In recent years, the eutrophication of Lake Reservoir type drinking water source areas has become increasingly prominent, posing a threat to people's health and sustainable socio-economic development, and preventing and controlling Lake Reservoir pollution is extremely urgent. Only by qualitative identification and quantitative analysis of the pollution sources of lake reservoir type water sources and tracing back to the main sources of pollutants can the main contradictions and key cruxes of drinking water source pollution be clarified, scientific, effective and targeted comprehensive prevention and control countermeasures be put forward, and the safety guarantee ability and level of drinking water source areas be improved. Therefore, this paper establishes a water quality monitoring system based on the depth convolution neural network (CNN), obtains the remote sensing image of a lake by combining remote sensing technology, corrects the remote sensing image pixels through CNN algorithm, and helps to monitor the water quality of the lake area by analyzing the distribution of suspended solids, TP, TN in the image.

Sanders Eduard. Deep Convolution Neural Network in Remote Sensing Monitoring of Water Source Pollution Sources. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 4: 47-55. https://doi.org/10.38007/WPPCP.2022.030406.

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