Although China's urbanization is developing rapidly and people's quality of life has been greatly improved, the water environment of people's life is getting worse and worse. Water pollution (WP) has become a major environmental problem endangering people's daily life. Water is an indispensable resource in people's daily life. People should cherish our limited water resources and strengthen the protection of water resources. Therefore, this paper deeply analyzes the current situation of WP in A city, and combines convolutional neural network (CNN) to simulate the morphological characteristics of pollutants in water. Through this study, it is found that the industrial pollution in City A has the greatest impact on the water environment. The industrial pollutants are directly discharged into the river basin, resulting in an increase in the content of COD, ammonia nitrogen and TP in the water body. In addition, there are problems in the prevention and control of WP in City A, such as weak government supervision, weak prevention and control willingness, and low participation of citizens in the prevention and control of WP. Therefore, this paper puts forward corresponding water resources protection suggestions, I hope it can provide reference for WP control in other cities.

Jacopo Marconi. Urban Water Pollution Prevention and Protection Based on Convolutional Neural Network. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 4: 29-37. https://doi.org/10.38007/WPPCP.2022.030404.

[1] Anastasia Iskhakova, Daniyar Volf, Roman V. Meshcheryakov. Method for Reducing the Feature Space Dimension in Speech Emotion Recognition Using Convolutional Neural Networks. Autom. Remote. Control. (2022) 83(6): 857-868. https://doi.org/10.1134/S0005117922060042

[2] T. Chen, Elans Grabs, Ernests Petersons, Dmitry Efrosinin, Aleksandrs lpatovs, Nikolajs Bogdanov, Dmitrjs Rjazanovs. Multiclass Live Streaming Video Quality Classification Based on Convolutional Neural Networks. Autom. Control. Comput. Sci. (2022) 56(5): 455-466. https://doi.org/10.3103/S0146411622050029

[3] Chaimae Ouchicha, Ouafae Ammor, Mohammed Meknassi. A Novel Deep Convolutional Neural Network Model for Alzheimer's Disease Classification Using Brain MRI. Autom. Control. Comput. Sci. (2022) 56(3): 261-271. https://doi.org/10.3103/S0146411622030063

[4] Herbert T. Kruitbosch, Yasmin Mzayek, Sara Omlor, Paolo Guerra, Andreas Milias-Argeitis. A convolutional neural network for segmentation of yeast cells without manual training annotations. Bioinform. (2022) 38(5): 1427-1433. https://doi.org/10.1093/bioinformatics/btab835

[5] Vincent Mallet, Luis Checa Ruano, Alexandra Moine-Franel, Michael Nilges, Karen Druart, Guillaume Bouvier, Olivier Sperandio. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein-protein interactions. Bioinform. (2022) 38(5): 1261-1268. https://doi.org/10.1093/bioinformatics/btab849

[6] 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. (2021) 5(2): 693- 702. https://doi.org/10.1109/TGCN.2021.3062470

[7] Amal Agarwal, Lingzhou Xue. Model-Based Clustering of Nonparametric Weighted Networks With Application to Water Pollution Analysis. Technometrics. (2020) 62(2): 161-172. https://doi.org/10.1080/00401706.2019.1623076

[8] Mostafa Kabolizade, Kazem Rangzan, Sajad Zareie, Mohsen Rashidian, Hossein Delfan. Evaluating quality of surface water resources by ANN and ANFIS networks using Sentinel-2 satellite data. Earth Sci. Informatics. (2022) 15(1): 523-540. https://doi.org/10.1007/s12145-021-00741-z

[9] Giovani Farias, Bruna Leitzke, Miriam Born, Marilton S. de Aguiar, Diana Francisca Adamatti. Water Resources Analysis: An Approach based on Agent-Based Modeling. RITA. (2020) 27(2): 81-95. https://doi.org/10.22456/2175-2745.94319

[10] Hamidou Kassogue, Abdes-Samed Bernoussi, Mina Amharref, Mustapha Ouardouz. Cellular automata approach for modelling climate change impact on water resources. Int. J. Parallel Emergent Distributed Syst. (2019) 34(1): 21-36. https://doi.org/10.1080/17445760.2017.1331438

[11] Nazar Rasheed Nori, Sandeep Kumar Gupta. The Role of Industrial Ecology in Maximizing the Value of the Organization: A Case Study of Mineral Water Industry Organizations of Duhok City, Iraq. Int. J. Serv. Sci. Manag. Eng. Technol. (2021) 12(3): 34-53. https://doi.org/10.4018/IJSSMET.2021050103

[12] 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

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

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

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

[16] 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. (2021) 55(8): 999-1004. https://doi.org/10.3103/S0146411621080204

[17] Sivaraman Eswaran, Daniel Dominic, Jayapandian Natarajan, Prasad B. Honnavalli. Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment. IET Networks. (2020) 9(5): 215-222. https://doi.org/10.1049/iet-net.2019.0165

[18] Mina Emami Khansari, Saeed Sharifian. A modified water cycle evolutionary game theory algorithm to utilize QoS for IoT services in cloud-assisted fog computing environments. J. Supercomput. (2020) 76(7): 5578-5608.  https://doi.org/10.1007/s11227-019-03095-y