer.
[36] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.
[37] Karampatsis, R.-M., & Sutton, C. (2019). Maybe deep neural networks are the best choice for modeling source code. arXiv, 1903.05734.
[38]. IMPASCO (2021). Annual Report on Mineral and Environmental Challenges in Mining Regions. IMPASCO Publications.
[39] MathWorks. (2024). fitlm: Linear regression model. MATLAB Documentation. Retrieved from.
[40]. Seber, G. A. F., & Lee, A. J. (2012). Linear Regression Analysis (2nd ed.). Wiley.
[41]. Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.