بهینه‏ سازی مدل دراستیک با استفاده از ماشین بردار پشتیبان و شبکۀ عصبی مصنوعی به‌منظور ارزیابی آسیب ‏پذیری ذاتی آبخوان دشت اردبیل

1[. فرج‏زادۀ اصل، منوچهر؛ محمدی، عثمان؛ 1390، «پهنه‏بندی آسیب‏پذیری آب‏های زیرزمینی با کمک الگوریتم‏های فازی عصبی (مطالعۀ موردی: استان زنجان)»، سنجش از دور و GIS ایران، سال سوم، شمارۀ اول، ص 1-18.

]2[. کرد، مهدی؛ 1393، «مدل‌سازی عددی آبخوان دشت اردبیل و مدیریت آن با استفاده از بهینه‏سازی برداشت آن»، رساله دکتری، دانشگاه تبریز.

]3[. ندیری، عطاالله؛ 1392، «مقایسه کارایی مدل‌های عددی و هوش مصنوعی در مدیریت آبخوان‌ها (مطالعۀ موردی: دشت تسوج)»، رسالۀ دکتری، دانشگاه تبریز.

[4]. Al-Abadi, Alaa M; Al-Shamma’a, Ayser M; Aljabbari, Mukdad H; 2014, A GIS-based DRASTIC model for assessing intrinsic groundwater vulnerability in northeastern Missan governorate, southern Iraq, Appl Water Sci.

[5]. Aller, Linda; Bennett, Truman; Lehr, Jay.H; Petty, Rebecca.; and Hackett, Glen;1987, DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings, EPA 600/2-87-035. U.S. Environmental Protection Agency, Ada, Oklahoma.

[6]. Antonakos, Andreas.K; and Lambrakis, Nikolaos. I; 2007, Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates based on the drastic model, an example from NE Korinthia, Greece, Journal of Hydrology, pp. 288– 304.

[7]. 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.

[8]. Asefa, Tirusew; Kemblowski, Mariush; McKee,Mac; Khalil, Abedalrazq; 2006, Multi-time scale stream flow predictions: The support vector machines approach, Journal of Hydrology, 318, pp. 7–16.

[9]. Babiker,Insaf, S; Mohamed, Mohamed, A.A; Tetsuya, Hiyama;and Kikuo, Kato; 2005, A GIS-basedDRASTIC model for assessing aquifer vulnerability in KakamigaharaHeights, Gifu Prefecture, central Japan, Sci Total Environ, vol 354, pp.127–140.

[10]. Barzegar, Rahim; Asghari Moghaddam, Asghar; Baghban, Hamed; 2015, A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran, Stoch Environ Res Risk Assess.

[11]. Behzad, Mohsen; Asghari, Keyvan; and Coppola, Jr,Emery.A; 2010, Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction. J. Comput. Civ. Eng, vol 24, pp. 408-413.

[12]. Chen, Bo.Juen; Chang, Ming.Wei; and Lin, Chih.Jen;2004, Load forecasting using Support Vector Machines: A study on EUNITE Competition 2001, IEEE Transactions on Power Systems, 19(4) ,pp. 1821–1830.

[13]. Chitsazan, Nima ; Nadiri, Ata Allah; Tsai, Frank, T-C, 2015, Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging, Journal of Hydrology, 528, pp. 52-62.

[14]. Cortes, Corinna; and Vapnik, Vladimir; 1995, Support-vector networks. Machine learning, 20: 3, pp. 273-297.

[15]. Cristianini, Nello; and Shawe-Taylor, John; 2000, An Introduction to Support VectorMachines. Cambridge University Press, New York, USA.

[16]. Dibike, Yonas, B; Velickov, Slavco; Solomatine, Dimitri; and Abbot, Michael, B; 2001, ModelInduction with Support Vector Machines-Introduction and Applications. J. Comp. Civil Engin. ASCE, vol 15, pp. 208-216.

[17]. Dixon, Barnali; 2005a, Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis, Journal of Hydrology, vol 309, pp. 17-38.

[18]. Dixon, Barnali; 2009, A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N, Hydrogeology Journal, vol 17, pp. 1507–1520.

[19]. Fausett, Laurene; 1994, Fundamentals of neural network. Prentice Hall, Englewood Cliffs, N. J. 461 Pages.

[20]. Fijani, Elham; Nadiri, Ata. Allah; Asghari, Moghaddam, Asghar; Tsai, Frank, T-C; and Dixon, Barnali; 2013, Optimization of DRASTIC Method by Supervised Committee Machine Artificial Intelligence to Assess Groundwater Vulnerability for Maragheh-Bonab Plain Aquifer, Iran, Journal of hydrology, vol 530, pp. 89-100.

[21]. Haykin, Simon; 1994, Neural networks: a comprehensive foundation,Macmillan College Publishing, New York.

[22]. Hong, Wei, Chiang; 2011, Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm, Neurocomputing, Vol 74, pp. 2096-2107.

[23]. Jovanovic, N. Z; Adams S; Thomas A; Fey M; Beekman H. E; Campbell R; Saayman I; Conrad J; 2006, Improved DRASTIC method for assessment of groundwater vulnerability to generic aqueousphase contaminants, WIT Transactions on Ecology and the Environment, Vol 92, waste Management and the Environment III, pp. 393–402.

[24]. McCulloch, Warren, S; Pitts, Walter; 1943, A logic calculus of the ideas immanent in nervousactivity. Bulletin of Mathematical Biophysics, vol 5, pp. 115-133.

[25]. McLay, C.D.A., Dragten, R., Sparling, G., and Selvarajah, N., 2001, Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches, Environmental Pollutants, vol 115, pp. 191-204.

[26]. Mohammadi, Kourosh; Niknam, Ramin;and Majd, Vahid, Johari; 2009, Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran-Karaj aquifer, Iran, Environ Geol, vol 58, pp.437–446.

[27]. Mousavi, S.F. Amiri, M.J. Gohari, A.R. and Afyuni, M. 2011, Estimation of Nitrate Concentration Using Fuzzy Regression Method and Support Vector Machines, World Applied Sciences Journal, 12 (6), pp. 774-782.

[28]. Nadiri, Ata Allah; Fijani, Elham; Asghari Moghaddam, Asghar; 2013, Supervised committee machine with artificial intelligence for prediction of fluoride concentration, IWA Publishing.

[29]. Panagopoulos, George; Antonakos, Andreas; and Lambrakis, Nicolaos; 2005, Optimization of DRASTIC model for groundwater vulnerability assessment, by the use of simple statistical methods and GIS, Hydrogeology Journal.

[30]. Raghavendra, Sujay; and Chandra, Deka, Paresh; 2014, Support vector machine applications in the field of hydrology: A review, Elsevier, Applied Soft Computing,vol 19, pp. 372-386.

[31]. Secunda, S., Collin, M.L., and Melloul, A.J., 1998, Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon region, Journal of Environmental Management, vol 54, pp. 39-57.

[32]. Seifi, Akram; 2010, Developing of expert system to prediction of dailyevapotranspiration by support vector machine and compare results to ANN,ANFIS and experimental method. M.Sc. Thesis, Department of WaterEngineering, Tarbiat Modarres University, Tehran, Iran (In Persian).

[33]. Shabri, Ani; and Suhartono; 2012, Streamflow forecasting using least-squares support vector machines, Hydrological Sciences journal, 57(7), pp. 1275-1293.

[34]. Suykens, Johan, A, K; Van, Gestel, Tony; Brabanter, Jos, De; De, Moor, Bart; Vandewalle, Joos; 2002, in:Least Squares Support Vector Machines, World Scientific Publishing, Singapore.

[35]. Tayfur, Gokmen; Nadiri, Ata Allah; Asghari Moghaddam, Asghar; 2014, Supervised intelligent committee machine method for hydraulic conductivity estimation, Water resources management, 28 (4), pp. 1173-1184.

[36]. Vrba, Jiri; and Zoporozec, Alexander; 1994, Guidebook on mapping groundwater vulnerability, International Contributions to Hydrogeology, Verlag Heinz Heise GmbH and Co, KG.

[37]. Yin, Jiajian;2011, LogP prediction for blocked tripeptides with amino acids descriptors (HMLP) by multiple linear regression and support vector regression, Procedia Environmental Sciences, vol 8, pp. 173–178.