Water is the source of life and the material basis for human survival, so the healthy state of water resources and environment is of great significance to the orderly development of human society. However, at present, the pollution of lake water body is getting worse, causing serious damage to the lake water environment. In order to improve the lake water quality, this paper uses machine learning algorithm to sample and detect the water quality indicators of Lake Q, uses K-means clustering to analyze the maximum, minimum and average values of water quality, and compares and analyzes eight indicators, including TN, TP, NH3-N, CODMn, DO, BOD, WT and pH value, in the high water season The change trend of water quality concentration and the spatial distribution characteristics of water quality in the normal and dry seasons found that the lake water quality(LWP) was seriously polluted in the normal season, followed by the high water period(HWP) and the low water period(LWP). Through the analysis of the characteristics of lake water pollution, this paper learned the situation of lake water pollution, and provided prevention and control suggestions for solving the problem of lake water pollution.

Holger Böse. The Characteristics Clustering of Lake Water Pollution Based on Machine Learning Algorithm. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 3: 19-27. https://doi.org/10.38007/WPPCP.2022.030303.

[1] Angelika Zube, Dominik Kleiser, Alexander Albrecht, Philipp Woock, Thomas Emter, Boitumelo Ruf, lgor Tchouchenkov, Aleksej Buller, Boris Wagner, Ganzorig Baatar, Janko Petereit. Autonomously mapping shallow water environments under and above the water surface. Autom. (2022) 70(5): 482-495. https://doi.org/10.1515/auto-2021-0145

[2] Amandeep S. Gill, Stefan Germann. Conceptual and normative approaches to Al governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs). Al Ethics. (2022) 2(2): 293-301. https://doi.org/10.1007/s43681-021-00058-z

[3] Ersan Batur, Derya Maktav. Assessment of Surface Water Quality by Using Satellite lmages Fusion Based on PCA Method in the Lake Gala, Turkey. IEEE Trans. Geosci. Remote. Sens. (2019) 57(5): 2983-2989. https://doi.org/10.1109/TGRS.2018.2879024

[4] Kai Matsui, Yoichi Kageyama, Hiroshi Yokoyama. Analysis of Water Quality Conditions of Lake Hachiroko Using Fuzzy C-Means. J. Adv. Comput. Intell. Intell. Informatics. (2019) 23(3): 456-464. https://doi.org/10.20965/jaciii.2019.p0456

[5] Samuel A. Ajila, Chung-Horng Lung, Anurag Das. Analysis of error-based machine learning algorithms in network anomaly detection and categorization. Ann. des Telecommunications. (2022) 77(5-6): 359-370. https://doi.org/10.1007/s12243-021-00836-0

[6] Koushiki Dasgupta Chaudhuri, Bugra Alkan. A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications. Appl. Intell. (2022) 52(10): 11489-11505. https://doi.org/10.1007/s10489-022-03251-7

[7] Felix Duong, Michael Gadermayr, Dorit Merhof, Christiane Kuhl, Philipp Bruners, Sven H. Loosen, Christoph Roderburg, Daniel Truhn, Maximilian F. Schulze-Hagen. Automated major psoas muscle volumetry in computed tomography using machine learning algorithms. Int. J. Comput. Assist. Radiol. Surg. (2022) 17(2): 355-361. https://doi.org/10.1007/s11548-021-02539-2

[8] Lamiaa M. Elshenawy, Chouaib Chakour, Tarek A. Mahmoud. Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes. J. Frankl. Inst. (2022) 359(13): 7115-7139. https://doi.org/10.1016/j.jfranklin.2022.06.022

[9] V. Parimala, K. Devarajan. Modified fuzzy C-means and K-means clustering based spectrum sensing using cooperative spectrum for cognitive radio networks applications. J. Intell. Fuzzy Syst. (2022) 43(3): 3727-3740. https://doi.org/10.3233/JIFS-212863

[10] Peter O. Olukanmi, Fulufhelo V. Nelwamondo, Tshilidzi Marwala, Bhekisipho Twala: Automatic detection of outliers and the number of clusters in k-means clustering via Chebyshev-type inequalities. Neural Comput. Appl.34(8): 5939-5958 (2022). https://doi.org/10.1007/s00521-021-06689-x

[11] Sharanpreet Kaur, Satwinder Singh: COVID-19 Infection and Air Pollution Characteristics in USA. Int. J. Appl. Evol. Comput.12(2): 16-35 (2021). https://doi.org/10.4018/IJAEC.2021040102

[12] Totan Garai, Harish Garg : Possibilistic multiattribute decision making for water resource management problem under single-valued bipolar neutrosophic environment. Int. J. Intell. Syst. 37(8): 5031-5058 (2022). https://doi.org/10.1002/int.22750

[13] Suresh Muthulingam, Suvrat S. Dhanorkar, Charles J. Corbett : Does Water Scarcity Affect Environmental Performance? Evidence from Manufacturing Facilities in Texas. Manag. Sci. 68(4): 2785-2805 (2022). https://doi.org/10.1287/mnsc.2021.4013

[14] Tristan Thielmann : Environmental conditioning: Mobile geomedia and their lines of becoming in the air, on land, and on water. New Media Soc. 24(11): 2438-2467 (2022). https://doi.org/10.1177/14614448221122190

[15] V. A. Miklush, I. A. Sikarev, Tatiana M. Tatarnikova: Organization of Environmental Monitoring of the Port Water Area by Processing an Anti-Interference Signal from a Vessel Traffic Control System. Autom. Control. Comput. Sci. 55(8): 999-1004 (2021) https://doi.org/10.3103/S0146411621080204

[16] Robert Smail, Christopher Donaldson, Rafael Govaerts, Paul Rayson, Carly Stevens : Uncovering Environmental Change in the English Lake District: Using Computational Techniques to Trace the Presence and Documentation of Historical Flora. Digit. Scholarsh. Humanit.36(3): 736-756 (2021). https://doi.org/10.1093/llc/fqaa047

[17] Mario Arzamendia, Daniel Gutierrez-Reina, Sergio Toral, Derlis Gregor, Eleana Asimakopoulou, Nik Bessis : Intelligent Online Learning Strategy for an Autonomous Surface Vehicle in Lake Environments Using Evolutionary Computation. IEEE Intell. Transp. Syst. Mag.11(4): 110-125 (2019). https://doi.org/10.1109/MITS.2019.2939109

[18] Swati Chopade, Hari Prabhat Gupta, Rahul Mishra, Preti Kumari, Tanima Dutta: An Energy-Efficient River Water Pollution Monitoring System in Internet of Things. IEEE Trans. Green Commun. Netw.5(2): 693-702 (2021). https://doi.org/10.1109/TGCN.2021.3062470