International Scientific Journal

Authors of this Paper

External Links


In recent decades, the Yangtze River Basin, which carries hundreds of millions of people and a substantial economic scale, has been plagued by water quality deterioration, threatening considerably sustainable development. In this paper, a sample set is established based on the water quality indexes of chemical oxygen demand and dissolved oxygen obtained by week-by-week monitoring on the main stream of the Yangtze River in Panzhihua, Yueyang, Jiujiang, and Nanjing from 2006 to 2018. The twelve characteristic variables are selected by random forest technique, and the week-by-week dynamic prediction models of chemical oxygen demand and dissolved oxygen at each section of main stream are established by the projection pursuit regression, which can effectively predict the water quality dynamics of the Yangtze River main stream.
PAPER REVISED: 2021-08-10
PAPER ACCEPTED: 2021-08-10
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 3, PAGES [2561 - 2567]
  1. Patterson, J. J., et al., Understanding Enabling Capacities for Managing the 'Wicked Problem' of Non-point Source Water Pollution In Catchments: A Conceptual Framework, Journal of Environmental Man-agement, 128 (2013), Oct., pp. 441-452
  2. Xue, Q. R., et al., A Three-Stage Hybrid Model for the Regional Assessment, Spatial Pattern Analysis and Source Apportionment of the Land Resources Comprehensive Supporting Capacity in the Yangtze River Delta Urban Agglomeration, Science of the Total Environment, 711 (2020), Apr., pp. 1-18
  3. Jiang, Y., China's Water Scarcity, Journal of Environmental Management, 90 (2009), 11, pp. 3185-3196
  4. Yang, X. H., et al., Hierarchy Evaluation of Water Resources Vulnerability under Climate Change in Beijing, China, Natural Hazards, 84 (2016), 1, pp. 63-76
  5. Sun, B. Y., et al., Evaluation of Water Use Efficiency of 31 Provinces and Municipalities in China Using Multi-Level Entropy Weight Method Synthesized Indexes and Data Envelopment Analysis, Sustainabil-ity, 11 (2019), 17, pp. 1-8
  6. Emamgholizadeh, S., et al., Prediction of Water Quality Parameters of Karoon River (Iran) by Artificial Intelligence-Based Models, International Journal of Environmental Science and Technology, 11 (2014), 3, pp. 645-656
  7. Zhang, Z. W., et al., Evidence Integration Credal Classification Algorithm vs. Missing Data Distribu-tions, Information Sciences, 569 (2021), Aug., pp. 39-54
  8. Li, L., et al., Modelling and Filtering for a Stochastic Uncertain System in a Complex Scenario, Thermal Science, 25 (2021), 2, pp. 1411-1424
  9. Liu, L., et al., Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturb-ances and Transmission Time Delays, Complexity, 2020 (2020), ID 7698504
  10. Memon, F. A., et al., Assessment of Gully Pot Management Strategies for Runoff Quality Control Using a Dynamic Model, Science of the Total Environment, 295 (2002), 1-3, pp. 115-129
  11. Yang, X. H., et al., A Fractional-Order Genetic Algorithm (FOGA) for Parameter Optimization of the Moisture Movement in A Bio-Retention System, Thermal Science, 23 (2019), 4, pp. 2343-2350
  12. Yang, X. H., et al., Comprehensive Assessment for Removing Multiple Pollutants, by Plants in Biore-tention Systems, Chinese Science Bulletin, 59 (2014), 13, pp. 1446-1453
  13. Zhao, J., et al., Dynamic Risk Assessment Model for Water Quality on Projection Pursuit Cluster, Hy-drology Research, 43 (2012), 6, pp. 798-807
  14. Raheli, B., et al., Uncertainty Assessment of the Multilayer Perceptron (MLP) Neural Network Model with Implementation of the Novel Hybrid MLP-FFA Method for Prediction of Biochemical Oxygen Demand and Dissolved Oxygen: a Case Study of Langat River, Environmental Earth Sciences, 76 (2017), 14, pp. 502-517
  15. Breiman, L., Random Forests, Machine Learning, 45 (2001), 1, pp. 5-32
  16. Kim, S., et al., Assessing the Biochemical Oxygen Demand Using Neural Networks and Ensemble Tree Approaches in South Korea, Journal of Environmental Management, 270 (2020), ID 110834
  17. Huang, H., et al., Identification of River Water Pollution Characteristics Based on Projection Pursuit and Factor Analysis, Environmental Earth Sciences, 72 (2014), 9, pp. 3409-3417

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence