Linking <scp>SVM</scp> based habitat model and evolutionary optimisation for managing environmental impacts of hydropower plants

The present study proposes a support vector machine (SVM)-based habitat model linked with evolutionary optimisation to balance the impacts of generating hydropower on downstream river habitats. This method was applied in Rajaei reservoir and Tajan River basin Iran mitigate environmental plants. SVM classified suitability at which sigmoid function considering different slopes applied. Nash–Sutcliffe efficiency coefficient as evaluation index is 0.8, implies robust simulate physical Hydraulic simulation demonstrated that depth velocity change from zero 1.79 m 1.82 m/s, respectively. Most suitable flow 7 m3/s reservoir. Five algorithms were used hydropower. Finally, fuzzy technique for order preference by similarity ideal solution (FTOPSIS) selected best optimal Based results, simulated annealing (SA) algorithm ecological impacts, average more than 90% natural flow, while reliability 38%. Moreover, SA able minimise difference between release properly. Using proposed recommendable potential