Construction and Design of Intelligent Decision-Making Platform for Environmental Protection of the Yangtze River

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Abstract

The crossing and conflicting functions of multiple administrative departments in China's Yangtze River Basin in the field of eco-environment protection result in the scatter of data information, the fragmentation of scientific work, and the lack of a comprehensive information query and management platform. This paper puts forward the construction idea of an intelligent decision-making platform for eco-environment protection in Yangtze River Basin, with the goal of implementation of data resource sharing and accurate decision-making into the environmental management process of the region. The general structure of the platform consists of basic layer, data collection layer, data processing layer, brain layer and application layer. The major functions of the platform cover data acquisition and processing, data mining and analysis, data visualization via WebGIS, data classification and sharing, decision-making and consultation, news release, water environment modeling tool and spatial mapping tool. Potential difficulties and key technologies in platform construction, including data collection and standardization, water quality model selection, water environment prediction and early warning, are analyzed and discussed. A platform deployment plan, a phased construction plan and a run-guarantee system are proposed. The platform is designed to realize the visualization of eco-environment elements, the diversity of data collection, the precision of environmental problem diagnosis, the automation of management and response, as well as the facilitation of decision-making and consultation, which ultimately aims at providing intelligent decision-making support for the great protection of the Yangtze River.

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Liu, B., Liu, X., & Wang, W. (2020). Construction and Design of Intelligent Decision-Making Platform for Environmental Protection of the Yangtze River. Research of Environmental Sciences, 33(5), 1276–1283. https://doi.org/10.13198/j.issn.1001-6929.2020.03.43

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