Application Research of Fish-Plant Symbiosis Technology in a Urban Lake: A Case Study of the Environment Remediation Project with Machine Learning Application

Xi Chen, Dunxian Lu, Tao Chen, Ti Liu, Yijuan Luo, Jing Liu, Shengjie Wei, Ying Liu


This research examines the Jiefang Park Water Environment Remediation Project, a pioneering initiative in urban lake rehabilitation. The project is anchored in a fish-plant symbiosis system, which has significantly improved water quality, upgrading it from Category V to Category III, and in certain zones, achieving the commendable Category II. This enhancement is quantitatively marked by a notable reduction in nitrogen and phosphorus levels, and a marked increase in water transparency, with submerged plants covering over 70% of the lakebed.

A key innovation is the utilization of a machine learning model to predict chlorophyll-a concentration, a vital water quality metric. The model’s accuracy is underscored by its R² values, ranging from 0.23 to 0.99, and RMSE values between 6.921 and 0.237, with the best performance at point 1. Additionally, comprehensive measurement data highlight the project’s effectiveness. Water transparency significantly improved from baseline levels, as evidenced by increased dissolved oxygen levels at various sampling sites, indicative of restored ecological equilibrium in the lake. This study represents an exemplary fusion of ecological restoration and advanced data analytics, signifying a substantial advancement in urban lake restoration. It underscores the potential of combining synergistic ecological approaches with technological innovation to further sustainable urban environmental management.

Full Text:




  • There are currently no refbacks.

Copyright (c) 2024 Xi Chen, Dunxian Lu, Tao Chen, Ti Liu, Yijuan Luo, Jing Liu, Shengjie Wei, Ying Liu

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © SCHOLINK INC.  ISSN 2470-637X (Print)  ISSN 2470-6388 (Online)