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Water Pollution Prevention and Control Project, 2021, 2(4); doi: 10.38007/WPPCP.2021.020405.

Structure Design of Water Pollution Control System Based on Decision Tree Algorithm and Robust Optimization

Author(s)

Aras Bozkurt

Corresponding Author:
Aras Bozkurt
Affiliation(s)

University of Sulimanyah Univ Sulaimani, Iraq

Abstract

In recent years, urbanization has been accompanied by the rapid development of population density and open space. People’s attention and understanding of water pollution issues have gradually increased, and the infrastructure of water pollution control (hereinafter referred to as WPC) has also developed rapidly. However, the increasing pressure of the water supply system, the shortage of water resources, the increase of household pollutant emissions, the pollution of rainwater runoff and the continuous deterioration of water environment quality have made the design, construction and technical selection of the WPC system face serious challenges. The current situation of water environment lies in the lack of theoretical guidance and system structure for the design and construction of the existing WPC system. This shows that it is difficult to solve the complex problems caused by the diversification of water pollution system technology and functional objectives. Based on this, this paper first analyzed the problems faced by the WPC system, focusing on the fact that the WPC technology has not formed a complete method and theoretical framework, the lack of systematic analysis of the WPC infrastructure construction, and the lack of analysis and assurance of the planning scheme of the WPC system. Then, this paper proposed the construction and application of robust optimization model in WPC system, and discussed the basic framework of robust optimization model and standard control system design. After that, the improved decision tree algorithm and robust optimization were proposed to strengthen the construction of WPC system. Through comparison, it can be seen that the pollution source control strength of the new WPC system was 0.34 higher than that of the traditional WPC system, and the water quality monitoring accuracy was 0.37 higher than that of the traditional WPC system. The system perfection under the new WPC system was 39.2% higher than that of the traditional WPC system, and the cost control effect was 48.2% higher than that of the traditional WPC system.

Keywords

Water Pollution Control, Pollution Control System, Decision Tree Algorithm, Robust Optimization

Cite This Paper

Aras Bozkurt. Structure Design of Water Pollution Control System Based on Decision Tree Algorithm and Robust Optimization. Water Pollution Prevention and Control Project (2021), Vol. 2, Issue 4: 42-51. https://doi.org/10.38007/WPPCP.2021.020405.

References

[1] Shukla Bishnu Kant. Physico-chemical parameters and status of ground water pollution in Jalandhar-Phagwara region. Green Eng. (2019) 9(2): 212-223.

[2] Jichuan Sheng, Michael Webber, Xiao Han. Governmentality within China's South-North Water Transfer Project: tournaments, markets and water pollution. Journal of Environmental Policy & Planning. (2018) 20(4): 533-549. https://doi.org/10.1080/1523908X.2018.1451309

[3] Li Zhou, Lingzhi Li, Jikun Huang. The river chief system and agricultural non-point source water pollution control in China. Journal of Integrative Agriculture. (2021) 20(5): 1382-1395. https://doi.org/10.1016/S2095-3119(20)63370-6

[4] Xiaojie Ma. Metal-organic framework films and their potential applications in environmental pollution control. Accounts of chemical research. (2019) 52(5): 1461-1470. https://doi.org/10.1021/acs.accounts.9b00113

[5] Yi Liu, Liyuan Yang, Wei Jiang. Qualitative and quantitative analysis of the relationship between water pollution and economic growth: a case study in Nansi Lake catchment, China. Environmental Science and Pollution Research. (2020) 27(4): 4008-4020. https://doi.org/10.1007/s11356-019-07005-w

[6] Yubao Wang. Chinese industrial water pollution and the prevention trends: An assessment based on environmental complaint reporting system (ECRS). Alexandria Engineering Journal. (2021) 60(6): 5803-5812. https://doi.org/10.1016/j.aej.2021.04.015

[7] Mingjing He. Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment. (2021) 3(7): 444-460. 

[8] Kumar Vinod. Assessment of heavy-metal pollution in three different Indian water bodies by combination of multivariate analysis and water pollution indices. Human and ecological risk assessment: an international journal. (2020) 26(1): 1-16. https://doi.org/10.1080/10807039.2018.1497946

[9] Li He, Juan Lu. Can regional integration control transboundary water pollution? A test from the Yangtze River economic belt. Environmental Science and Pollution Research. (2020) 27(22): 28288-28305. https://doi.org/10.1007/s11356-020-09205-1

[10] Rahman Mirza ATM. Heavy metal pollution assessment in the groundwater of the Meghna Ghat industrial area, Bangladesh, by using water pollution indices approach. Applied Water Science. (2020) 10(8): 1-15. https://doi.org/10.1007/s13201-020-01266-4

[11] Pichura Vitalii. Causal regularities of effect of urban systems on condition of hydro ecosystem of Dnieper River. Indian Journal of Ecology. (2020) 47(2): 273-280.

[12] Zhiwei Guo. Graph embedding‐based intelligent industrial decision for complex sewage treatment processes. International Journal of Intelligent Systems. (2021) 37(12): 10423-10441. https://doi.org/10.1002/int.22540

[13] Gibson Matthew. Regulation-induced pollution substitution. Review of Economics and Statistics. (2019) 101(5): 827-840. https://doi.org/10.1162/rest_a_00797

[14] Yuniarti Biyatmoko D., Fauzi H. Hafizianor. Load Capacity of Water Pollution of Jaing River in Tabalong. International Journal of Environment Agriculture and Biotechnology. (2019) 4(3): 805-811. https://doi.org/10.22161/ijeab/4.3.30