Ghazal Kooti, Bahram Dabir, Christoph Butscher, Reza Taherdangkoo
A constrained machine learning surrogate model to predict the distribution of water-in-oil emulsions in electrostatic fields
Scientific reports
paper proposes new framework evaluating water environmental resources carrying capacity wercc based concept resilience uncertainty first several quantitative qualitative criteria based seven principles resilience pressuresupportstate pss framework defined incorporate positive negative impacts human interventions natural factors water resources environment resilience principles include redundancy diversity managing connectivity managing slow variables feedbacks fostering complex adaptive system cas thinking encouraging learning broadening participation promoting polycentric governance evaluating values criteria subcriteria using twopoint evidential reasoning tper approach considering existing uncertainties monthly time series wercc uncertainty bands calculated proposed methodology used evaluate wercc zarrinehrud river basin iran given historical period 19912012 period 2020 2049 different climate change scenarios results analysis demonstrate inadequacy wercc historical period indicate continuation existing trend base scenario msc0 cause many environmental issues hence several water environmental resources management werm scenarios proposed enhance wercc scenarios evaluated using multiagentmulticriteria decisionmaking method identify preferable werm scenario msc12356 scenario encompasses various projects eg development enhancement water transfer networks upgrading cultivation methods improves average value wercc 26 results proposed methodology compared traditional decisionmaking method considers three criteria average wercc pressuresupport index implementation cost results demonstrate multiagentmulticriteria decisionmaking approach provides costeffective management scenario 30 less cost leading 3 less carrying capacity