The water pollution control system is a complex multi-level system, which must be analyzed from the perspective of network and development. Only through quantitative analysis can the best choice to control pollution and achieve the best social impact be found. The latest development of modern science-system engineering technology and computer technology are used to solve the planning problem of water pollution control system. System engineering technology provides an ideal tool for this research, and computer is an indispensable tool for this research. The research and development in the field of machine intelligence has attracted more and more attention, and the ability and performance of computers continue to develop rapidly. Based on this, this paper first analyzed the elements of water pollution control planning, focused on the basic characteristics of water pollution control planning, and put forward the principles of water pollution control planning and the contents and means of water pollution control planning. Then, this paper designed the application of neural network technology in the field of urban wastewater treatment, and discussed the effect of neural network in the simulation and control of urban wastewater treatment process. The soft sensor of neural network for urban wastewater treatment was proposed, and the effect of neural network combined with other intelligent technologies for urban wastewater treatment was discussed. Neural network was used to strengthen the prevention and protection of water pollution. Through comparison, it can be seen that the perfection of treatment facilities after the new urban water pollution prevention and protection system was 33.2% higher than that before the prevention and control, and the pollution source prevention and control was 25% higher than that before the prevention and control. After using the new urban water pollution prevention and protection system, the real-time sewage purification rate was 0.33 higher than that before the traditional monitoring system, and the water resource protection degree was 0.25 higher than that of the traditional system.

Aziz Remli. Prevention and Protection of Urban Water Pollution Based on Neural Network. Water Pollution Prevention and Control Project (2021), Vol. 2, Issue 4: 32-41. https://doi.org/10.38007/WPPCP.2021.020404.

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