Ahmadi, F., Aysham, S., Khalili, K., & Javad bemanesh., A. (2015). Application of ANFIS and GP Models to Estimate Monthly Reference Crop Evapotranspiration in Northwest of Iran. Journal of Water Research in Agriculture, 29(2), 235-247.
Ahmadi, M., Kaviani, A., Ramezani Etedali, H., & Tavakoli, A. (2023). Simulation of Rained Wheat Yield Using Drought Indices by Employing Artificial Neural Network, Random Forest and Support Vector Regression Case Study, Saqqez City. Iran-Water Resources, 194, 95-109.
Anaraki, V., Mousavi, M.,
Mousavi, F., Farzin ,S., & karami, h. (2020). Introducing a Nonlinear Model Based on Hybrid Machine Learning for Modeling and Prediction of Precipitation and Comparison with SDSM Method Cases Studies, Shahrekord, Barez, and Yasuj. Iranian Journal of Soil and Water Research, 5(12), 325-339.
Ansari Ghojghar, M. (2023). Development of strategic wheat crop prediction toolkit using machine learning algorithms to reduce food security risks case study, Alborz province. Iranian Journal of Soil and Water Research, 53(10), 2277-2294.
Arumugam, A. (2017). A predictive modeling approach for improving paddy crop productivity using data mining techniques. Turkish. Journal of Electrical Engineering and Computer Sciences, 25(6), 4777-4787.
Ayoubi, SH.A., & Jalalian, A. (2010). Land Evaluation Agricultural and Natural Resources Second Edition, Isfahan University of Technology Publication Center, Isfahan, Iran, 385 (In Persian).
Baba ali, H., Ramak, Z., & Sepahv, R. (2017). The Effectiveness of Genetic Planning Model in rainfall-runoff Simulation process. Journal of Water Science Engineering.,7(18), 83-94.
Babaei, F., Vaezi, A., Teheri, M., Zarrinabadi, E., & Eslami, F. (2016). Development a regression relationship between rainfed wheat yield and soil properties in a semiarid region, Zanjan Province. Iranian Journal of Soil and Water Research, 46(4), 715-725.
Badde, D.S., Gupta, A.k., & Patki, V.K. (2013). Cascade and Feed Forward Back Propagation Artificial Neural Network Models for Prediction of Compressive Strength of Ready Mix Concrete. Journal of Mechanical and Civil Engineering IOSR-JMCE, 1-6.
Bahmani, M., Mohammadi, J., Esfandiarpour Borouleni, I., & Mottaghian, H.R. (2020). Assessment of relationship between Rose yield and soil and topography properties by using multivariate regression, artificial neural network and structure equation modeling. Applied Soil Research, 8(2), 142-157.
Breiman, L., Friedman, J., Olshen, R., and Stone, Ch. (1984). Classification and regression trees. Boca Raton, Chapman & Hall/CRC.
Brown, ME., Lary, DJ., Vrieling, A., Stathakis, D., & Mussan, H. (2008). Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. The International Journal of Remote Sensing, 29, 7141–7158.
Cox, M.S., Gerard, P.D., and Abshire, M.J. (2006). Selected soil properties variability and their relationships with yield in three Mississippi fields, Soil science, 171(7), 541-551.
Dahmardeh, A.R., Shahriari, A., Pahlavan-Rad M., Shabani, A., & Ghorbani, M. (2021). Modeling wheat yield using some soil properties at the field scale Case study, Sistan dam research farm, university of Zabol. Agricultural Engineering Scientific Journal of Agriculture, 44(1).
Diaz-Ramirez, J. N W. H., McAnally, J. & Martin, L. (2011). Analysis of hydrological processes applying the HSPF model in selected watersheds in Alabama, Mississippi, and Puerto Rico. Applied Engineering in Agriculture, 27 (6), 937-954.
Emamgholizadeh, S., Parsaeian, M., & Baradaran, M. (2015). Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy, 68, 89-96.
Emami, S., Dehghanisanij ,H., & Rezaie, M. (2021). Application of artificial neural network models in estimating nectarine crop yield under two-sided furrow irrigation. Water and Soil Management and Modeling, 1(2), 47-59.
Eslami, R., Azarnoosh, M., Kialashki, A., & Kazemnejad, F. (2021). Assessing the Probability of Forest Fire Occurring using Dong Model, Artificial Neural Network and K Nearest Neighbors in Babolrood Basin, Mazandaran. Ecology of Iranian Forest, 9(17),185-195.
Fang, X. (2009). Are you becoming a diabetic a data mining approach. Sixth International Conference on Fuzzy Systems and Knowledge Discovery. Tianjin, China, IEEE, 18-22.
Ghorbani, M. A., Kisi, O., & Aalinezhad, M. (2010). A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods. Applied Mathematical Modelling, 34, 4050–4057.
Gomrokchi, A.U., Baghani, J., & Abbasi, F. (2022). Evaluating the Capability of Data Mining Models in Predicting Irrigated Wheat Yield in Iran. Journal of Water and Soil, 35(2), 189-202.
Hafezi, N., Bahrami, H., Seikh Davoodi,M., & Alavi, E. (2021). Hybrid Artificial Neural Network with Meta-heuristic Algorithms for Predicting Sugarcane Yield, Iranian Journal of Biosystems Engineering,51(3), 515-526.
Hattamvelli, R. (2011). Data Mining Algorithms, First Edition. Alpha Science International. 8, 274-290.
Jafari, M.M., Liaghat, A., & Mohtasham, A. (2023). Comparison and Evaluation of LM, BR and GD Algorithms of Artificial Neural Network in Estimating Rainfed Wheat Yield Based on Meteorological Parameters Case Study, Kermanshah Province. Iranian Journal of Irrigation and Drainage, 31(7), 529-544.
Jagtap SS., Lall U., Jones JW., Gijsman AJ., & Ritchie JT. (2004). Dynamic nearest-neighbor method for estimating soil water parameters.Trans. ASAE. 4(7),1437–1444.
Kia. 2014. Soft computing in Matlab. Kian University Pressin persian.
Kumar, M., Raghuwanshi, N.S., Wallender, W.W., & Pruitt, W.O. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 12(84), 224-233.
Lukas, F., Zuzana, S., Maria, D., Beata, H., & Tatian, P. (2015). Application of Neural Network Models in Modelling Economic Time Series with Non-Constant Volatility. Business Economics and Management. Conference BEM2015, Procedia Economics and Finance. 3(4), 600–607.
Mesri, S., Ghorbani Dashtaki, Sh., Shirani, H., Kamkar Rohani, A., & Motaghian, H.R. (2020). Separating Effective Soil Properties on Moisture Characteristic Curve Using Decision tree. Journal of Soil Management and Sustainab, l10(3).
Monjezi, N. (2020). Diagnose & Prioritizing of Effective Managerial and Executive Factors on Water Productivity in Sugarcane Production and Providing Practical Solutions to Increase It. Iranian Journal of Biosystems Engineering, 5(11), 99-112.
Moodi,Y., Eslami, E., Mousavi, R., Dizangian, B., & Mirshekar, H. (2022). Applying neural networks for estimating the compressive strength of confined circular concrete columns with FRP sheets. Journal of Structural and Construction Engineering, 9(5), 58-77.
Modaresi, F., Ebrahimi, K., & Araghinejad, Sh. (2020). Ranking Evaluation of Data-driven and Conceptual Modelling of Rainfall- Runoff Process in Monthly Time Scale. Iranian Journal of Irrigation and Water Engineeringnm,48(4).
Mohammadi, J. (2018). Pedomining, Volume 9, Structure Equation Modeling. Pelk Publishing, 396 (In Persian).
Parviz, L., Ahadi, R., & Seyed Milani, F. (2022). Genetic algorithm application for soil pH spatial distribution estimation with geostatistics and gene expression programming combination. Applied Soil Research,10(3), 117-129.
Pourmohamadali, B.,
Salehi,M.H.,
Hosseinifard, S.J.,
Shirani, H
.,& Esfandiarpour Borujeni, I . (2018. Pistachio yield prediction using multiple linear regression and artificial neural network A Case Study, Rafsanjan and Anar regions, Kerman Province. journal of Agricultural Engineering,40(2), 71-78.
Salajegheh, A., Fathabadi, A., & Gholami, H. (2010). Predict river discharge using the nearest neighbor. 5th national conference on science and management engineering Iran. Gorgan University of Agricultural Sciences and Natural Resources (In Persian).
Sadr, S., & Eslami, M. (2021). Determination of effective weather variables on pistachio yield using C and R decision tree algorithm, Journal of Agricultural Meteorology, 9(1), 53-62.
Sattari, MT., Rezazazadeh Joudi, A., & Kusiak, A. (2015). Estimation of water quality parameters with data-driven models. American Water Works Association,108(4), 232-239.
Sharafi, M., Samadianfard, S., & Hashemi, S. (2021). Monthly rainfall Forecasting using genetic programming and support vector machine. Journal of Rainwater Catchment Systems, 8 (4) ,63-71
Sivaneasan, B., Yu C.Y., & Goh, K.P. (2017). Solar Forecasting Using ANN with Fuzzy Logic Pre- Processing, Energy Procedia, 143,727-732.
Soltani, A., Gorbani, MA., Fakheri Fard, A., Darbandi, S., & Farsadizadeh, D. (2010) Genetic Programming and Its Application in Rainfall-Runoff Modeling. Water and soil science.1,20.
Tashakkori, F., Mohammadi Torkashvand, A., Ahmadi, A., & Esfandiari M. (2021). Prediction of Saffron Yield based on Soil Properties Using Regression and Artificial Neural Network Models in the Vamenan Region of the Golestan Province. Saffron Agronomy and Technology, 92, 159-175.
Xindung, W., and Kumar, V. (2009). Top Ten Algorithm in Data Mining, First Edition, Taylor & Francis Group, USA.
Yue, J., Feng, H., Yang, G., & Li, Z. (2018). A comparison of regression techniques for estimation of aboveground winter wheat biomass using near-surface spectroscopy. Remote Sensing, 101(66).
Zahiri, A., & Azamathulla, H. (2014). Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Computing and Applications, 24 ,413-420.
Zheng, K. G. Q. L. J. Z. J. X. W. M. (2017). Applications of support vector machine and improved k-Nearest Neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear. 1st International Conference on Electrical Materials and Power Equipment, Xian, Peppler R China.
