References
Abd-Elaty, I., Kushwaha, N.L., Grismer, M.E., Elbeltagi, A., & Kuriqi, A. (2022). Cost-effective management measures for coastal aquifers affected by saltwater intrusion and climate change.
Science of The Total Environment,
836, 155656.
doi:10.1016/j.scitotenv.2022.155656
Adnan, R.M., Petroselli, A., Heddam, S., Santos, C.A.G., & Kisi, O. (2021). Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards, 105, 2987-3011. doi: 10.1007/s11069-020-04438-2
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., & Ren, H. (2020). Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data.
Water Research,
171, 115454.
doi:10.1016/j.watres.2019.115454
Chen, X.W., & Lin, X. (2014). Big data deep learning: challenges and perspectives.
IEEE access,
2, 514-525. doi:
10.1109/ACCESS.2014.2325029
Elbeltagi, A., Al-Mukhtar, M., Kushwaha, N.L., Al-Ansari, N., & Vishwakarma, D.K. (2023). Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment. Applied Water Science, 13(2), 42. doi:10.1007/s13201-022-01846-6
Erdebilli, B., & Devrim-İçtenbaş, B. (2022). Ensemble voting regression based on machine learning for predicting medical waste: a case from Turkey.
Mathematics, 10(14), 2466.
doi:10.3390/math10142466
Feng, K., & Tian, J. (2021). Forecasting reference evapotranspiration using data mining and limited climatic data. European Journal of Remote Sensing, 54(sup2), 363-371. doi:10.1080/22797254.2020.1801355
Gholami, H., Mohamadifar, A., Sorooshian, A., & Jansen, J.D. (2020). Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran. Atmospheric Pollution Research, 11(8), 1303-1315. doi;10.1016/j.apr.2020.05.009
Granata, F., & Di Nunno, F. (2021). Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks. Agricultural Water Management, 255, 107040. doi:10.1016/j.agwat.2021.107040
Granata, F., Gargano, R., & de Marinis, G. (2020). Artificial intelligence-based approaches to evaluate actual evapotranspiration in wetlands.
Science of The Total Environment,
703, 135653.
doi:10.1016/j.scitotenv.2019.135653
Hinton, G.E., & Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. doi:
10.1126/science.1127647.
Khan, R.A., El Morabet, R., Mallick, J., Azam, M., Vambol, V., Vambol, S., & Sydorenko, V. (2021). Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia. Ecological Questions, 32(4), 127-133. doi:10.12775/EQ.2021.038.
Khan, N., Shahid, S., Ismail, T.B., & Wang, X.J. (2019). Spatial distribution of unidirectional trends in temperature and temperature extremes in Pakistan. Theoretical and Applied Climatology, 136, 899-913.
Kisi, O., Mansouri, I., & Hu, J.W. (2017). A new method for evaporation modeling: dynamic evolving neural-fuzzy inference system.
Advances in Meteorology,
1, 1-9.
doi:10.1155/2017/5356324.
Kushwaha, N.L., Rajput, J., Elbeltagi, A., Elnaggar, A.Y., Sena, D.R., Vishwakarma, D.K., & Hussein, E.E. (2021). Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: a case study from Northern India. Atmosphere, 12(12), 1654. doi:10.3390/atmos12121654
Kushwaha, N.L., Rajput, J., Sena, D.R., Elbeltagi, A., Singh, D.K., & Mani, I. (2022). Evaluation of data-driven hybrid machine learning algorithms for modelling daily reference evapotranspiration. Atmosphere-Ocean, 60(5), 519-540. doi:10.1080/07055900.2022.2087589
Lundberg, A. (1993). Evaporation of intercepted snow-review of existing and new measurement methods. Journal of Hydrology, 151(2-4), 267-290. doi:10.1016/0022-1694(93)90239-6
Malik, A., Tikhamarine, Y., Al-Ansari, N., Shahid, S., Sekhon, H.S., Pal, R.K., Rai, P., Pandey, K., Singh, P., Elbeltagi, A., & Sammen, S.S. (2021). Daily pan-evaporation estimation in diferent agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test.
Engineering Applications of Computational Fluid Mechanics,
15, 1075-1094.
doi:10.1080/19942060.2021.1942990
Masoner, J.R., Stannard, D.I., & Christenson, S.C. (2008). Differences in evaporation between a floating pan and class a pan on land 1. Journal of the American Water Resources Association, 44(3), 552-561. doi:10.1111/
j.1752-1688.2008.00181.x
Mohammadi, M., Forozanfard, M., & Gholami, H. (2022). Predicting pan evaporation in a hyper-arid climate using soft computing models: A Case Study of Sistan Plain, Sistan-Baluchistan, Iran. Desert Ecosystem Engineering, 11(36), 71-82. doi:10.22052/deej.2021.11.36.43.[In Persian]
Mohammadi, M., Vagharfard, H., Mahdavi Najafabadi, R., Daneshkar Arasteh, P., & Nazemosadat, M.J. (2021). Rainfall-runoff modelling of coastal watersheds near Hormuz strait using data mining. Iranian Journal of Soil and Water Research, 52(2), 313-327. doi: 10.22059/ijswr.2021.309641.668732. [In Persian]
Mohammadifar, A., Gholami, H., & Golzari, S. (2023). Stacking-and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence. Environmental Science and Pollution Research, 30(10), 26580-26595.
Mosavi, A., Sajedi Hosseini, F., Choubin, B., Taromideh, F., Ghodsi, M., Nazari, B., & Dineva, A.A. (2021). Susceptibility mapping of groundwater salinity using machine learning models.
Environmental Science and Pollution Research,
28, 10804-10817.
doi:10.1007/s11356-020-11319-5.
Naganna, S.R., Deka, P.C., Ghorbani, M.A., Biazar, S. M., Al-Ansari, N., & Yaseen, Z.M. (2019). Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water, 11(4), 742. doi:10.3390/w11040742
Parisouj, P., Mohebzadeh, H., & Lee, T. (2020). Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States. Water Resources Management, 34, 4113-4131. doi: 10.1007/s11269-020-02659-5
Rahman, A.S., Hosono, T., Quilty, J.M., Das, J., & Basak, A. (2020). Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms.
Advances in Water Resources,
141, 103595.
doi:10.1016/j.advwatres.2020.103595
Rezaie-Balf, M., Attar, N.F., Mohammadzadeh, A., Murti, M.A., Ahmed, A.N., Fai, C.M., & El-Shafie, A. (2020). Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach. Journal of Cleaner Production, 271, 122576. doi:10.1016/j.jclepro.2020.122576
Sabzevari, Y., & Ghanbarpouri, M. (2022). Evaluation of experimental and intelligent models in estimation of reference evapotranspiration: Case Study Aligodarz. Desert Ecosystem Engineering, 11(36), 17-30. doi: 10.22052/deej.2023.248181.0 [In Persian]
Salih, S.Q., Sharafati, A., Ebtehaj, I., Sanikhani, H., Siddique, R., Deo, R.C., & Yaseen, Z.M. (2020). Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments. Hydrological Sciences Journal, 65(7), 1145-1157. doi:10.1080/02626667.2020.1734813
Seyedi, S.N., Fazloula, R., Masoudian, M., & Kia, I. (2022). Evaluation the performance of different models of artificial neural network in estimating evaporation losses from pan around the Shahid Rajaei Dam Lake. Irrigation and Water Engineering, 13(2), 179-196. doi:10.22125/iwe.2022.162631
Shahabi, S., Azarpira, F., & Barzkar, A. (2020). Estimation of daily and weekly evapotranspiration using hybrid approaches of soft computing. Iranian Journal of Irrigation & Drainage, 14(5), 1550-1561. dor:20.1001.1.20087942.1399.14.5.5.6. [In Persian]
Sze, V., Chen, Y.H., Yang, T.J., & Emer, J.S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295-2329. doi:10.48550/arXiv.1703.09039
Taylor, K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of geophysical research: atmospheres, 106(D7), 7183-7192. doi:10.1029/2000JD900719
Vishwakarma, D.K., Pandey, K., Kaur, A., Kushwaha, N.L., Kumar, R., Ali, R., & Kuriqi, A. (2022). Methods to estimate evapotranspiration in humid and subtropical climate conditions.
Agricultural Water Management,
261, 107378.
doi:10.1016/j.agwat.2021.107378
Wu, L., Huang, G., Fan, J., Ma, X., Zhou, H., & Zeng, W. (2020). Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Computers and electronics in agriculture, 168, 105115. doi:10.1016/j.compag.2019.105115
Zhao, L., Xia, J., Xu, C.Y., Wang, Z., Sobkowiak, L., & Long, C. (2013). Evapotranspiration estimation methods in hydrological models.
Journal of Geographical Sciences,
23, 359-369.
doi:10.1007/s11442-013-1015-9.
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