Water bloom prediction method based on space-time sequence hybrid model

Abstract

The invention discloses a water bloom prediction method based on a space-time sequence hybrid model, and belongs to the technical field of water environment prediction. The water bloom prediction method comprises the following steps: firstly, extracting a large-scale nonlinear trend term of water bloom spatio-temporal data based on a deep belief network; establishing a space weight matrix based onthe geographic positions of the multivariate space-time meteorological monitoring points; then extracting a small-scale residual term and carrying out modeling again; superposing the large-scale nonlinear trend term prediction value and the small-scale residual term prediction value, and obtaining a meteorological prediction value of the target water area according to an inverse distance weighteddifference method; and using ANFIS fusion to predict the water quality and meteorological data of the target water area. According to the method, the number of influence factors of water bloom outbreak is increased, so that the result of water bloom modeling prediction is more accurate, and the influence effect of the surrounding water area on the target water area can be reflected more truly. The method is high in applicability, can be used under the condition of bloom space-time sequence data of different water areas, is suitable for predicting bloom outbreak under different water qualitiesand weather conditions, and has universal applicability.

Description

technical field [0001] The invention belongs to the technical field of water environment prediction, and relates to a water bloom prediction method. Specifically, it is a time-space sequence based on the analysis of historical multivariate water quality data of monitored water areas and historical multivariate spatio-temporal meteorological data of multiple meteorological stations near the water areas. A hybrid model for algal bloom prediction with improved prediction accuracy. Background technique [0002] Eutrophication of water bodies is already a global water environment problem. With the rapid development of social economy, the rapid progress of agriculture and industry, the use of a large amount of chemical fertilizers and pesticide industrial raw materials has led to excessive nitrogen, phosphorus and other nutrients required by organisms to flow into rivers, lakes, bays and other slow-flowing water bodies. It causes the rapid reproduction of algae and other plankton...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problem that the prediction accuracy of water blooms in the field of water environment prediction is not high enough, and only a single monitoring point data is considered in the prediction process, and the influence of space meteorological factors on the outbreak of water blooms is ignored, the present invention proposes a method using time-space sequence of water blooms The method of analysis, modeling and prediction makes full use of the multi-dimensional information of time and space series to improve the accuracy of water bloom prediction

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