Adamowski, J. (2013). Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India.
Land Reclamat.,
45 (1).
DOI:
10.29252/jwmr.8.16.22
Adnan, R. M., Yuan, X., Kisi, O., Adnan, F., & Mehmood, A. (2018). Stream flow forecasting of poorly gauged mountainous watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree using climatic data from Nearby Station.
Water Resour Manag,
32, 4469- 4486.
DOI:
10.1007/s11269-018-2033-2
Aurenhammer, F., Klein, R., & Lee, D. T. (2013). Voronoi Diagrams and Delaunay Triangulations. World Scientific Publ. Co., Singapore, p. 337. DOI:
10.1142/8685
Barani, N., & Karami, A. (2019). Annual trend analysis of climate parameters of temperature and precipitation in decuple agroecology regions of Iran.
Environm. Sci.,
17(4), 75-90. DOI :
10.29252/envs.17.4.75
Bashirian, F., Rahimi D, Movahedi S., & Zakerinejad, R. (2020). Water level instability analysis of Urmia Lake Basin in the northwest of Iran.
Arab. J. Geosci.,
13(4), 1-14.
DOI: 10.1007/s12517-020-5207-1
Bigdeli, Z., Majnooni Heris, A., Delirhasannia, R., & Karimi, S. (2023). Rainfall-Runoff Modeling of Aji Chai Basin using random forest and artificial neural network models.
New Res. Sustain. Water Eng.,
1(2), 27-42.
DOI:
10.22103/nrswe.2023.20278.1013
Dibike, Y. B., Velickov, S., Solomatine, D. P., & Abbott, M. B. (2001). Model induction with support vector machines: introduction and applications.
J. Comput. Civ. Eng.,
15(3), 208-216. DOI:
10.1061/(ASCE)0887-3801(2001)15:3(208)
Fazli Fard, P., Sheikh, V., Sadoddin, A., & Hessari, B. (2019). Study of trends in precipitation and stream flow of the lake Urmia Basin during the past four decades. Water Soil Sci., 29(4), 27-41. [In Persian].
Friedman, N., Goldszmidt, M., & Wyner, A. (1999). Data analysis with Bayesian networks: A bootstrap approach. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Burlington (MA): Morgan Kaufmann Publishers. DOI:
10.1061/(ASCE)0887-3801(2001)15:3(208)
Ghorbani, M., Azani, A., & Mahmoudi Vanolya, S. (2015). Rainfall-Runoff Modeling Using Hybrid Intelligent Models Iran.
Water Resour. Manag.,
11(2),146-150 [In Persian]. DOI:
20.1001.1.2008479.1400.52.5.18.2
Hosseini –Moghari, S. M., Araghinejad, S., Tourian, M. J., Ebrahimi, K., & Döll, P. (2020). Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and
in situ data for calibrating a global hydrological model.
Hydrol Earth Syst. Sci.,
24(4), 1939-1956. DOI:
10.5194/hess-24-1939-2020, 2020.
https://en.wikipedia.org/wiki/Tabriz
https://en.wikipedia.org/wiki/Aji_Chay
Hussain, D., & Khan, A. A. (2020). Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan.
ESIN,
13, 939-949. DOI:
10.1007/s12145-020-00450-z
Kendall, M. G. (1975). Rank correlation methods. fourth ed. Charles Griffin, London.
Kisi, O., & Parmar, K. S. (2016). Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution.
J. Hydrol.,
534, 104–112.
DOI: 10.1016/j.jhydrol.2015.12.014
Kumar, S., Roshni, T. and Himayoun, D. (2019). A comparison of emotional neural network (ENN) and artificial neural network (ANN) approach for rainfall-runoff modelling. C. E.
J.,
5(10), 2120-2130. DOI:
10.28991/cej-2019-03091398
Lee, S., Kim, J. C., Jung, H. S., Lee, M. J., & Lee, S. (2017). Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea.
Geomat. Nat. Hazard. Risk,
8(2), 1185-1203.
DOI:
10.1080/19475705.2017.1308971
Lorrai, M., & Sechi, M. G. (1995). Neural nets for modeling rainfall-runoff transformation.
Water Resour. Manag.,
9, 299 – 313. DOI:
10.1007/BF00872489
Mann, H. B. (1945). Non-parametric test against trend. Econom., 13, 245-259.
Nazeri Tahroudi, M., Ahmadi, F., & Khalili, K. (2017). Eveluation the trend and trend chang point of Urmia Lake Basin precipitation. Journal of water and soil (Agricultural sciences and technology),
31(2), 644-659 [In Persian]. DOI:
10.22067/JSW.V31I2.55338
Nazeri Tahroudi, M., Ahmadi, F., & Khalili, K. (2018). Impact of 30 years changing of river flow on Urmia Lake Basin.
AJCE,
2(1), 115-122.
DOI:
10.22060/AJCE.2018.14520.5481
Nourani, V., Keynezhad, M., & Makani L. (2009). Using adaptive neuro-fuzzy inference
system rainfall-runoff modeling. J. Civil Environ. Eng., 39,
75-81 [In Persian].
Phomcha, P., Wirojanagud, P., Vangpaisal, T., & Thaveevouthti, T. (2011). Suitability of SWAT model for simulating of monthly streamflow in Lam Sonthi Watershed. J. Indust. Technol., 7(2), 49- 56.
Poursalehi, F., KhasheiSiuki, A., & Hashemi, S. R. (2022). Investigating the performance of random forest algorithm in predicting water table fluctuations compared with two models of decision tree and artificial neural network (Case study: unconfined aquifer of Birjand plain).
Iran. J. Ecohydrol.,
8(4), 961-974 [In Persian].
DOI:
10.22059/IJE.2022.327263.1526
Sabouhi, R., & Soltani, S. (2008). Analysis of the climate trend in the major cities of Iran.
J. Agric. Sci. Technol.,
12(46). [In Persian]. DOI:
10.22059/JPHGR.2021.301111.1007528
Salehi Bavil, S., Zeinalzadeh, K., & Hessari, B. (2017). The changes in the frequency of daily precipitation in Urmia Lake basin, Iran.
Theor. Appl. Climatol., 1- 10.
DOI:
10.1007/s00704-017-2177-7
Seyedian, S. M., Soleimani, M., & Kashani, M. (2014). Predicting streamflow using data-driven model and time series.
Iran. J. Ecohydrol.,
1(3),167-179. [In Persian].
DOI:
10.22059/IJE.2014.54219
Shafeizadeh, M., Fathian, H., & Nikbakht Shahbazi, A. (2019). Continuous rainfall-runoff simulation by artificial neural networks based on efficient input variables selection using partial mutual information (PMI) algorithm.
Iran-Water Resour. Res.,
15(2), 144-161 [In Persian].
DOI:
20.1001.1.17352347.1398.15.2.12.1
Shekhar, S., & Xiong, H. (2007). Encyclopedia of GIS. Springer Science & Business Media, New York, USA, p. 1370.
Shiri, J., Shamshirband, S., Kisi, O., Karimi, S., Bateni, S. M., Hosseini Nezhad, S. H., & Hashemi, A. (2016). Prediction of water-level in the Urmia Lake using the extreme learning machine approach.
Water Resour. Manag.,
30, 5217-5229. DOI:
10.1007/s11269-016-1480-x
Worland, S. C., Farmer, W. H., & Kiang, J. E. (2018). Improving predictions of hydrological low-flow indices in ungauged basins using machine learning.
Environ. Model. Software,
101, 169- 182.
DOI:
10.1016/j.envsoft.2017.12.021
Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., & Lee, K. K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
. J. Hydrol.,
396, 128–138.
DOI:
10.1016/j.jhydrol.2010.11.002
Zarei, M., Zandi, R., & Naemitabar, M. (2022). Assessment of flood occurrence potential using data mining models of support vector machine, chaid and random forest (Case study: Frizi watershed).
JWMR,
13 (25) ,133-144. [In Persian].
DOI: 10.52547/jwmr.13.25.133
)