Multi-basin real-time intelligent water quality predication method and system

Abstract

The invention discloses a multi-basin real-time intelligent water quality predication method and system. According to the multi-basin real-time intelligent water quality predication method and system, an NARX model is optimized through a genetic algorithm and a problem that earlier stage parameters of the NARX model are uncertain; a GA-NARX model is stored or called through a model storage module to predicate the multi-basin water quality condition in real time; essential data, recent data and history emergency data are performed on three-section training and accordingly an optimized GA-NARX model can basically comprise river pollution characteristics and the predication accuracy is improved; weather data replaces hydrological data and is performed on obfuscation processing and accordingly influences to the model from flow data missing are effectively solved; matching of similar pollution time sequence templates is performed through an improved DTW algorithm, a similar pollution process in the basin history is rapidly found out, experience is referred and learned, and sudden emergency accidents are accurately predicted. The multi-basin real-time intelligent water quality predication method and system can be widely applied to the water quality predication field.

Description

technical field [0001] The invention relates to the field of water quality prediction, in particular to a multi-watershed real-time intelligent water quality prediction method and system. Background technique [0002] Water quality prediction is a frontier work and is widely used in water environment planning, evaluation, management and scientific research. Scientific and reasonable water quality prediction is of great significance to the development and utilization of water resources, real-time understanding of the development trend of river water quality, and the formulation of water environment monitoring and protection implementation plans. With the country's emphasis on water resources protection and long-term construction and exploration accumulation, the surface water automatic monitoring network has gradually improved, and the water quality monitoring data presents the characteristics of "real-time monitoring, multiple indicators, and networking", providing a basis f...

AI Technical Summary

Problems solved by technology

Because there is no mature theoretical basis for their selection, it can only be given by experience, and its application is affected by too many uncertain factors.

[0007] At the same time, the overall distribution of the connection weights and thresholds of the NARX neural network determines the effect of data fitting, but the initial weights and thresholds of the current network are randomly generated, and there is no basis for selection

[0008] (3) The NARX model is suitable for learning deterministic relationships, but when there are contradictory samples and samples containing non-real measurable factors, the prediction accuracy will be greatly reduced

Since the data of the environmental protection department and the water conservancy department have not been shared in real time, there is no hydrological data support for water quality prediction, which affects the prediction accuracy

Images