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Environmental quality evaluation method based on rough set-RBF neural network

A technology of environmental quality and neural network, applied in the field of environmental quality evaluation using rough set-RBF neural network composite technology, can solve the problems of reduced network generalization ability, redundant training samples, redundant network structure, etc., to avoid falling into The effect of local minimum, good convergence and strong adaptability

Inactive Publication Date: 2012-03-21
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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  • Claims
  • Application Information

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Problems solved by technology

[0005] (2) When using environmental quality monitoring data as training samples, many evaluation factors will lead to redundant training samples, resulting in "overfitting" phenomenon, reducing the generalization ability of the network, and resulting in redundant network structures

Method used

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  • Environmental quality evaluation method based on rough set-RBF neural network
  • Environmental quality evaluation method based on rough set-RBF neural network
  • Environmental quality evaluation method based on rough set-RBF neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Example 1: Evaluation of the water environment quality of a certain water system in the Yangtze River in 2002

[0057] 1. Determine the environmental quality evaluation factors

[0058] Select the monitoring data of a certain water system in the Yangtze River in 2002 as shown in Table 1, including five evaluation factors: total nitrogen (TN), ammonia nitrogen (NH 3 -N), total phosphorus (TP), permanganate index (COD Mn ) and total lead, as well as the evaluation results of the comprehensive index method of monitoring data.

[0059] Table 1: Water Environment Quality Monitoring Data

[0060] Sampling point TN NH 3 -N TP COD Mn total lead Comprehensive index method evaluation results 1 0.897 0.35 0.11 2.4 0.036 III 2 0.952 0.29 0.08 2.1 0.037 III 3 1.736 0.32 0.09 2.3 0.038 IV 4

5 0.893

0.744 0.31

0.39 0.07

0.08 2.6

2.1 0.034

0.031 III

III 6 0.787 0.42 0.08 2.4 0.031 III ...

Embodiment 2

[0098] Example 2: Evaluation of Air Environment Quality in Xi'an City, Shaanxi Province

[0099] 1. Determine the environmental quality evaluation factors

[0100] The monitoring data of the air environment in Xi’an City, Shaanxi Province in 2006 are selected as shown in Table 8, including 7 evaluation factors: total suspended particulate matter (TSP), sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), nitrogen dioxide (NO 2 ), floating dust (PM 10 ), carbon monoxide (CO) and ozone (O 3 ), in addition to the comprehensive index method evaluation results of monitoring data.

[0101] Table 8: Air Environment Quality Monitoring Data

[0102] Sampling point TSP SO 2 NO X NO 2 PM 10 CO O 3 Comprehensive index method evaluation results 1 0.423 0.110 0.025 0.046 0.296 2.029 0.068 III2 2 0.409 0.126 0.026 0.049 0.286 1.961 0.065 III2 3 0.446 0.151 0.028 0.054 0.312 2.139 0.071 III2 4 0.491 0.152 0.033 0.09...

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Abstract

The invention discloses a method for enviromental assessment based on rough set-RBF neural network composite technology. The method comprises the following steps: environmental assessment factors are selected from monitored data of a monitored environment according to different environmental assessment requirements; a rough set attribute decision table for environment quality assessment is established; attribute value reduction and attribute reduction for the rough set are carried out and then extracted according to rules; and a newrb function in a MATLAB toolbox is used for establishing and training an RBF network and the trained RBF network is used for environmental assessment. When used for assessing the environment, the method has more satisfactory rapidity, correctness and universality and solves the problem of small partial regions, few training samples and 'over-match', which are easy to happen when only the neural network is used. The method can also be used for assessing thequality of an acoustic environment, a soil environment, an ecological environment, etc.

Description

technical field [0001] The invention relates to an environmental quality evaluation method, in particular to an environmental quality evaluation method using rough set-RBF neural network composite technology. Background technique [0002] Accurate evaluation of environmental quality is a primary task in environmental protection and ecological civilization construction. Since environmental quality assessment involves many factors and parameters, it is more difficult to evaluate it accurately. The existing methods in environmental quality evaluation include: comprehensive evaluation method, gray set method, fuzzy comprehensive evaluation method, etc. These methods need to design the weights of each evaluation factor to each level of standard membership function and each index, so the evaluation mode It is difficult to be universal, and the influence of human factors on the evaluation conclusion cannot be eliminated, so the evaluation result loses its scientific nature and acc...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/00G09F19/00G06N3/00
Inventor 于军琪王佳
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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