Three gorges reservoir area water body eutrophication risk assessment early warning system and analysis method thereof

Abstract

The invention relates to a three gorges reservoir area water body eutrophication risk assessment early warning system and an analysis method thereof, and belongs to the technical field of water resource early warning. The system comprises a data acquisition subsystem, a preprocessing subsystem, a data analysis subsystem, an eutrophication evaluation subsystem, a risk early warning subsystem and anauxiliary subsystem. The data acquisition subsystem acquires various types of index parameters and stores the acquired data into a database. The preprocessing subsystem classifies, sorts and screensthe collected data, performs data normalization and dimension removal processing, and provides a basis for subsequent data analysis. Evaluation and early warning are objective and accurate, reservoirarea water eutrophication trend analysis and risk early warning processing can be timely and efficiently achieved, reservoir area water environment supervision efficiency can be improved, and technical support is provided for reservoir area water environment safety and eutrophication prevention and control. And the method can also be applied to eutrophication risk research of other reservoirs similar to rivers and lakes.

Description

technical field [0001] The invention belongs to the technical field of early warning of water resources, and relates to a risk assessment and early warning system for water eutrophication in the Three Gorges reservoir area and an analysis method thereof. Background technique [0002] At present, there are various assessment and prediction methods for eutrophication in water bodies, which mainly include two categories: one is a statistical model based on mathematical statistical analysis, such as regression analysis, gray decision-making, artificial neural network method, analytic hierarchy process and fuzzy evaluation method, etc. . This type of model requires a large amount of monitoring data, and the correlation between a certain monitoring indicator and other influencing factors can be obtained through analysis and calculation; however, since the water quality system is a multi-level complex system composed of multiple pollution variables, each indicator has different deg...

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Problems solved by technology

This type of model requires a large amount of monitoring data, and the correlation between a certain monitoring indicator and other influencing factors can be obtained through analysis and calculation; however, since the water quality system is a multi-level complex system composed of multiple pollution variables, each indicator has different degrees of influence. Correlation between each other, a single indicator only reflects the water quality from one aspect, which leads to certain defects in the actual use of the above method

For example, the fuzzy comprehensive evaluation method cannot determine the weight of the complex index system, and the selection of the weight factor is not objective enough; the artificial neural network method needs a large number of sample training in order to improve the prediction accuracy, and at the same time it is easy to fall into the loca

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