بررسی تغییرات مکانی غلظت فلزات سنگین در رسوبات رودخانه‌ای معدن مس سونگون، آذربایجان شرقی

آبابایی ب و سهرابی ت، 1388. ارزیابی عملکرد مدل SWAT در حوضه آبریز زاینده رود. پژوهش‌های حفاظت آب و خاک، جلد 16، شماره 3، صفحه‌های 41 تا 58.

اخوان س و جودی حمزه­آباد آ، 1391. شبیه­سازی هیدرولوژیک حوضه آبریز دریاچه ارومیه. صفحه‌های 1-15. کنفرانس بین المللی دریاچه ارومیه، چالش ها و راهکارها، 18 مهرماه، دانشگاه ارومیه.

اکبری‌مجدر ح، بهره‌مند ع، نجفی‌نژاد ع و بردی‌شیخ و، 1392. شبیه‌سازی جریان روزانه رودخانه چهل‌چای استان گلستان با مدلSWAT. حفاظت آب و خاک، جلد 20، شماره 3، صفحه‌های 253 تا 259.

بی­نام، 1391. به­هنگام­سازی طرح جامع حوضه­های مازندران و دریاچه ارومیه،  گزارش مطالعات هواشناسی جلد یک.

جودی حمزه­آباد آ، 1392. شبیه­سازی رواناب حوضه آبریز دریاچه ارومیه با استفاده از مدل SWAT. پایان‌نامه کارشناسی ارشد، دانشکده کشاورزی، دانشگاه بوعلی سینا همدان.

نبی­زاده م، مساعدی ا، حسام م و دهقانی ا، 1391. مقایسه عملکرد مدل­های مبتنی بر منطق فازی در پیش­بینی آبدهی روزانه رودخانه لیقوان. پژوهش‌های حفاظت آب و خاک، جلد 19، شماره 1، صفحه‌های 117 تا 134.

Abbaspour KC, 2005. Calibration of hydrologic models: when is a model calibrated? Pp. 2449-12455. In: Zerger A, and Argent RM. (eds) MODSIM 2005 International Congress on Modelling and Si-mulation. Modelling and Simulation Society of Australia and New Zealand. 26-28 December, ISBN: 0-9758400-2-9.

Akhavan S, Jahangir AK, Mousavi SF, Afyuni M, Eslamian SS and Abbspour KC, 2010. Application of SWAT model to investigate nitrate leaching in Hamadan–Bahar Watershed, Iran. Agriculture, Ecosystems and Environment 139: 675–688.

Azimi M, Heshmati GhA, Farahpour M, Faramarzi M and Abbaspour KC, 2013. Modeling the impact of rangeland management on forage production of sagebrush species in arid and semi-arid regions of Iran. Ecological Modelling 250: 1– 14.

Belayneh A and Adamowski J, 2013. Drought forecasting using new machine learningmethods. Journal of Water and Land Development 18: 3–12.

Faramarzi M, Abbaspour KC, Schulin R and Yang H, 2009. Modelling blue and green water resources availability in Iran. Hydrolgical Processes 23: 486–501.

Jajarmizadeh M, Kakaei Lafdani E, Harun S and Ahmadi A, 2014. Application of SVM and SWAT Models for Monthly Streamflow Prediction, a Case Study in South of Iran. KSCE Journal of Civil Engineering. 1-13.

Hamel L, 2009. Knowledge Discovery with Support Vector Machines. Hoboken NJ. John Wiley.

Haipni A, EL-Shafie A, Najah A, Abdol Karim O, Hussain A, and Mukhlisi M, 2013. Daily forecastingof dam water levels: comparing a support vector machine (SVM) model withadaptive neuro fuzzy inference system (ANFIS). Water Resources Management 27: 3803–3823.

Karamouz M, Ahmadi A and Moridi A, 2009. Probabilistic reservoir operation using Bayesian stochastic model and support vector machine. Advance in Water Resources 32: 1588–1600.

Kuligowski R and Barros AP, 1998. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and Forecasting 13 (40): 1195-1205.

Lin GF, Chen GR, Huang PY and Ching Chong Y, 2009b. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. Journal of Hydrology 372: 17-29.

Lin GF, Chun YC and Wu CM, 2013. Typhoon flood forecasting using integrated two-stage support vector machine approach. Journal of Hydrology  486: 334–342.

Liong SY and Sivapragasam C, 2002. Flood stage forecasting with SVM. Journal of the American Wate Resources Association 38: 173-186.

Malekmohamadi I, Bazargan-Lari MR, Kerachian R, Nikoo MR and Fallahnia M, 2011. Evaluating the Efficacy of SVMs, BNs, ANNs and ANFIS in Wave Height Prediction Iman. Ocean Engineering 38: 487–497.

Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD and Veith TL, 2007. Model evaluation guideline for systematic quantification of accuracy in watershed simulation. American Society of Agricultural and Biological Engineers ISSN 0001−2351 Transactions of the ASABE 50(3): 885-900.

Nash JE and Sutcliffe JV, 1970. River flow forecasting through conceptual models. Part I –A discussion of principles. Journal of Hydrology 10: 282–290.

Neitsch SL, Arnold JG, Kiniry JR and Williams JR, 2009. Soil and water assessment tool, the oretical doucumentation. Texas Water Resources Institute Technical Report. Texas, USA.

Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A and Gousheh MG, 2011. Assessment of input variables determination on the SVM model performance using PCA. gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology 401: 177–189.

Riahi S, Pourbasheer E, Ganjali MR and Norouzi P, 2009. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine. Journal of Hazardous Materials 166(2): 853-859.

Setegn SG, 2010. Modeling hydrological and hydrodinamic prosseses in lake Tana basin, Ethiopia. KTH. TRITA-LWR PhD Thesis 1057. Royal Institute of Technology. Sweden.

Sudheer CH, Nitin ABK, Panigrahi BK and Shashi M, 2013. Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101: 18–23.

Taffese T. 2012. Physically based rainfall- runoff modeling in the Northern Ethiopian highlands: the case of mizewa watershed. the thesis for Degree of Master of Science in Water Resource Engineering. Bahir Day University. Ethiopia.

Vaghefi SA, Mousavi SJ, Abbaspour KC, Srinivasan R and Yang H, 2014. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrological Processes 28: 2018-2032.

Vapnik VN, 1998. Statistical Learning Theory. Wiley, New York.

White ED, Eston ZM, Fuka DR and Steenhuis TS, 2009. SWAT-WB theoretical documentation, Soil and Water Lab. Department of Biological and Environmental Engineerig. Technical Report. Cornell University. Ithaca NY.

Yong J, Reichert P, Abbaspour KC, Xia J and Yang H, 2008. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology 358: 1-23.

Yu PS, Chen ST and Chang IF, 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328: 704-716.

Zahrahtul AZ and Ani S, 2012. Streamflow forecasting at ungaged sites using supportvector machines. Applied Mathematical Sciences 6 (60): 3003–3014.

Zhibin H, Xiaohu W and Jun D, 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology 509: 379-386.