In recent years, the situation of water resources in many regions has been deteriorating day by day, with water quality deterioration, various kinds of pollution, water shortage and other problems coming one after another. This can not only make the industrialization and agricultural production in trouble or even unable to continue to develop, but also cause great economic losses to the society, affect the sustainable development of the society, and pose a great threat to the survival of mankind. Many researchers have provided new ideas for the study of water pollution (WP) problems and prevention strategies, and this paper takes this as the research direction and basis. This paper analyzes the content of environmental engineering with AI technology, and then carries out academic research and summary on WP problems and prevention strategies and AI technology environmental engineering related research on WP problems and prevention strategies. This paper then establishes an algorithm model, and proposes relevant algorithms to provide theoretical basis for WP problems and prevention strategies in environmental engineering based on artificial intelligence technology. At the end of the paper, the simulation experiment is carried out, and the experiment is summarized and discussed. According to the distribution of the total amount of sewage in the cities in the region, the total amount of sewage in the city has been decreasing year by year since 2018. As one of the main sources of urban sewage, industrial sewage and domestic and commercial sewage are also related. This paper takes it as an example to analyze the reasonable index of the project content of a sewage treatment plant. Among them, the reasonable index difference of pipe diameter and slope before and after use is 3, the reasonable index difference of pipe network is 5, and the reasonable index difference of quantities is 4. This paper concludes that the project of sewage treatment plant after adopting this method is more reasonable. At the same time, with the in-depth study of artificial intelligence technology and environmental engineering, the research of WP problems and prevention strategies is also facing new opportunities and challenges.

Danna Zhang. Water Pollution Problems and Prevention Strategies in Environmental Engineering Based in Artificial Intelligence Technology. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 2: 1-11. https://doi.org/10.38007/WPPCP.2022.030201.

[1] Ahmed, Shahid, and Saba Ismail. "Water pollution and its sources, effects & management: a case study of Delhi." Shahid Ahmed and Saba Ismail (2018)'Water Pollution and its Sources, Effects & Management: A Case Study of Delhi', International Journal of Current Advanced Research 7.2 (2018): 10436-10442.

[2] Chen, Sophia Shuang. "Assessment of urban river water pollution with urbanization in East Africa." Environmental Science and Pollution Research 29.27 (2022): 40812-40825.

[3] Pico, Yolanda, and Damia Barcelo. "Analysis and prevention of microplastics pollution in water: current perspectives and future directions." ACS omega 4.4 (2019): 6709-6719.

[4] Elkhatib, Dounia, and Vinka Oyanedel-Craver. "A critical review of extraction and identification methods of microplastics in wastewater and drinking water." Environmental Science & Technology 54.12 (2020): 7037-7049.

[5] Li, Jing, Kok Fong See, and Jin Chi. "Water resources and water pollution emissions in China's industrial sector: A green-biased technological progress analysis." Journal of cleaner production 229.8 (2019): 1412-1426.

[6] Quesada, Heloise Beatriz. "Surface water pollution by pharmaceuticals and an alternative of removal by low-cost adsorbents: A review." Chemosphere 222.5 (2019): 766-780.

[7] Ighalo, Joshua O., Adewale George Adeniyi, and Gonçalo Marques. "Artificial intelligence for surface water quality monitoring and assessment: A systematic literature analysis." Modeling Earth Systems and Environment 7.2 (2021): 669-681.

[8] Alizadeh, Mohamad Javad. "Effect of river flow on the quality of estuarine and coastal waters using machine learning models." Engineering Applications of Computational Fluid Mechanics 12.1 (2018): 810-823.

[9] Barzegar, Rahim, Mohammad Taghi Aalami, and Jan Adamowski. "Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model." Stochastic Environmental Research and Risk Assessment 34.2 (2020): 415-433.

[10] Peterson, Kyle T., Vasit Sagan, and John J. Sloan. "Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing." GIScience & Remote Sensing 57.4 (2020): 510-525.

[11] Zhi, Wei. "From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?." Environmental science & technology 55.4 (2021): 2357-2368.

[12] Xu, Zuxin. "Urban river pollution control in developing countries." Nature Sustainability 2.3 (2019): 158-160.

[13] Dwivedi, Sanjay, Seema Mishra, and Rudra Deo Tripathi. "Ganga water pollution: a potential health threat to inhabitants of Ganga basin." Environment international 117.8 (2018): 327-338.

[14] Tang, Yankui. "Emerging pollutants in water environment: Occurrence, monitoring, fate, and risk assessment." Water Environment Research 91.10 (2019): 984-991.

[15] Rink, Karsten. "Virtual geographic environments for water pollution control." International Journal of Digital Earth 11.4 (2018): 397-407