بررسی عملکرد الگوریتم جنگل تصادفی در پیش‌بینی نوسانات سطح ایستابی در مقایسه‏ با دو مدل‏ درخت تصمیم و شبکۀ عصبی مصنوعی ‌(مطالعۀ موردی: آبخوان آزاد دشت بیرجند)

[1]. Rajaee T, Zeynivand A. Modeling of groundwater level using ANN–Wavelet Hybrid model (Case Study: Sharif Abad Plain). Journal of Civil and Environmental Engineering. 2015; 44(4): 51-63. [Persian]

[2]. Mirmorsley N, Karbasi M. Comparison of the J48, Random Forest and Tree Random algorithms efficiency in predicting bed form in sandy rivers. Second Iranian National Hydrology Conference. 2017; Shahrekord University, Shahrekord, Iran. [Persian]

[3]. Rajaee T, Mirbagheri S.A. Suspended sediment model in rivers using artificial neural networks. J. Civil Engin. 2009; 21(1): 27-43. [Persian]

[4]. Rajaee T, Ebrahimi H. Application of wavelet-neural network model for forecasting groundwater level time series with non-stationary and nonlinear characteristics. J. of Water and Soil Conservation. 2016; 22(5): 99-115. [Persian]

[5]. Altunkaynak A. Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour. Manage. 2007; 21 (2): 399-408.

[6]. Li Y, Zhang Q, Yao J, Werner A.D, Li X. Hydrodynamic and hydrological modeling of the Poyang Lake catchment system in China. J. Hydrol. Eng. 2013; 19 (3): 607–616.

[7]. Li B, Yang G, Wan R, Dai X, Zhang Y. Comparison of random forests and other statistical methods for the prediction of lake water level : a case study of the Poyang Lake in China. Hydrology Research. 2016; 69–83.

[8]. Khalili Naft Chali A, Shahidi A, khashei siuki A. Comparison of Lazy Algorithms and M5 model to estimate groundwater level (Case Study: Plain Neyshabur). JWSS. 2017; 21 (3): 15-26. [Persian]

[9].  Mirhashemi S.H, Haghighat jou P, Mirzaei F, Panahi M. Using CART algorithm in predicting groundwater table fluctuations inside and outside of an irrigation system (case study: irrigating area of Qazvin). Iranian Journal of Soil and Water Research. 2018; 49(2): 385-395. [Persian]

[10]. Mohtasham M, Dehghani A.A, Akbarpour A, Meftah M, Etebari B. Oundwater level determination by using Artificial Neural Network (Case study: Birjand Aquifer). Iranian Journal of lrrigation and drainage. 2010; 4(1): 1-10. [Persian]

 

[11]. Khashei-Siuki A, Ghahraman B, Kouchakzadeh M. Comparison of ANN, ANFIS and Regression models to estimate groundwater level of Neyshaboor Aquifer. Iranian Journal of lrrigation and Drainage. 2013; 1(7): 10-22. [Persian]

[12]. Saeedi Razavi B, Arab A. Groundwater Level Prediction of Ajabshir Plain using Fuzzy Logic, Neural Network Models and Time Series. Hydrogeology. 2019; 3(2): 69-81. [Persian]

[13]. Jabalbarezi B, Malekian A. Comparison of the performance of artificial neural networks and gene expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain). Iranian Journal of Range and Desert Research. 2019; 26(2): 292-301. [Persian]

[14]. Nayak P.C, Satyaji Rao Y.R, Sudheer K.P. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management. 2006; 2(1): 77-99.

[15]. Sreekanth P.D, Geethanjali N, Sreedevi P.D, Ahmed S, Ravi Kumar N, Kamala Jayanthi P.D. Forecasting groundwater level using artificial neural networks. Current Science. 2009; 96: 933- 939.

[16]. Sun Y, Wendi D, Kim D.E, Liong S.Y. Technical note: Application of artificial neural networks in groundwater table forecasting - a case study in a Singapore swamp forest. Hydrology and Earth System Sciences. 2016; 20(4): 1405–1412.

[17]. Noruzi H, Nadiri A.A, Asgharimoghaddam A, Gharekhani M. Prediction of Transmissivity of Malikan Plain Aquifer Using Random Forest Method. Water and Soil Science. 2017; 27(2): 61-75. [Persian]

[18]. Norouzi H, Nadiri A. Groundwater Level Prediction of Boukan Plain using Fuzzy Logic, Random Forest and Neural Network Models. Journal of Range & Watershed Management. 2018; 71(3): 829-845. [Persian]

[19]. Wang X, Liu T, Zheng X, Peng H, Xin J, Zhang B. Short‑term prediction of groundwater level using improved random forest regression with a combination of random features. Applied Water Science. 2018; 8(5): 1–12.

[20]. Hamraz B.S, Akbarpour A, Pourreza Bilondi M. Assessment of parameter uncertainty of MODFLOW model using GLUE method (Case study: Birjand plain). Journal of Water and Soil Conservation. 2016; 22(6): 61-79. [Persian]

[21]. Farpoor A, Ramezani Y, Akbarpour A. Numerical Simulation of Chromium Changes Trend in Aquifer of Birjand Plain. Iranian Journal of Irrigation and Drainage. 2018; 12(5): 1203-1216. [Persian]

[22]. Breiman L. Random forests. Mach Learn. 2001; 45(1):5-32.

[23]. Kotsiantis S, Pintelas P. Combining bagging and boosting. Journal of Computational Intelligence. 2004; 1(4): 324-333.

[24]. Guo L, Chehata N, Mallet C, Boukir S. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS J Photogramm Remote Sens. 2011; 66(1): 56-66.

[25]. Rodriguez-Galiano V, Mendes M.P, Garcia-Soldado M.J, Chica-Olmo M, Ribeiro L. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of the Total Environment. 2014; 189–206.

[26]. Talebi A, Akbari Z. Investigation of ability of decision Trees model to estimate river suspended sediment (Case Study: Ilam Dam Basin). J. Sci. & Technol. Agric. & Natur. Resour. Water and Soil Sci. 2013; 17(63): 109-121. [Persian]

[27]. Fallahi M.R, Varvani H, Golian S. Precipitation prediction using tree regression model to flood control. Fifth National Conference on Watershed Management and Soil and Water Resources Management. 2012; Kerman, Iran. [Persian]

[28]. Ghafari G.A, Vafakhah M. Simulation of rainfall-runoff process using Artificial Neural Network and adaptive Neuro-Fuzzy Interface System (Case Study: Hajighoshan Watershed). Journal of Watershed Management Research. 2013; 4(8): 120-136. [Persian]

[29]. Yue, S, Wang C. Y. The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series, Water Res.Manage. 2004; 18: 201-218.

[30]. Ghodoosi1 M, Morid S, Delavar M. Comparison of detrending methods for the temperature and precipitations time series. Journal of Agricultural Meteorology. 2013; 1(2): 32-45. [Persian]

[31]. Ahmadi F, Radmanesh F. Trend Analysis of Monthly and Annual Mean Temperature of the Northern Half of Iran Over the Last 50 Years. Journal of Water and Soil. 2014; 28(4): 855-865. [Persian]