تاثیر پارامترهای هواشناسی و تصاویر ماهواره‌‌ای در تخمین تبخیرتعرق مرجع روزانه با استفاده از مدل جنگل تصادفی بهینه شده با الگوریتم ژنتیک

A

Allen G, Pereira S, Raes D and Smith M, 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome.

Breiman L, 2001. Random forests. Machine learning 45: 5-32.

Chia MY, Huang YF, Koo CH and Fung KF, 2020. Recent advances in evapotranspiration estimation using artificial intelligence approaches with a focus on hybridization techniques-a review. Agronomy 101:1-10

Douna V, Barraza V, Grings F, Huete A, Restrepo N and Beringer J, 2021. Towards a remote sensing data based evapotranspiration estimation in Northern Australia using a simple random forest approach. Journal of Arid Environments 191:10-13.

Hillel D and Hatfield JL, 2005. Encyclopedia of Soils in the Environment, Elsevier Amsterdam, The Netherlands.

Holland JH, 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT Press.

Holmes J, 1984. Measuring evapotranspiration by hydrological methods. Agricultural Water Management 8: 29-40.

Huang G, Wu L, Ma X, Zhang W, Fan J, Yu X, Zeng W and Zhou H, 2019. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology 574: 1029-1041.

Liu Q, Wu Z, Cui N, Zhang W, Wang Y, Hu X, Gong D and Zheng S, 2022. Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China. Atmosphere 13: 971.

Manikumari N, Murugappan A and Vinodhini G, 2017. Time series forecasting of daily reference evapotranspiration by neural network ensemble learning for irrigation system.  IOP Conference Series: Earth and Environmental Science. IOP Publishing. 17-18 March, Thanjavur, India.

Maroufpoor S, Bozorg-Haddad O and Maroufpoor E, 2020. Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology 588: 50-60.

Moser J and Suarez F, 2021. Actual evapotranspiration estimates in arid cold regions using Machine Learning Algorithms with in situ and remote sensing data. Water 136: 80-70.

Reyes Rojas LA, Moletto-Lobos I, Corradini F, Mattar C, Fuster R and Escobar C, 2021. Determining actual evapotranspiration based on machine learning and sinusoidal approaches applied to thermal high-resolution remote sensing imagery in a semi-arid ecosystem. Remote Sensing 13(20): 4105.

Rodell M, Houser P, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J and Bosilovich M, 2004. The global land data assimilation system. Bulletin of the American Meteorological Society 85: 381-394.

Samadianfard S, Kargar K, Shadkani S, Hashemi S, Abbaspour A and Safari MJ, 2021. Hybrid models for suspended sediment prediction: Optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods. Neural Computing and Applications 80: 1-19

Sattari MT, Fezie H, Samadianfard S, Falsafian K and Sal Wana E, 2021. Estimation of monthly and seasonal precipitation: A comparative study using data-driven methods versus hybrid approach. Measurement 173: 85-96.

Tyralis H, Papacharalampous G and Langousis A, 2019. A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 910:15-36.

Wang S, Lian J, Peng Y, and Chen H, 2019. Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agricultural Water Management 221: 220-230.

Yang Y, Shang S and Jiang L, 2012. Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China. Agricultural and Forest Meteorology 164: 112-122.

Ye X, Dong LA and Ma D, 2018. Loan evaluation in P2P lending based on random forest optimized by genetic algorithm with profit score. Electronic Commerce Research and Applications 32: 23-36.