Water is one of the most important resources in nature, which can conserve water. It can also irrigate farmland and purify air. However, with the over-exploitation and utilization of water resources by human beings, water environmental problems are becoming increasingly serious and the scope of pollution is gradually expanding. At present, all over the world are strengthening the prevention and control of Water Pollution (WP), especially focusing on the quality of surface water environment. However, limited by geographical conditions and technical factors, the current situation of WP is still not optimistic. The water quality deteriorates rapidly and the degree of pollution is high, which affects people’s normal living water demand. Therefore, it is urgent to find new ways to improve water quality. Intelligent recognition is a new research method based on machine learning theory, which can effectively reduce or avoid environmental pollution and protect biodiversity. In WP management, it can not only detect whether the water contains pollutants, but also transform them into information and store them in the computer for analysis and processing, so as to better predict and evaluate the water environment quality. This paper first introduced the source and characteristics of WP, and then analyzed the common intelligent identification technology. Finally, the design scheme of water quality automatic monitoring system based on neural network model was proposed, and the online monitoring and early warning functions of water quality were realized. The traditional manual detection method was compared with the intelligent identification monitoring model. The results showed that the intelligent identification technology had obvious advantages for the rapid diagnosis of WP, and could reduce the harm caused by human misjudgment to a certain extent. The classification accuracy was improved by about 6.54%, and good results could be achieved without manual intervention, which could well adapt to the current complex water situation.

Sensus Wijonarko. Water Pollution Evaluation and Prevention Measures Based on Intelligent Recognition. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 3: 10-18. https://doi.org/10.38007/WPPCP.2022.030302.

[1] 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

[2]  Shafeeque Muhammad. Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia. Geomatics, Natural Hazards and Risk. (2021) 12(1): 560-580. https://doi.org/10.1080/19475705.2021.1885503

[3] 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.

[4] Rizk Roquia. Does Lake Balaton affected by pollution? Assessment through surface water quality monitoring by using different assessment methods. Saudi Journal of Biological Sciences. (2021) 28(9): 5250-5260. https://doi.org/10.1016/j.sjbs.2021.05.039

[5] Scavia Donald. St. Clair-Detroit River system: Phosphorus mass balance and implications for Lake Erie load reduction, monitoring, and climate change. Journal of Great Lakes Research. (2019) 45(1): 40-49. https://doi.org/10.1016/j.jglr.2018.11.008

[6] Mingjing He. Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment. (2022) 3(7): 444-460. https://doi.org/10.1038/s43017-022-00306-8 

[7] Ismail Muhammad. Pollution, toxicity and carcinogenicity of organic dyes and their catalytic bio-remediation. Current pharmaceutical design. (2019) 25(34): 3645-3663. https://doi.org/10.2174/1381612825666191021142026

[8] Anbarasan M. Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Computer Communications. (2020) 150(1): 150-157. https://doi.org/10.1016/j.comcom.2019.11.022

[9] Zhiwei Guo. Graph embedding‐based intelligent industrial decision for complex sewage treatment processes. International Journal of Intelligent Systems. (2022) 37(12): 10423-10441. https://doi.org/10.1002/int.22540

[10]  Dwivedi Sanjay, Seema Mishra, Rudra Deo Tripathi. Ganga water pollution: a potential health threat to inhabitants of Ganga basin. Environment international. (2018) 117(8): 327-338. https://doi.org/10.1016/j.envint.2018.05.015

[11] Janga Reddy M., D. Nagesh Kumar. Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review. H2Open Journal. (2020) 3(1): 135-188. https://doi.org/10.2166/h2oj.2020.128

[12] Lee Changgu. Porous electrospun fibers embedding TiO2 for adsorption and photocatalytic degradation of water pollutants. Environmental science & technology. (2018) 52(7): 4285-4293. https://doi.org/10.1021/acs.est.7b06508

[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] KiliC Zeyneb. The importance of water and conscious use of water. International Journal of Hydrology. (2020) 4(5): 239-241. https://doi.org/10.15406/ijh.2020.04.00250

[15] Zuxin Xu. Urban river pollution control in developing countries. Nature Sustainability. (2019) 2(3): 158-160. https://doi.org/10.1038/s41893-019-0249-7