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Water Pollution Prevention and Control Project, 2023, 4(2); doi: 10.38007/WPPCP.2023.040204.

Early Warning System for Urban Industrial Wastewater based on Remote Sensing Technology and Image Analysis

Author(s)

Smith Risser

Corresponding Author:
Smith Risser
Affiliation(s)

Forschungszentrum, D-52428 Julich, Germany

Abstract

With the rapid development of economy, the scale of China's cities is expanding, and the discharge of urban industrial wastewater is also increasing, which has caused some pressure on urban wastewater treatment plants. In this paper, a method of early warning analysis of urban industrial wastewater based on remote sensing technology and image analysis technology is proposed based on the analysis of the current situation of industrial wastewater discharge. The method is used in the sewage early warning system can effectively improve the operational efficiency of sewage treatment plants and water quality monitoring capabilities. It provides a reference basis for enterprises to provide real-time monitoring data and formulate corresponding pollution treatment plans. In view of the industrial sewage pollution, this paper proposes a scheme to build an early warning system for urban industrial sewage based on remote sensing technology and image analysis technology. The system combines remote sensing images and field data to identify industrial wastewater pollution characteristics as the core objective, classify and identify pollutant molecules in images through multi-scale identification and classification algorithms, and use discriminative methods to make a comprehensive evaluation of this water quality model. The results show that the early warning system can accurately identify industrial wastewater pollutants.

Keywords

Remote Sensing Technology, Image Analysis, Urban Industrial Wastewater, Early Warning System

Cite This Paper

Smith Risser. Early Warning System for Urban Industrial Wastewater based on Remote Sensing Technology and Image Analysis. Water Pollution Prevention and Control Project (2023), Vol. 4, Issue 2: 26-34. https://doi.org/10.38007/WPPCP.2023.040204.

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