With rich resources and superior habitats, coastal areas have become the most important and concentrated areas for economic and development. At present, a major problem affecting the economic development of coastal areas is the blind development of the coastal economy and the accidental introduction of a large number of land pollutants discharged into the sea and causing marine pollution, which has damaged the ecological balance. Artificial Neural Network (ANN) and genetic algorithm are suitable for solving the problem of water pollution. In this paper, two methods were used to control the total amount of water pollution in marine areas. In this paper, the current situation of the total amount control of water pollution in the sea area was studied firstly, and then the artificial neural network and genetic algorithm were fused to obtain the genetic neural network using the auxiliary combination method. For the study of total amount control of water pollution in the sea area, this paper first calculated the water environment capacity of the sea area, then used genetic neural network to optimize the total amount distribution of water environment, and calculated the pollution concentration standards at different control points. The experimental part applied the total amount control method of sea water pollution in this paper to the management of sea water pollution, predicted the water pollution situation of the sea area, and compared it with the real situation. The results showed that the total amount control method of sea water pollution combined with artificial neural network and genetic algorithm can effectively reduce the water pollution situation of the sea area. The chemical oxygen demand in the sea area decreased from 1.65mg/L to 1.16mg/L, and the phosphate content in the sea area decreased from 0.028mg/L to 0.016mg/L.

Hyunwoo Kim. Evaluation on Total Amount Control of Marine Water Pollution based on ANN and Genetic Algorithm. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 4: 21-30. https://doi.org/10.38007/WPPCP.2020.010403.

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