Paper
14 February 2024 Regional water environment risk assessment and ecological health regulation based on genetic neural network
Cen Wang, Hongmei Bu, Jietao Wang, Xinwen Zhao, Xuehao Liu, Pan Wu, Shihua Qi, Jiwen Ge, Yulei Li
Author Affiliations +
Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301855 (2024) https://doi.org/10.1117/12.3024131
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
Groundwater is one of the important water resources in China. Its advantages include rich reserves, good water quality, convenient access and not easy to lose. According to the groundwater resources survey, two-thirds of cities in China exploit groundwater, which has become the main drinking water source of these cities. Therefore, groundwater is of great significance in protecting the ecological environment and human health. In this paper, Genetic neural network (GNN) algorithm is proposed for regional water environment risk assessment (RA) and ecological health regulation (EHR), and the water environment RA index system based on GNN model is studied and analyzed; The regulation measures of ecological health of water environment are put forward; Taking the groundwater environment of a city as the research object, the regional water environment RA and health regulation test analysis are carried out. The test results verify the effectiveness of the regional water environment RA and EHR based on GNN proposed in this paper, and ensure the rationality of the design of ecological regulation and control scheme, which has strong popularization.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Cen Wang, Hongmei Bu, Jietao Wang, Xinwen Zhao, Xuehao Liu, Pan Wu, Shihua Qi, Jiwen Ge, and Yulei Li "Regional water environment risk assessment and ecological health regulation based on genetic neural network", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301855 (14 February 2024); https://doi.org/10.1117/12.3024131
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KEYWORDS
Water

Artificial neural networks

Neurons

Neural networks

Risk assessment

Environmental sensing

Genetics

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