باردوج، ف. و بذرافشان، ا. 1401. مدلسازی ردپای آب گندم با استفاده از مدلهای یادگیری ماشین در استان فارس. اکو هیدرولوژی. (3)9 : 689-675.
پوزش شیرازی، م. 1383. بررسی تأثیر مقادیر مختلف پتاسیم بر کارایی مصرف آب و تحمل به خشکی گیاه گوجه فرنگی در استان بوشهر. مجله علوم و کشاورزی ایران. (6) 36 :1548-1539.
Abdallah, M., Mohammadi, B., Zaroug, M.A., Omer, A., Cheraghalizadeh, M., Eldow, M.E. and Duan, Z. 2022. Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models. Journal of Hydrology: Regional Studies. 44:101259.
Amini A, Li H. 2019.Quantile regression for tree-based methods. Ann Stat 47(4):2039–2068.
Bromberg, C.L., Gazen, C., Hickey, J.J., Burge, J., Barrington, L. and Agrawal, S., 2019, December. Machine learning for precipitation nowcasting from radar images. In Proceedings of the Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada (pp. 1-4).
Breiman, L. 2001. Random forests. Machine learning. 45(1): pp.5-32. https:// doi.org/10.1023/A:1010933404324.
Chen, T. and Guestrin, C. 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
Bazarfshan, O., Yahyazadeh, M., Jamshidi, S. and Zamani, H., 2022. Spatial prioritization of tomato cultivation based on water footprint, land productivity, and economic indices. Irrigation and Drainage. 71(5): pp.1363-1378.
Chapagain, A.K. and Orr, S. 2009. An improved water footprint methodology linking global consumption to local water resources: A case of Spanish tomatoes. Journal of environmental management, 90(2), pp.1219-1228.
Choubin, B. and Malekian, A. 2017. Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ Earth Sci 76 (15): 538 [online]
Chowdhary, P., Raj, A. and Bharagava, R.N., 2018. Environmental pollution and health hazards from distillery wastewater and treatment approaches to combat the environmental threats: a review. Chemosphere. 194: 229-246.
Chapagain, A.K., Hoekstra, A.Y., Savenije, H.H. and Gautam, R. 2006. The water footprint of cotton consumption: An assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecological economics. 60(1): 186-203.
Cheng, C. L. and Kawamura, S. 2023. Toward near-zero water consumption grade labelling and evaluation benchmarks for residential buildings. Journal of Asian Architecture and Building Engineering. 22(4): 2365-2375.
Dogulu, N., López López, P., Solomatine, D.P., Weerts, A.H. and Shrestha, D.L., 2014. Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments. Hydrology & Earth System Sciences Discussions. 11(9).
Dogulu, N., López López, P., Solomatine, D.P., Weerts, A.H. and Shrestha, D.L. 2015. Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments. Hydrology and Earth System Sciences. 19(7):3181-3201.
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. 2020. A survey on ensemble learning. Frontiers of Computer Science. 14(2): 241-258. https://10.1007/s11704-019-8208-z.
Dharumarajan, S., Kalaiselvi, B., Suputhra, A., Lalitha, M., Hegde, R., Singh, S.K. and Lagacherie, P., 2020. Digital soil mapping of key GlobalSoilMap properties in Northern Karnataka Plateau. Geoderma Regional, 20, p.e00250.
Duckett, D., Feliciano, D., Martin-Ortega, J. and Munoz-Rojas, J. 2016. Tackling wicked environmental problems: The discourse and its influence on praxis in Scotland. Landscape and Urban Planning. 154: pp.44-56.
Elbeltagi, A., Deng, J., Wang, K. and Hong, Y., 2020a. Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt. Agricultural Water Management, 235, p.106080.
Elbeltagi, A., Deng, J., Wang, K., Malik, A. and Maroufpoor, S., 2020b. Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment. Agricultural Water Management. 241:106334.
Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X., Lu, X. and Xiang, Y., 2018. Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and forest meteorology. 263: 225-241.
Freeman, E.A. and Moisen, G.G., 2015. An application of quantile random forests for predictive mapping of forest attributes. In In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: US Department of Agriculture, Forest Service, Pacific Northwest Research Station. 362. (Vol. 931).
Fazeli Farsani, I., Farzaneh, M.R., Besalatpour, A.A., Salehi, M.H. and Faramarzi, M., 2019. Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed. Theoretical and Applied Climatology. 136(1): 169-184.
Hofste, R.W., Kuzma, S., Walker, S., Sutanudjaja, E.H., Bierkens, M.F., Kuijper, M., Sanchez, M.F., Van Beek, R., Wada, Y., Rodríguez, S.G. and Reig, P., 2019. Aqueduct 3.0: Updated decision-relevant global water risk indicators. World Resources Institute. 784.
Hoekstra, A.Y. and Mekonnen, M.M. 2012. The water footprint of humanity. Proceedings of the national academy of sciences. 109(9): 3232-3237.
Hoekstra, A.Y. and Chapagain, A.K. 2011. Globalization of water: Sharing the planet's freshwater resources. John Wiley & Sons.
Hoekstra, A.Y. 2003. Virtual water trade: A quantification of virtual water flows between nations in relation to international crop trade. In Proc. the International Expert Meeting on Virtual Water Trade 12, Delft, 2003.
Harshadeep, N. R. and Young, W. 2020. Disruptive technologies for improving water security in large river basins. Water. 12(10): 2783.
Hindiyeh, M., Albatayneh, A., Altarawneh, R., Jaradat, M., Al-Omary, M., Abdelal, Q., ... and Jeguirim, M. 2021. Sea level rise mitigation by global sea water desalination using renewable-energy-powered plants. Sustainability. 13(17): 9552.
Hoosain, M. S., Paul, B. S., Doorsamy, W. and Ramakrishna, S. 2023. The influence of circular economy and 4IR technologies on the climate–water–energy–food Nexus and the SDGs. Water, 15(4), 787.
Koenker, R. and Hallock, K.F., 2001. Quantile regression. Journal of economic perspectives. 15(4): 143-156.
Hofste, R.W., Kuzma, S., Walker, S., Sutanudjaja, E.H., Bierkens, M.F., Kuijper, M., Sanchez, M.F., Van Beek, R., Wada, Y., Rodríguez, S.G. and Reig, P. 2019. Aqueduct 3.0: Updated decision-relevant global water risk indicators. World Resources Institute. 784.
Hoekstra, A.Y., 2008. Water neutral: reducing and offsetting the impacts of water footprints, Value of Water Research Report Series No. 28. Delft, Netherlands: UNESCO-IHE. Recuperado em, 10.
Koenker, R. 2005. Quantile regression (Vol. 38). Cambridge university press.
Kasraei, B., Heung, B., Saurette, D.D., Schmidt, M.G., Bulmer, C.E. and Bethel, W. 2021. Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning. Environmental Modelling & Software. 144: 105139.
Lenzen, M., Moran, D., Bhaduri, A., Kanemoto, K., Bekchanov, M., Geschke, A. and Foran, B., 2013. International trade of scarce water. Ecological Economics. 94: 78-85.
Lovarelli, D., Bacenetti, J. and Fiala, M. 2016. Water Footprint of crop productions: A review. Science of the total environment. 548: 236-251.
Luan, X.B., Yin, Y.L., Wu, P.T., Sun, S.K., Wang, Y.B., Gao, X.R. and Liu, J., 2018. An improved method for calculating the regional crop water footprint based on a hydrological process analysis. Hydrology and Earth System Sciences. 22(10):5111-5123.
Mekonnen, M.M. and Gerbens-Leenes, W. 2020. The water footprint of global food production. Water. 12(10): 2696.
Meinshausen, N. and Ridgeway, G., 2006. Quantile regression forests. Journal of machine learning research, 7(6). Mahato, A., Upadhyay, S., & Sharma, D. (2022). Global water scarcity due to climate change and its conservation strategies with special reference to India: A review. Plant Archives (09725210), 22(1).
Pellicer-Martínez, F. and Martínez-Paz, J.M. 2016. The Water Footprint as an indicator of environmental sustainability in water use at the river basin level. Science of the Total Environment. 571: 561-574.
Papacharalampous, G., Tyralis, H., Langousis, A., Jayawardena, A.W., Sivakumar, B., Mamassis, N., Montanari, A. and Koutsoyiannis, D., 2019. Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms. Water. 11(10): 2126.
Patidar, V.K., Wadhvani, R., Shukla, S., Gupta, M. and Gyanchandani, M., 2023, February. Quantile regression comprehensive in machine learning: a review. In 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE.
Rahmati, O., Falah, F., Dayal, K.S., Deo, R.C., Mohammadi, F., Biggs, T., Moghaddam, D.D., Naghibi, S.A. and Bui, D.T., 2020. Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the total environment. 699:134230.
Ruiz-Aĺvarez, M., Gomariz-Castillo, F. and Alonso-Sarria, F., 2021. Evapotranspiration response to climate change in semi-arid areas: Using random forest as multi-model ensemble method. Water. 13(2): 222.
Tao, M., Zhang, T., Xie, X. and Liang, X., 2023. Water footprint modeling and forecasting of cassava based on different artificial intelligence algorithms in Guangxi, China. Journal of Cleaner Production. 382: 135238.
Van Loon, A.F. and Van Lanen, H.A. 2013. Making the distinction between water scarcity and drought using an observation‐modeling framework. Water Resources Research. 49(3): 1483-1502.
Vaysse, K. and Lagacherie, P. 2017. Using quantile regression forest to estimate uncertainty of digital soil mapping products. Geoderma. 291: 55-64.
Wang, L., Kisi, O., Hu, B., Bilal, M., Zounemat‐Kermani, M. and Li, H., 2017. Evaporation modelling using different machine learning techniques. International Journal of Climatology. 37: 1076-1092.
Xu, K., Yang, D., Xu, X. and Lei, H., 2015. Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China. Journal of Hydrology. 527: 630-640.
Zamani, H., Pakdaman, Z., Shakari, M., Bazrafshan, O. and Jamshidi, S. 2025. Enhancing drought monitoring with a multivariate hydrometeorological index and machine learning-based prediction in the south of Iran. Environmental Science and Pollution Research. 32(9): 5605-5627.
Zhang, S., Wang, Y., Zhang, Y., Wang, D. and Zhang, N. 2020. Load probability density forecasting by transforming and combining quantile forecasts. Applied energy. 277:115600.