Water resources have always been an indispensable and important resource in the development of modern society. At the same time, water resources play an important role in the high-quality development of social economy and the daily life and work of residents. However, with the gradual deepening of the brutal development model of heavy industry and manufacturing, the degree of damage to water resources in different regions is also deepening. The further development of this water pollution problem has had certain restrictions and impacts on the development of social economy and the daily life and work of residents in different regions. Therefore, the pollution analysis and treatment mode of water environment has become increasingly important. After analyzing the pollution of the water environment in different regions, it can not only help the relevant staff to put forward a more reasonable pollution control model of the water environment, but also help the social economy to achieve better development. In the assessment of the pollution of different water environments, it is necessary to first analyze the quality of the water environment in the basin and comprehensively evaluate the water environment in combination with the consideration of various environmental factors of the regional environment. In this paper, a new water environment quality evaluation and information system is constructed by using neural networks (NN) and logical regression algorithm model. This new water environment quality evaluation and information system mainly uses NN and logical regression algorithm, and its data analysis and processing ability can perform efficient operations on various data in different water environments, thus greatly improving the efficiency of water environment quality evaluation. On the other hand, NN and logistic regression algorithm model can also play a positive role in improving the accuracy of water environment assessment. Finally, the performance difference between the water environment quality assessment and information system combined with NN and logistic regression algorithm and the existing water environment assessment system is analyzed, and it is determined that the performance of this new system in many aspects has been improved by about 18% on average.

Cherif Guerroudj. Water Environment Quality Assessment and Information System Based on Neural Network and Logistic Regression. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 3: 20-28. https://doi.org/10.38007/WPPCP.2020.010303.

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