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Sustainable River and Lake Restoration: From Challenges to Solutions
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Topic Editors

Prof. Dr. Yun Li
Nanjing Hydraulic Research Institute, State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing 210029, China
Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
Nanjing Hydraulic Research Institute, State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing 210029, China
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China
Dr. Boran Zhu
Institute of Water Resource and Hydropower Research, Beijing 10038, China
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Sustainable River and Lake Restoration: From Challenges to Solutions

Abstract submission deadline
1 April 2025
Manuscript submission deadline
1 June 2025
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Topic Information

Dear Colleagues,

Sustainable river and lake restoration is an extremely challenging yet profoundly significant research area. With the continuous exacerbation of climate change, industrialization, and urbanization, rivers and lakes worldwide are facing unprecedented pressures and threats. Systematically addressing a series of thorny issues such as water pollution and degradation of aquatic ecosystems by integrating modern science and technology, thereby achieving sustainable restoration of river and lake systems, is an urgent task that needs to be addressed. To this end, we welcome worldwide scholars in this field to contribute review articles and research papers to the Special Issue "Sustainable River and Lake Restoration: From Challenges to Solutions". The Topics will comprehensively summarize the major obstacles currently being faced in river and lake restoration, sharing the latest research advances and innovative technological approaches and providing forward-looking insights into future development directions. We believe this will help promote interdisciplinary integration and advance the in-depth development of research on sustainable river and lake restoration.

This Topics will invite experts and scholars from relevant fields, both domestically and internationally, to contribute review articles and research papers, covering but not limited to the following topics:

(1) Causes of degradation in river and lake ecosystems and their impacts on biodiversity and human activities;
(2) Application of Artificial Intelligence and Multi-source Data Fusion Methods in River and Lake Ecosystem Management;
(3) Methods for assessing the health of river and lake ecosystems and ecological risk analysis;
(4) Principles, technologies, and practical cases of river and lake ecological restoration;
(5) Scientific management and environmental governance strategies for river and lake ecosystems;
(6) New technologies and methods in river and lake ecosystem research and restoration engineering.

Prof. Dr. Yun Li
Prof. Dr. Hong Yang
Prof. Dr. Xiaogang Wang
Dr. Zhengxian Zhang
Dr. Boran Zhu

Topic Editors

 

Keywords

  • river and lake management
  • ecological restoration
  • reservoir ecological operation
  • artificial intelligence
  • ecological risk analysis
  • digital twins
  • sustainability
  • environmental impact assessment review

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Atmosphere
atmosphere 		2.5 4.6 2010 15.8 Days CHF 2400 Submit
Journal of Marine Science and Engineering
jmse 		2.7 4.4 2013 16.9 Days CHF 2600 Submit
Sustainability
sustainability 		3.3 6.8 2009 20 Days CHF 2400 Submit
Water
water 		3.0 5.8 2009 16.5 Days CHF 2600 Submit

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Published Papers (1 paper)

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Article
Salinity Prediction Based on Improved LSTM Model in the Qiantang Estuary, China
by Rong Zheng, Zhilin Sun, Jiange Jiao, Qianqian Ma and Liqin Zhao
J. Mar. Sci. Eng. 2024, 12(8), 1339; https://doi.org/10.3390/jmse12081339 - 7 Aug 2024
Abstract
Accurate prediction of estuarine salinity can effectively mitigate the adverse effects of saltwater intrusion and help ensure the safety of water resources in estuarine regions. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity and obtained [...] Read more.
Accurate prediction of estuarine salinity can effectively mitigate the adverse effects of saltwater intrusion and help ensure the safety of water resources in estuarine regions. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity and obtained considerable achievements. Due to the nonlinear and nonstationary features of estuarine salinity sequences, this paper proposed a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters for the LSTM model were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). The proposed ISSA-LSTM model was applied to predict salinity at the Cangqian and Qibao stations in the Qiantang Estuary of China, based on measured data from 2011–2012. The model performance is evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The results show that compared to other models including Back Propagation neural network (BP), Gate Recurrent Unit (GRU), and LSTM model, the new model has smaller errors and higher prediction accuracy, with NSE improved by 8–32% and other metrics (MAP, MAPE, RMSE) improved by 15–67%. Meanwhile, compared with LSTM optimized with the original SSA (SSA-LSTM), MAE, MAPE, and RMSE values of the new model decreased by 13–16%, 15–16%, and 11–13%, and NSE value increased by 5–6%, indicating that the ISSA has a better hyperparameter optimization ability than the original SSA. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity. Full article
(This article belongs to the Topic Sustainable River and Lake Restoration: From Challenges to Solutions)
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