نوع مقاله : پژوهشی
نویسنده
دانشیار، گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران
چکیده
کلیدواژهها
موضوعات
[1] Maeda EE, Wiberg DA, Pellikka PKE. Estimating refrence evapotranspiration using sensing empirical models in
a region with limited data availability in Kenya. Applied Geography. 2010; 31(2011): 251-258.
[2] Bos MG, Kselik RG, Allen k, Molden DJ. Water Requirements for Irrigation and the Environment. Springer,
[3] Panahi S, Rezvanizadeh F, Samadianfard S. Evaluation and comparison of experimental methods for estimation
of reference evapotranspiration in Tabriz station. The first international conference on Iran's natural hazards and
environmental crises, solutions and challenges, 2016; 9 p. [Persian]
[4] Alizadeh A. Irrigation System Design. Ferdowsi University of Mashhad, 2002. 450pp. [Persian].
[5] Chiew FHS, Kamaladassa NN, Malano HM, MacMahon, TA. 1995. Penman- Monteith, FAO-24 reference
crop evapotranspiration and class-A pan data in Australia. Agricultural Water management, 28: 9- 21.
[6] Hashemi E, Ahmadpari H, Kohneh K. Comparison of different methods of estimating potential
evapotranspiration with FAO Penman-Monteith method (Case study: Sepidan region). Nivar. 2017; 41, 13-22.
DOI: 10.30467/nivar.2017.51886. [In Persian]
[7] Samadianfard S, Panahi S. Estimating Daily Reference Evapotranspiration using Data Mining Methods of
Support Vector Regression and M5 Model Tree. Journal of Watershed Management Research. 2018; 18(10):
157-167. [Persian].
[8] Wang Z, Xie P, Lai C, Chen X, Wu X, Zeng Z, Li J. Spatiotemporal variability of reference evapotranspiration
and contributing climatic factors in China during 1961– 2013. Journal of Hydrology. 2017; 544: 97-108.
[9] Ansari Ghojghar M, Pourgholam-Amiji M, Bazrafshan J, Liaghat A, Araghinejad S. Performance Comparison of
Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran.
Iranian Journal of Soil and Water Research. 2020; 51(8): 2051-2063. [Persian].
[10] Pai P, Hong WCA. Recurrent support vector regression model in rainfall forecasting. Hydrological Process.
2007; 21: 819-827.
[11] Kihani A, Akhundali A, Fathian H. Uncertainty analysis of parameters of SVM model for estimation of
suspended sediment load and bed in Sierra Karaj station with Monte Carlo simulation method. Iran Water and
Soil Research (Agricultural Sciences of Iran). 2021; 52(1): 195-212. [Persian].
[12] Breiman L. Application and analysis of random forests and machine learning. Journal of Water Management.
2001; 15(1): 5-32
[13] Pal M. M5 model tree for land cover classification. International Journal of Remote Sensing, 2006; 27(4): 825-
831.
[14] Javidan S, Sattari MT, Mehrabi A. Comparative analysis of estimating monthly reference evapotranspiration
using Kernel and Tree-Based data mining models versus empirical methods. Environment and Water
Engineering. 2022; 8(4): 908-922.
[15] Piri H, Mobaraki M. Investigating the effectiveness of transfer functions based on machine learning methods for
predicting reference evaporation and transpiration (Case study: Bushehr). Eco Hydrology. 2023; 10(3): 421-434.
[Persian].
[16] Seifzadeh KH, Zare Haghi D, Samadianfard S, Neyshabouri MR, Mikaeili F. Estimation of daily evaporation
from class A pan using five data mining methods (case study: tabriz synoptic station. Water and Soil Science.
2023; 33(4): 167-184. [Persian].
[17] Sattari MT, Nahrin F, Azimi V. Prediction of daily reference evapotranspiration using artificial neural network
model and M5 tree model (Case study: Bonab Station). Iranian Journal of Irrigation and Drainage. 2013; 1(7):
104-113. [Persian].
[18] Siaser H, Dindarlou A. Estimation of daily reference evaporation and transpiration using deep learning model,
random forest and decision tree (case study: Sistan plain). Iranian Water Research, 2019; 14(1): 108-99.
[19] Izadyar M, Samadianfard S, Majnooni Heris A, Sadraddini AA. Estimating Pan Evaporation in Three Different
Climates using Data-Driven. Water and Soil Science, 2024; 34(1): 113-132. [Persian].
[20] Integrated Management Plan for Iran's Coastal Areas (Process and Achievements), Deputy Director of Port
Development and Equipment, General Directorate of Coastal and Port Engineering. 2013. [Persian].
[21] Tabari H., Grismer M. E., Trajkovic S. Comparative analysis of 31 reference evapotranspiration methods under
humid conditions. Irrigation Science. 2013; 31: 107-117.
[22] Allen RG, Pereira LS, Raes D, Smith M. Crop Evapotranspiration. Guidelines for Computing Crop Water
Requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy, 1998; 300 pp.
[23] Farshadfar A. Multivariate statistical principles and methods. Publications of Razi University of Kermanshah.
[24] Azimi A, Rangzan K, Kaboulizade M, Khoramian M. Estimating of evapotranspiration using remote sensing,
artificial neural network and comparison with the experimental method (Penman-Monteith-FAO). Journal of RS
and GIS for Natural Resources. (2016); 6(4): 61-75.
[25] Burges C.J. A. tutorial on support vector machines for pattern recognition. Data mining and knowledge
discovery. 1998; 2: 121-167
[26] Norouzi Ghoshbalagh H, Nadiri A, Asghari Moghaddam A, Qarahkhani M. Comparison of the efficiency of
artificial neural networks, fuzzy logic and random forest in estimating the aquifer transfer capability of Malekan
plain. Echo Hydrology. 2018; 5(3): 739-751. [Persian]
[27] Nosrati Karizak F, Movahedi Naeni SA. Hezarjaribi A. Using Artificial Neural Networks to estimate saturated
hydraulic conductivity from easily available soil properties. Journal of Soil Management and Sustainable
Production. 2012; 2(1): 95-110. [Persian].
[28] Wosten JHM, Pachepsky Ya A, Rawls WJ. Pedotransfer functions: bridging the gap between available basic soil
data and missing soil hydraulic characteristics. Journal of Hydrology. 2001; 251: 123–150.
[29] Debeljak M, Džeroski S. Decision Trees in Ecological Modelling. In: Jopp, F., Reuter, H., Breckling, B. (eds),
Modelling Complex Ecological Dynamics. 2011; (197-209). Springer, Berlin, Heidelberg.
[30] Wang Y, Witten I.H. Inducing model trees for continuous classes. In Proceedings of the Ninth European
Conference on Machine Learning. 1997; 128-137.
[31] Breiman L. Application and analysis of random forests and machine learning. Journal of Water Management.
2001; 15(1): 5-32
[32] Siasar H, Honar T. The application of support vector machine, chaid and random forest models in estimating
daily reference transpiration evaporation in the north of Sistan and Baluchistan province. Iran Irrigation and
Drainage. 2018; 2(13): 378-388. [Persian].
[33] Joudy AR, Sattari MT. Estimation of scour depth of piers in hydraulic structures using Gaussian process
regression. Irrigation and Drainage Structures Engineering Research 2015; 16(65): 19-36. [Persian].
[34] Kuss M. Gaussian process models for robust regression, classification, and reinforcement learning. Ph. D.
dissertation, Technische Universität Darmstadt, Darmstadt, Germany. 2006.
[35] Saghebian M, Roushangar K. Prediction of total and bedform roughness coefficient in alluvial channels based
on experimental data via Gaussian process regression method. Iranian Journal of Irrigation and Drainage. 2019;
2: 437-499. [Persian].
[36] Mouneskhah V., Samadian Fard S., Mouneskhah M.H. Evaluation of Data Mining Methods and Experimental
Temperature-Radiation-Based Models in Estimating Evaporation from the Pan (Case Study: East of Urmia Lake).
Iran water and soil research. 2020; 51(9): 2348-2337.
[37] Seifzadeh KH, Zare Haghi D, Samadianfard S, Neyshabouri MR, Mikaeili F. Estimation of Daily Evaporation
from Class A Pan Using Five Data Mining Methods (Case Study: Tabriz Synoptic Station). Water and Soil
Science. 2023; 3(4): 167-184.
[38] Bidabadi M, Babazadeh H, Shiri J, Saremi A. Estimation of reference crop evapotranspiration (ET0) using
artifcial intelligence model in an arid climate with external data. Applied Water Science. 2024; 14: 3.
[39] Soltani A, Mirltifi M, Dehghani Sanij H. Estimation of reference evapotranspiration using limited
meteorological data in different climatic journal. Water and soil. 2013; 26(1): 139-149
[40] Mousavi Baighi M. Erfanian M, Sarmad M. Using minimum meteorological data to estimate reference plant
evapotranspiration and provide correction coefficient (case study: Razavi Khorasan province). Water and Soil
Journal. 2018; 23(1): 91-99
)