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Water quality parameter prediction method based on multilayer recurrent neural network (RNN) and D-S evidence theory

A technology of water quality parameters and evidence theory, applied in neural learning methods, biological neural network models, predictions, etc., can solve the problems of low accuracy in predicting water quality parameters and poor multi-parameter early warning effects, etc., to achieve enhanced practicability and enhanced applicability effect of ability

Inactive Publication Date: 2018-11-06
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0005] The present invention aims at the problems of low prediction accuracy of water quality parameter content and poor multi-parameter early warning effect in the existing prediction method, and provides a water quality parameter prediction method based on multi-layer cyclic neural network and D-S evidence theory

Method used

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  • Water quality parameter prediction method based on multilayer recurrent neural network (RNN) and D-S evidence theory
  • Water quality parameter prediction method based on multilayer recurrent neural network (RNN) and D-S evidence theory
  • Water quality parameter prediction method based on multilayer recurrent neural network (RNN) and D-S evidence theory

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Embodiment Construction

[0040] The concrete realization process of the present invention is as follows:

[0041] Such as figure 1 As shown, the structure of the present invention includes: 1). Historical data sets used for training and testing collection; 2). Multi-layer RNN cyclic neural network for preliminary prediction; 3). Identification framework formed by preliminary prediction results; 4 ). D-S evidence theory, including evidence fusion methods and conflict resolution; 5). Final fusion results.

[0042] Such as figure 2 Shown, the realization process of the present invention is as follows:

[0043] Step 1: Preprocess the collected water quality parameter samples: the samples are water quality parameter values, including CODmn concentration and pH value, and divide the data set into a training set and a test set according to the "leaving out method", wherein the training set occupies The proportion is 70%, and the test set is 30%; further, the training set and the test set are normalized by ...

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Abstract

The invention discloses a water quality parameter prediction method based on a multilayer RNN and a D-S evidence theory. Aimed at a high-dimension, multi-peak, discontinuous and non-convex complex nonlinear system of the water environment, historical data of water quality parameter content is used to train a deep RNN predication model, the model is constructed, and the future content of the waterquality parameter is predicted preliminarily; and the problem of uncertainty can be solved in the mathematical theory via the D-S evidence theory. Thus, conflict solution and evidence fusion can be realized by utilizing the D-S evidence theory on the basis of the RNN model prediction result, the prediction precision of the single parameter is improved, and early warning of multiple parameters is more accurate.

Description

technical field [0001] The invention relates to a water quality parameter prediction method, in particular to a water quality parameter prediction method based on multi-layer cyclic neural network and D-S evidence theory. Background technique [0002] Water is an indispensable resource in industrial production and agricultural production, and is also the source of life for human society. In recent years, with the continuous development of human society, water resources are constantly decreasing, and some areas are even seriously in short supply, which is restricting the development of human society and economy. Therefore, water quality analysis is carried out to strengthen the supervision of water resources and water quality early warning for water resources. utilization is crucial. Quantitative analysis of water quality parameters is one of the important tasks of water quality analysis, and the first task of obtaining quantitative analysis of water quality parameters is to...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/084G06Q10/04G06Q50/06Y02A20/152
Inventor 蒋鹏李雷许欢余善恩林广
Owner HANGZHOU DIANZI UNIV
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