In reservoir flood control operation, selection of criteria is an important part of the multi-criteria decision making (MCDM) procedure. This paper proposes a method to select criteria for MCDM of reservoir flood control operation based on the back-propagation (BP) neural network. According to the concept of ideal and anti-ideal points, we propose a method to generate training samples of the BP neural network via stochastic simulation. The topological structure of a three-layer BP neural network used for criteria selection is established. The relative importance of criteria is derived via the learned connection weights of a trained BP neural network, and its calculation method is proposed. The sensitivity curve method is employed to conduct sensitivity analysis, and the relative contribution ratio is defined to quantify the relative sensitivity strength of each criterion. We present the principle and threshold value of criteria selection based on the comprehensive discrimination index defined by the combination of the relative importance and relative contribution ratio. The Pubugou reservoir is selected as the case study. The results show that the proposed method can provide an effective tool for decision makers to select criteria before MCDM modeling of reservoir flood control operation.
- back-propagation (BP) neural network
- multi-criteria decision making
- reservoir flood control operation
- selection of criteria
- threshold value
- First received 29 May 2016.
- Accepted in revised form 30 January 2017.
- © IWA Publishing 2017