ارزیابی کارایی برخی روش‌های هوش مصنوعی در مدل‌سازی فرسایش‌پذیری بادی خاک در بخشی از اراضی شرق دریاچه ارومیه

Abbasi, Y., Ghanbarian-Alavijeh, B., Liaghat, A. and Shorafa, M. (2011). Evaluation of pedotransfer functions for estimating soil water retention curve of saline and saline-alkali soils of Iran. Pedosphere 21 (2), 230–237.

Aljarah, I., Faris, H. and Mirjalili, S. (2016). Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22 (1), 1-15.

Barzegar, R., Asghari Moghadam, A. and baghban, H. (2015). A supervised committee machine artifical intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from tabriz plain aquifer, iran. Stoch Environ Res Risk Assess, 30(3), 883–899.

Chebud, Y., Naja, G.M., Rivero, R.G. and Melesse, A.M. (2012). Water quality monitoring using remote sensing and artificial neural network. Water Air Soil Pollut, 223, 4875–4887.

Cook, D.F., Ragsdale, C.T. and Major, R.L. (2000). Combining a neural network with a genetic algorithm for process parameter optimization. Engineering Applications of Artificial Intelligence 13: 391-396.

Dastranj, H., Tavakoli, F. and  Soltanpour, A. (2018). Investigating the water level and volume variations of Lake Urmia using satellite images and satellite altimetry. Scientific - Research Quarterly of Geographical Data 27(107), 149-163. (In Farsi).

De-Gennaro, G., Trizio, L., DiGilio, A., Pey, J., Pérez,  N., Cusack, M., Alastuey, A. and Querol, X. (2013). Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean. Science of the Total Environment, 463–464, 875–883.

Fallah-Mehdipour, E., Bozorg Haddad, O. and Marino, M.A. (2013). Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-environment Research, 7, 253-260.

Galletly, J.E. (1992). An overview of genetic algorithms. Kybernetes 21(6): 26-30.

Garcia, M. and Arguello, C. (2005). A hybrid approach based on neural networks and genetic algorithms to study the profitability in the Spanish stock market. Applied Econnomics Letters, 12, 303–308.

Gee, G.W. and Or, D. (2002). Particle size analysis. In: Dane J.H., G.C.Topp, editors. Methods of soil analysis. Part 4. Physical methods. Soil Science Society of America. Madison (WI), p. 255–293.

Haghverdi A., Ghahraman, B., Joleini, M., Khoshnud A., Yazdi, A. and Arabi, Z. (2011). Comparison of different Artificial Intelligence methods in modeling water retention curve (Case study: North and Northeast of Iran). J. of Water and Soil Conservation, 18(2), 65-84. In Farsi

Hamm, L., Brorsen, B.W. and Hagan, M.T. (2007). Comparison of stochastic global optimization methods to estimate neural network weights. Neural Process Lett, 26, 145–58.

Hashimoto, Y. (1997). Applications of artificial neural networks and genetic algorithms to agricultural system. Computers and Electronics in Agriculture 18:71- 72.

Holland, J.H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-72.

Jain, A. and Srinivasulu, S. (2004). Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water and resource research, 40(4), W04302.

Jamalizadeh Tajabadi, M.R., Moghadam Nia, A.R., Piri, J. and Ekhtesasi, M.R. (2010). Application of artificial neural networks in dust storm prediction (case study: Zabol city). Iranian Journal of Rangeland and Desert Research 17 (2): 205-220. (In Farsi)

Kantardzic, M. (2011). Data Mining: concepts, models, methods, and algorithms. John Wiley & Sons. Alberta, Canada. pp 529.

Kaunda, R.B. (2015). A neural network assessment tool for estimating the potential for backward erosion in internal erosion studies. Computers and Geotechnics, 69, 1–6.

Kaveh, A., Ghazaan,  M.I. (2017). Enhanced whale optimization algorithm for sizing op- timization of skeletal structures, Mech. Based Des. Struct. Mach, 45(3), 345–362.

Kemper, W.D. and Rosenau, R.C. (1986). Aggregate stability and size distribution. In: Klute A,editor, Methods of Soil Analysis. ASA and SSSA, Madison (WI), p. 425–442.

Keshavarzi, A. and Sarmadian, F. (2010). Comparison of artificial neural network andmultivariate regression methods in prediction of soil cation exchange capacity. Int. J. Environ. Chem. Ecol. Geo GeoEng. 4(12): 644–649.

Kim,  R.J., Loucks, D.P. and Stedinger, J.R. (2012). Artificial neural network models of watershed nutrient loading. Water Resour. Manage, 26, 2781–2797.

Ladumor, D.P., Jangir, p., Trivedi, P.N. and Kumar, A. (2016). A whale optimization algorithm approach for unit com- mitment problem solution, in: Proceeding of the 2016 National Conference on Advancements in Electrical and Power Electronics Engineering (AEPEE-2016), Morbi.

Liu, L.Y., Li, X.L., Shi, P.J., Gao, S.Y., Wang, J.H., Ta, W.Q., Song, Y., Liu, M.X., Wang, Z. and Xiao, B.L. (2007). Wind erodibility of major soils in the farming-pastoral ecotone of China. Journal of Arid Environments, 68, 611-623.

Lopez, M.V., de Dios Herrero, J.M., Hevia, G.G., Gracia, R. and Buschiazzo, D.E. (2007). Determination of the wind-erodible fraction of soils using different methodologies. Geoderma, 139, 407–411.

Mafarja, M. and  Mirjalili, S.  (2017). Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302–312.

Mafarja, M. and Mirjalili, S. (2018). whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441–453.

Menhaj, M.B. (2018). Fundamental of neural network (Computational intelligence). Amirkabir University of Technology Press, Tehran, Iran. pp 716. (In Farsi)

Mirjalili, S. and Lewis, A. (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67.

Nazghelichi, T., Aghbashlo, M. and Kianmehr, M.H. (2011). Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture, 75, 84–91.

Nelson, D.W. and Sommers, L.E. (1982). Total carbon, organic carbon, and organic matter. Pp. 539–579. In: Page AL, Miller RH and Keeney DR (eds). Methods of Soil Analysis, part 2. ASA and SSSA, Medison, Wisconsin.

Nelson, R.E. (1982). Carbonate and Gypsum. P. 181- 197. In Page, A. L. (ed.). Methods of Soil Analysis. Part 2. (2nd ed.). Agron. Mongor. 9. ASA and SSSA, Madison, WI.

Nimmo, J.R. and Perkins, K.S. (2002). Aggregate stability and size distribution. In: Dane, J.H., Topp, G.C. (Eds.), Methods of Soil Analysis. Part 4. Physical Methods. Soil Science Society of America, Inc., Madison, WI, pp. 317–328.

Ostovari, Y., Ghorbani-Dashtaki, S., Bahrami, H.A., Naderi, M., Dematte, J.A.M. and Kerry, R. (2016). Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran. Geomorphology, 273, 385–395.