اصغری مقدم، الف.، ندیری، ع. و نورانی، و (1387). "مدل سازی بارش دشت تبریز با استفاده از شبکههای عصبی مصنوعی". مجله دانش کشاورزی. جلد18 شماره 1.
اصغری مقدم، الف.، ندیری، ع. و فیجانی، الف. (1389). "استفاده از مدلهای شبکههای عصبی مصنوعی و زمینآمار برای پیشبینی مکانی غلظت فلوئورید". مجلة دانش آب-خاک. سال1/19 شماره 2 ص 145-129.
مهندسین مشاور صحراکاو، (1387). "مطالعات ژئوفیزیک دشتهای تبریز، تسوج و هادیشهر". سازمان آب منطقهای آذربایجان شرقی.
ندیری، ع.، اصغری مقدم، الف. و فیجانی، الف. (1387). "پیشبینی مکانی هدایت هیدرولیکی در محدودة متروی شهر تبریز". دوازدهمین همایش انجمن زمین شناسی ایران ، اهواز 30 بهمن تا 2 اسفند.
ندیری، ع. و اصغری مقدم، الف. (1389). "استفاده از روشهای آماری چند متغیره در مطالعه فرآیندهای هیدروشیمیایی آبخوانها، مطالعه موردی: دشت تسوج". چهاردهمین همایش انجمن زمین ایران و بیست و هشتمین گردهمایی علوم زمین، دانشگاه ارومیه 25 تا 27 شهریور.
Abbaspour, K.C., Schulin, R. and Van Genuchten, M. (2001). Estimating unsaturated soil hydraulic parameters using ant colony optimization. Advances in Water Resources, 24(8), pp. 827-841.
Alyamani, M. and Sen, Z. (1993). Determination of hydraulic conductivity from complete grain size distribution curves. Ground Water, 31(4), pp. 551-555.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000). Artificial neural network in hydrology, part I and II. J. Hydrol. Eng. 5(2), pp. 115-137.
Bates, J.M. and Granger, C.W.J. (1969). The combination of forecast. Operations Research Quarterly 20(4), pp. 451–468.
Boadu, F.K. (1997). Rock properties and seismic attenuation: neural network analysis. Pure and Applied Geophysics 149(3), pp. 507–524.
Boadu, F.K. (1998). Inversion of fracture density from field seismic velocities using artificial neural networks. Geophysics 63(2), pp. 534–545.
Bouwer, H. (1989). The Bouwer and Rice slug test- An update. Ground Water, 27(3), pp. 304-309.
Bouwer, H. and Rice, R. C. (1976). A slug test for determining hydraulic conductivity of unconfined aquifer with completely or partial penetrating wells. Water Resource Research, 12(3), pp. 423-438.
Chen, C.H. and Lin, Z.S. (2006). A committee machine with empirical formulas for permeability prediction. Computers and Geosciences.32(4), pp. 485–496.
Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2(4), pp. 267–278.
Chow, V. T. (1952). On the determination of transmissibility and storage coefficient from pumping test data. Transactions, American Geophysical Union, 33(3), pp. 397-404.
Cooper, H. H., Bredehoeft, J. D. and Papadopulos, I. S. (1967). Response of a finite diameter well to an instantaneous charge of water. Water Resource Research, 3(1), pp. 263-269.
Cooper, H. H. and Jacob, C. E. (1946). A generalized graphical method for evaluation formation constants and summarizing well field history. Transactions, American Geophysical Union, 27(4), pp. 526-534.
Fair, G. M. and Hatch, L. P. (1933). Fundamental factors governing the stream line flow of water through sand. Journal of American Water Works Association. 25(11), pp. 1551-1565.
Geman, S., Bienenstock, E. and Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4(1), pp. 1–58.
Harb, N., Haddad, K. and Farkh, S. (2010). Calculation of transverse resistance to correct aquifer resistivity of groundwater saturated zones : implications for estimating its hydrogeological properties. Lebanese Science Journal, 11(1), pp. 105-115.
Haykin, S. (1991). Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ, 842p.
Hazen, A. (1892). Some physical properties of sands and gravels. Massachusetts State Board of Health 24th Annual Report. pp. 539-556.
Hopfield, J. J. (1982). Neural network and physical systems with emergent collective computational abilities. Proc. Nat. Academy of Scientists, 79, pp.2554-2558.
Huang, Y., Gedeon, T.D. and Wong, P.M. (2001). An integrated neural–fuzzy– genetic-algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Engineering Applications of Artificial Intelligence 14(1), pp.15–21.
Huang, Z. and Williamson, M.A. (1996). Artificial neural network modeling as an aid to source rock characterization. Marine and Petroleum Geology, 13(2), pp. 227–290.
Hvorslev, M. G. (1951). Time lag and soil permeability in groundwater observations. Bulletin No. 36, US Army Corps of Engineering, Waterways Experiments Stations, Vicksburg, Mississippi. 49p.
Jarrah, O.A. and Halawani, A. (2001). Recognition of gestures in Arabic sign language using neuro-fuzzy systems. Artificial Intelligence 133(1-2), pp. 117–138.
Kadkhodaie-Ilkhchi, A., Rezaee, M. R. and Rahimpour-Bonab, H. (2009). A committee neural network for prediction of normalized oil content from well log data: An example from South Pars GasField, Persian Gulf. .Journal of Petroleum Science and Engineerin,g 65(1-2), pp. 23-32.
Karimpouli, S., Fathianpour, N. and Roohi, J. (2010). A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). Journal of Petroleum Science and Engineering, 73(3-4), pp. 227–232.
Kennedy, J. (1998). The behavior of particles. In: Porto VW, Saravanan N, Waagen D and Eiben AE (eds) Evolutionary Programming VII, Springer, pp. 581- 590.
Kennedy, J., and Eberhart R. (1995). Particle Swarm Optimization. In: Proceedings of the International Conference on Neural Networks, Perth, Australia, IEEE, Piscataway.
Li, Sh. and Liu, Y. (2005). Parameter Identification Procedure in Groundwater Hydrology with Artificial Neural Network. Advances in Intelligent Computing, Springer Berlin, 971p.
Li, Sh., Liu, Y. and Yu, H. (2006). Parameter Estimation Approach in Groundwater Hydrology Using Hybrid Ant Colony System.
Lecture Notes in Computer Science,
Springer Berlin.
Lim, J.-S. (2005). Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. Journal of Petroleum Science and Engineering, 49(3-4), pp.182–192.
Maier H. R. , Jain, A., Dandy, G. C. and Sudheer, K.P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25(8), pp. 891-909.
Maier, H.R. and Dandy, G.C. (2000). Neural network for the prediction and forecasting water resources variables: a review of modeling issues and applications. Environmental Modeling & Software, 15(3), pp. 101-124.
Maillet, R. (1947). The fundamental equations of electrical prospecting. Geophysics, 12(4), pp. 529-556.
Mategaonkar, M. and Eldho, T.I. (2012). Groundwater remediation optimization using a point collocation method and particle swarm optimization. Environmental Modelling& Software, 32, pp. 37-48.
Myers, D. E. (1991). Pseudocross-variograms, posotive- definiteness and cokriging. Mathematical Geology, 23(6), pp. 805-816.
Naftaly, U., Intrator, N. and Horn, D. (1997). Optimal ensemble averaging of neural networks. Computation in Neural Systems, 8(3), pp. 283–296.
Neuman, S. P. (1972). Theory of flow in unconfined aquifers considering delayed response of water table, Water Resources Research, 8(4), pp. 1031-1045.
Nikravesh, M. and Aminzadeh, F. (2003). Soft Computing and Intelligent Data Analysis in Oil Exploration. Part1: Introduction: Fundamentals of Soft Computing. Elsevier, Berkeley, USA, 744 p.
Shepherd, R. G. (1989). Correlations of permeability and grain size. Ground Water, 27(5), pp. 633-638.
Shi Y. and Eberhart R. (1999). Empirical study of particle swarm optimization, Proceeding IEEE International Congers Evolutionary Compution, Washington, DC., USA, pp. 1945-1950.
Shi, Y. and Eberhart R. (1998). A modified Particle Swarm Optimizer, Proceeding of the 1998 IEEE Conference on Evolutionary Compution. AK, Anchorage.
Sperry, M. S. and Peirce, J. J. (1995). A model for estimating the hydraulic conductivity of granular material based on grain shape, grain size and porosity. Ground Water, 33(2), pp. 892-898.
Theis, C. V. (1935). The relationship between the lowering of piezometric surface and the rate and duration of discharge of a well using groundwater storage. Transactions, American Geophysical Union, 16(2), pp. 519-524.
Todd, D. K. and Mays, L. W. (2005). Groundwater Hydrology. Wiley, 3 edition. 656 p.
Valcarce, R. M. O. and Rodríguez, W. M. (2004). Resolution power of well log geophysics in karst aquifers. Journal of Environmental Hydrology. 12, pp. 1-7.