Sedighkia, Mahdi, and Datta, Bithin (2023) Linking SVM based habitat model and evolutionary optimisation for managing environmental impacts of hydropower plants. River Research and Applications, 39 (5). pp. 897-910.

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Abstract

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

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