1. A.K. Misra, Climate change and challenges of water and food security, Int. J. Sustainable Built Environ., 3 (2014) 153–165.
  2. F. Kaviari, M. Saadi Mesgari, E. Seidi, H. Motieyan, Simulation of urban growth using agent-based modeling and game theory with different temporal resolutions, Cities, 95 (2019) 102387, doi: 10.1016/j.cities.2019.06.018.
  3. S.D. Nerantzaki, D. Efstathiou, G.V. Giannakis, M. Kritsotakis, M.G. Grillakis, A.G. Koutroulis, I.K. Tsanis, N.P. Nikolaidis, Climate change impact on the hydrological budget of a large Mediterranean island, Hydrol. Sci. J., 64 (2019) 1190–1203.
  4. A. Rossati, Global warming and its health impact, Int. J. Occup. Environ. Med., 8 (2017) 7–20.
  5. Y. Tramblay, M.C. Llasat, C. Randin, E. Coppola, Climate change impacts on water resources in the Mediterranean, Reg. Environ. Change, 20 (2020 83, doi: 10.1007/s10113-020-01665-y.
  6. J. Xia, Q.-Y. Duan, Y. Luo, Z.-H. Xie, Z.-Y. Liu, X.-G. Mo, Climate change and water resources: case study of Eastern Monsoon Region of China, Adv. Clim. Change Res., 8 (2017) 63–67.
  7. Y.S. Getahun, M.-H. Li, I.-F. Pun, Trend and change-point detection analyses of rainfall and temperature over the Awash River basin of Ethiopia, Heliyon, 7 (2021) e08024, doi: 10.1016/j.heliyon.2021.e08024.
  8. R. Mahmood, S. Jia, W. Zhu, Analysis of climate variability, trends, and prediction in the most active parts of the Lake Chad basin, Africa, Sci. Rep., 9 (2019) 6317, doi: 10.1038/s41598-019-42811-9.
  9. P.-A. Versini, L. Pouget, S. McEnnis, E. Custodio, I. Escaler, Climate change impact on water resources availability: case study of the Llobregat River basin (Spain), Hydrol. Sci. J., 61 (2016) 2496–2508.
  10. S. Eslamian, F. Eslamian, Disaster Risk Reduction for Resilience: Climate Change and Disaster Risk Adaptation, Springer Nature, Cham, 2023.
  11. S. Tong, H.L. Berry, K. Ebi, H. Bambrick, W. Hu, D. Green, E. Hanna, Z. Wang, C.D. Butler, Climate change, food, water and population health in China, Bull. World Health Organ., 94 (2016) 759–765.
  12. A.J. McMichael, R.E. Woodruff, S. Hales, Climate change and human health: present and future risks, Lancet, 367 (2006) 859–869.
  13. J.M. Balbus, A.B.A. Boxall, R.A. Fenske, T.E. McKone, L. Zeise, Implications of global climate change for the assessment and management of human health risks of chemicals in the natural environment, Environ. Toxicol. Chem., 32 (2013) 62–78.
  14. X. Wang, J. Zhang, V. Babovic, Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique, Ecol. Indic., 66 (2016) 428–439.
  15. N. Mujere, W. Moyce, Climate Change Impacts on Surface Water Quality, in: Hydrology and Water Resource Management: Breakthroughs in Research and Practice, IGI Global, 2018, pp. 97–115.
  16. I. Delpla, A.-V. Jung, E. Baures, M. Clement, O. Thomas, Impacts of climate change on surface water quality in relation to drinking water production, Environ. Int., 35 (2009) 1225–1233.
  17. H.K. Moghaddam, A. Rajaei, Z. Rahimzadeh kivi, H.K. Moghaddam, Prediction of qualitative parameters concentration in the groundwater resources using the Bayesian approach, Groundwater Sustainable Dev., 17 (2022) 100758, doi: 10.1016/j.gsd.2022.100758.
  18. X. Wang, J. Zhang, V. Babovic, K.Y.H. Gin, A comprehensive integrated catchment-scale monitoring and modelling approach for facilitating management of water quality, Environ. Modell. Software, 120 (2019) 104489, doi: 10.1016/j.envsoft.2019.07.014.
  19. X. Li, A. Meshgi, X. Wang, J. Zhang, S.H.X. Tay, G. Pijcke, N. Manocha, M. Ong, M.T. Nguyen, V. Babovic, Three resampling approaches based on method of fragments for daily-to-subdaily precipitation disaggregation, Int. J. Climatol., 38 (2018) e1119–e1138.
  20. N. Chokkavarapu, V.R. Mandla, Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation, SN Appl. Sci., 1 (2019) 1698,
    doi: 10.1007/s42452-019-1764-x.
  21. W. Fang, Q. Xue, L. Shen, V.S. Sheng, Survey on the application of deep learning in extreme weather prediction, Atmosphere, 12 (2021) 661, doi: 10.3390/atmos12060661.
  22. X. Li, V. Babovic, A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach, Clim. Dyn., 52 (2019) 2247–2267.
  23. N. Peleg, S. Fatichi, A. Paschalis, P. Molnar, P. Burlando, An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Syst., 9 (2017) 1595–1627.
  24. H. Sanikhani, O. Kisi, B. Amirataee, Impact of climate change on runoff in Lake Urmia basin, Iran, Theor. Appl. Climatol., 132 (2018) 491–502.
  25. P.B. Parajuli, P. Jayakody, G.F. Sassenrath, Y. Ouyang, Assessing the impacts of climate change and tillage practices on stream flow, crop and sediment yields from the Mississippi River Basin, Agric. Water Manage., 168 (2016) 112–124.
  26. Z. Hassan, S. Shamsudin, S. Harun, Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature, Theor. Appl. Climatol., 116 (2014) 243–257.
  27. X. Li, K. Zhang, V. Babovic, Projections of future climate change in Singapore based on a multi-site multivariate downscaling approach, Water, 11 (2019) 2300, doi: 10.3390/w11112300.
  28. L. Mba, P. Meukam, A. Kemajou, Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region, Energy Build., 121 (2016) 32–42.
  29. A. Sharafati, S.B. Haji Seyed Asadollah, D. Motta, Z.M. Yaseen, Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis, Hydrol. Sci. J., 65 (2020) 2022–2042.
  30. S. Marabi, M. Hafezparast, Quantitative qualitative prediction of Khorramrud River discharge due to climate change with Neurosolution model and support vector regression, Irrig. Water Eng., 12 (2021) 291–313.
  31. N.A. Mohammed, A. Al-Bazi, An adaptive backpropagation algorithm for long-term electricity load forecasting, Neural Comput. Appl., 34 (2022) 477–491.
  32. D. Niu, F. Wu, S. Dai, S. He, B. Wu, Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network, J. Cleaner Prod., 290 (2021) 125187, doi: 10.1016/j.jclepro.2020.125187.
  33. S. Emamgholizadeh, H. Kashi, I. Marofpoor, E. Zalaghi, Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models, Int. J. Environ. Sci. Technol., 11 (2014) 645–656.
  34. N.M. Gazzaz, M.K. Yusoff, A.Z. Aris, H. Juahir, M.F. Ramli, Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors, Mar. Pollut. Bull., 64 (2012) 2409–2420.
  35. N.M. Gazzaz, M.K. Yusoff, M.F. Ramli, H. Juahir, A.Z. Aris, Artificial neural network modeling of the water quality index using land use areas as predictors, Water Environ. Res., 87 (2015) 99–112.
  36. K. Sulaiman, L.H. Ismail, M.A.M. Razi, M.S. Adnan, R. Ghazali, Water quality classification using an artificial neural network (ANN), IOP Conf. Ser.: Mater. Sci. Eng., 601 (2019) 012005,
    doi: 10.1088/1757-899X/601/1/012005.
  37. M. Al-Mukhtar, F. Al-Yaseen, Modeling water quality parameters using data-driven models, a case study Abu-Ziriq marsh in south of Iraq, J. Hydrol., 6 (2019) 24, doi: 10.3390/hydrology6010024.
  38. A.K. Kadam, V.M. Wagh, A.A. Muley, B.N. Umrikar, R.N. Sankhua, Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India, Model. Earth Syst. Environ., 5 (2019) 951–962.
  39. N. Jafarzadeh, S. Ahmad Mirbagheri, T. Rajaee, A. Danehkar, M. Robati, Using artificial intelligent to model predict the biological resilience with an emphasis on population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran, J. Environ. Health Sci. Eng., 20 (2022) 123–138.
  40. J. Aazami, N. KianiMehr, A. Zamani, Ecological water health assessment using benthic macroinvertebrate communities (case study: the Ghezel Ozan River in Zanjan Province, Iran), Environ. Monit. Assess., 191 (2019) 689, doi: 10.1007/s10661-019-7894-1.
  41. S. Fatichi, V.Y. Ivanov, E. Caporali, Simulation of future climate scenarios with a weather generator, Adv. Water Resour., 34 (2011) 448–467.
  42. C. Miao, Q. Duan, Q. Sun, J. Li, Evaluation and application of Bayesian multi-model estimation in temperature simulations, Prog. Phys. Geogr., 37 (2013) 727–744.
  43. C.W. Richardson, Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resour. Res., 17 (1981) 182–190.
  44. K. Duan, Y. Mei, A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China, Theor. Appl. Climatol., 116 (2014) 707–719.
  45. S. Moghanlo, M. Alavinejad, V. Oskoei, H.N. Saleh, A.A. Mohammadi, H. Mohammadi, Z. DerakhshanNejad, Using artificial neural networks to model the impacts of climate change on dust phenomenon in the Zanjan region, north-west Iran, Urban Clim., 35 (2021) 100750, doi: 10.1016/j.uclim.2020.100750.
  46. J.M. Melillo, T. Richmond, G. Yohe, Climate Change Impacts in the United States, Third National Climate Assessment, 2014.
  47. E. Pisoni, M. Farina, C. Carnevale, L. Piroddi, Forecasting peak air pollution levels using NARX models, Eng. Appl. Artif. Intell., 22 (2009) 593–602.
  48. Y. Chen, L. Song, Y. Liu, L. Yang, D. Li, A review of the artificial neural network models for water quality prediction, Appl. Sci., 10 (2020) 5776, doi: 10.3390/app10175776.
  49. K.S. Reddy, M. Kumar, V. Maruthi, B. Umesha, Vijayalaxmi, C.V.K. Nageswar Rao, Climate change analysis in southern Telangana region, Andhra Pradesh using LARS-WG model, Curr. Sci., 107 (2014) 54–62.
  50. H. Chen, J. Guo, Z. Zhang, C.-Y. Xu, Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future, Theor. Appl. Climatol., 113 (2013) 363–375.
  51. C. Petpongpan, C. Ekkawatpanit, D. Kositgittiwong, Climate change impact on surface water and groundwater recharge in Northern Thailand, Water, 12 (2020) 1029, doi: 10.3390/w12041029.
  52. R.P. Silberstein, S.K. Aryal, J. Durrant, M. Pearcey, M. Braccia, S.P. Charles, L. Boniecka, G.A. Hodgson, M.A. Bari, N.R. Viney, D.J. McFarlane, Climate change and runoff in south-western Australia, J. Hydrol., 475 (2012) 441–455.
  53. B. Ghazi, E. Jeihouni, O. Kisi, Q.B. Pham, B. Đurin, Estimation of Tasuj aquifer response to main meteorological parameter variations under shared socioeconomic pathways scenarios, Theor. Appl. Climatol., 149 (2022) 25–37.
  54. F. Li, Z. Xu, W. Liu, Y. Zhang, The impact of climate change on runoff in the Yarlung Tsangpo River basin in the Tibetan Plateau, Stochastic Environ. Res. Risk Assess., 28 (2014) 517–526.
  55. B. Ghazi, E. Jeihouni, Projection of temperature and precipitation under climate change in Tabriz, Iran, Arabian J. Geosci., 15 (2022) 621, doi: 10.1007/s12517-022-09848-z.
  56. H. Cai, H. Shi, S. Liu, V. Babovic, Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: the case of central eastern continental United States, J. Hydrol.: Reg. Stud., 37 (2021) 100930, doi: 10.1016/j.ejrh.2021.100930.
  57. S. Jiang, Y. Zheng, C. Wang, V. Babovic, Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments, Water Resour. Res., 58 (2022) e2021WR030185, doi: 10.1029/2021WR030185.
  58. H.M.V.V. Herath, J. Chadalawada, V. Babovic, Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling, Hydrol. Earth Syst. Sci., 25 (2021) 4373–4401.
  59. J. Chadalawada, H.M.V.V. Herath, V. Babovic, Hydrologically informed machine learning for rainfall-runoff modeling: a genetic programming-based toolkit for automatic model induction, Water Resour. Res., 87 (2015) 99–112.