References
Ahi, Y., Coşkun Dilcan, Ç., Köksal, D.D., & Gültaş, H. T. (2022). Reservoir evaporation forecasting based on climate change scenarios using artificial neural network model.
Water Resources Management,
37, 2607-2624.
doi:10.1007/s11269-022-03365-0
Arya Azar, N., Kardan, N., & Ghordoyee Milan, S. (2021). Developing the artificial neural network–evolutionary algorithms hybrid models (ANN–EA) to predict the daily evaporation from dam reservoirs.
Engineering with Computers,
39, 1375-1393.
doi:10.1007/s00366-021-01523-3
Ashrafzadeh, A., Ghorbani, M.A., Biazar, S.M., & Yaseen, Z.M. (2019). Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm.
Hydrological Sciences Journal,
64(15), 1843-1856.
doi:10.1080/02626667.2019.1676428
Begum, K.G. (2023). Coot bird optimization algorithm for the temperature control of continuous stirred tank reactor process.
Asia-Pacific Journal of Chemical Engineering,18(1), e2787.
doi:10.1002/apj.2787
Dong, L., Zeng, W., Wu, L., Lei, G., Chen, H., Srivastava, A.K., & Gaiser, T. (2021). Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm.
Water,
13(3), 256.
https://www.mdpi.com/2073-4441/13/3/256
Ghorbani, M.A., Kazempour, R., Chau, K.W., Shamshirband, S., & Taherei Ghazvinei, P. (2018). Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran.
Engineering Applications of Computational Fluid Mechanics,
12(1), 724-737.
doi:10.1080/19942060.2018.1517052
Goyal, M.K., Bharti, B., Quilty, J., Adamowski, J., & Pandey, A. (2014). Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11), 5267-5276. doi:10.1016/j.eswa.2014.02.047
Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W., & Zhou, H. (2019). Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions.
Journal of Hydrology,
574, 1029-1041.
doi:10.1016/j.jhydrol.2019.04.085
Khari, D., Egdernezhad, A., & Ebrahimipak, N.A. (2023). Comparison of artificial intelligence models and experimental models in estimating reference evapotranspiration (Case study: Ramhormoz synoptic station).
Water and Soil Management and Modeling,
3(2),112-124.
doi:10.22098/mmws.2022.11293.1117. [In Persian]
Malekahmadi, H., Mirzania, E., Khosravi, S., & Ebrahim Zadeh, A. (2022). Daily evapotranspiration modeling using regression and intelligent models (Case Study).
Iranian Water Research Journal, 16(1), 49-62.
doi:10.22034/iwrj.2022.10026.2338. [In Persian]
Maroufpoor, S., Bozorg-Haddad, O., & 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, 125060. doi:10.1016/j.jhydrol.2020.125060
Memarzadeh, G., & Keynia, F. (2021). A new optimal energy storage system model for wind power producers based on long short term memory and Coot Bird Search Algorithm.
Journal of Energy Storage,
44, 103401.
doi:10.1016/j.est.2021.103401
Mirzania, E., Malek Ahmadi, H., Yadegar Shahmohammadi, Y., & Ebrahimzadeh, A. (2021). Impact of wavelet on accuracy of estimated models in rainfall-runoff modeling (Case study: Sufi Chay.
Water and Soil Management and Modeling,
1(3), 67-79.
doi:10.22098/MMWS.2021.9335.1035 [In Persian]
Moazenzadeh, R., Mohammadi, B., Shamshirband, S., & Chau, K.W. (2018). Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran.
Engineering Applications of Computational Fluid Mechanics,
12(1), 584-597.
doi:10.1080/19942060.2018.1482476
Mohammadi, B., Linh, N.T.T., Pham, Q.B., Ahmed, A.N., Vojteková, J., Guan, Y., Abba, S.I., & El-Shafie, A. (2020). Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series.
Hydrological Sciences Journal,
65, 1738–1751. doi:10.1080
/02626667.2020.1758703
Naruei, I., & Keynia, F. (2021). A new optimization method based on COOT bird natural life model.
Expert Systems with Applications,
183, 115352.
doi:10.1016/j.eswa.2021.115352
Sheng, W., Li, R., Yan, T., Tseng, M.L., Lou, J., & Li, L. (2023). A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm.
Renewable Energy,
204, 493-506.
doi:10.1016/j.renene.2023.01.010
Talebi, H., Samadianfard, S., & Kamran, K.V. (2023). A novel method based on Landsat 8 and MODIS satellite images to estimate monthly reference evapotranspiration in arid and semi-arid climates. Water and Soil Management and Modeling, 3(3),180-195. doi: 10.22098/mmws.2023.12048.1198 [In Persian]
Zhao, X., Li, Y., Zhao, Z., Xing, X., Feng, G., Bai, J., Wang, Y., Qiu, Z., & Zhang, J. (2022). Prediction model for daily reference crop evapotranspiration based on hybrid algorithm in semi-arid regions of China. Atmosphere, 13(6), 922. https://www.mdpi.com/2073-4433/13/6/922
)