Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River

Jing Liu, Chao Zang (), Qiting Zuo, Chunhui Han and Stefan Krause
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Jing Liu: College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Chao Zang: Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8571, Japan
Qiting Zuo: School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
Chunhui Han: College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Stefan Krause: School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK

IJERPH, 2023, vol. 20, issue 5, 1-16

Abstract: In order to develop a better model for quantifying aquatic community using environmental factors that are easy to get, we construct quantitative aquatic community models that utilize the different relationships between water environmental impact factors and aquatic biodiversity as follows: a multi-factor linear-based (MLE) model and a black box-based ‘Genetic algorithm-BP artificial neural networks’ (GA-BP) model. A comparison of the model efficiency and their outputs is conducted by applying the models to real-life cases, referring to the 49 groups of seasonal data observed over seven field sampling campaigns in Shaying River, China, and then performing model to reproduce the seasonal and inter-annual variation of the water ecological characteristics in the Huaidian (HD) site over 10 years. The results show that (1) the MLE and GA-BP models constructed in this paper are effective in quantifying aquatic communities in dam-controlled rivers; and (2) the performance of GA-BP models based on black-box relationships in predicting the aquatic community is better, more stable, and reliable; (3) reproducing the seasonal and inter-annual aquatic biodiversity in the HD site of Shaying River shows that the seasonal variation of species diversity for phytoplankton, zooplankton, and zoobenthos are inconsistent, and the inter-annual levels of diversity are low due to the negative impact of dam control. Our models can be used as a tool for aquatic community prediction and can become a contribution to showing how quantitative models in other dam-controlled rivers to assisting in dam management strategies.

Keywords: aquatic community simulation; water ecosystem; GA-BP; modeling comparison; dam-controlled river (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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