A simulation–optimization framework for reducing thermal pollution downstream of reservoirs

Abstract Thermal pollution is an environmental impact of large dams altering the natural temperature regime downstream river ecosystems. The present study proposes a simulation–optimization framework to reduce thermal from reservoirs and tests it on real-world case study. This attempts simultaneously minimize impacts as well losses reservoir objectives for water supply. A hybrid machine-learning model applied simulate under various operation scenarios. shown be robust achieves acceptable predictive accuracy. results indicate that could operated in such way reasonably preserved protect habitats. Doing so, however, would result significant trade-offs storage supply objectives. Such can undermine benefits need carefully considered design operation.