A prediction method for the impact of hyper-concentrated flow on fishes based on the IPSO-BP neural network

The hyper-concentrated flow process may cause negative impacts on fishes and other aquatic animals during reservoir sediment flushing. Nevertheless, there is a lack of studies on corresponding quantitative assessment methods for the degree of impact of the hyper-concentrated flow. In order to predict and evaluate the impact of reservoir sediment discharge processes on downstream fish, this paper uses experimental data from the study of survival characteristics of Yellow River Gymnocypris Eckloni and Cyprinus Carpio in hyper -concentrated flow and established an IPSO-BP neural network-based method for predicting the impact on fish mortality, and takes into account the effects of suspended sediment concentration, median particle size, dissolved oxygen, exposure time, water temperature, and other factors on fish survival. The prediction error of the target fish mortality is less than 6%. In this paper, the IPSO algorithm, which is closely coupled with BP neural network and introduces dynamic parameters and variational perturbations, has better prediction ability than BP and PSO-BP neural networks, and its accuracy is significantly improved when compared to existing Stress Index (SI), Severity of 111 Effect (SEV), and multivariate fitting evaluation methods at home and abroad. The analysis demonstrates that the prediction method proposed in this paper can account for the situation in which fish mortality in hyper-concentrated flow is governed by a combination of multiple environmental factors with complex correlations among multiple factors. This paper provides a new method for assessing the impact of hyper-concentrated flow on fishes. © 2023 China Water Power Press. All rights reserved.