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A processing method for dual uncertainty of water quality monitoring data in water quality assessment
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A technology of water quality monitoring and uncertainty, applied in complex mathematical operations, general water supply conservation, etc., can solve the problem of large loss of water quality data information, and achieve the effect of reducing information loss
Inactive Publication Date: 2019-06-14
HEFEI UNIV
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[0006] Aiming at the problem in the prior art that the double uncertainty of water quality monitoring data cannot be dealt with, resulting in a large loss of water quality data information in water quality assessment, the purpose of the present invention is to provide a treatment for the double uncertainty of water quality monitoring data in water quality assessment method, it can effectively reduce the loss of water quality data information, thereby improving the accuracy of water quality assessment
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example 1
[0062] Example 1: Assumption A 1 ,A 2 ,...,A n is an element in A, is the fuzzy variable in F,
[0063]
[0064] Obviously, is a fuzzy random variable;
[0065] When F degenerates into a set of real numbers F∈R, respectively for y 1 ,y 2 ,...,y n ∈R converts formula (1) to (2)
[0066]
[0067] This is clearly a random variable;
[0068] When Ω is a single-element set, that is, A 1 =A 2 =...A n =Ω=(ξ), in this case, Formula (1) is transformed into formula (3):
[0069]
[0070] In formula (3) is a fuzzy variable whose value is the same as Same, is a fuzzy value;
[0071] According to the nature of the fuzzy random variable revealed in Note 1, the fuzzy random variable can be succinctly redefined as follows:
[0072] Definition 2: Suppose (Ω,A,Pr) is a probability space, and (F,B,Pos) is a possibility space. Then for any a∈A and b∈B, A fuzzy random variable is a map
example 2
[0073] Example 2: If and are the probability spaces (Ω 1 ,A 1 ,Pr 1 ) and (Ω 2 ,A 2 ,Pr 2 ) on the fuzzy random variable, then is the probability space (Ω 1 ×Ω 2 ,A 1 ×A 2 ,Pr 1 ×Pr 2 ) on the fuzzy random variable;
[0074] It can be deduced from Example 2 that the fuzzy random variable has the operation law defined as follows:
[0075] Definition 3: Hypothesis and are the probability spaces (Ω 1 ,A 1 ,Pr 1 ) and (Ω 2 ,A 2 ,Pr 2 ) on the fuzzy random variable, is a fuzzy variable on (F, B, Pos), and a, b, λ are real numbers, then:
[0076] 1) is the probability space fuzzy random variable on
[0077] 2) is the probability space a FRV on the
[0078] 3) and is the probability space (Ω 1 ,A 1 ,Pr 1 ) on the fuzzy random variable;
[0079] Note 3: Given the FRV Combined uncertainty with randomness and ambiguity, in order to measure FRV Requires a likelihood measure θ ∈ [0,1] and a probability measure These two measures will be c...
example 3
[0083] Example 3: Assumption and are two fuzzy random variables, and x 1 ~N(0,0.5),
[0084] x 2 ~N(2,1), then we can get:
[0085]
[0086]
[0087]
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Abstract
The invention discloses a processing method for double uncertainty of water qualitymonitoring data in water quality assessment. The processing method comprises the following steps: collecting and arranging water qualitymonitoring data, analyzing mathematical characteristics of the water quality monitoring data, and obtaining a description result of the monitoring data of each pollutant concentration value in the water body in the form of fuzzy random variables according to the mathematical forms of the fuzzy random variables; compared with an existing water quality monitoring data processingmethod, the processing method provided by the invention has the advantage of low water quality information loss, the method provided by the invention is embedded into a single-factor water quality assessment model, and then lake waterquality assessment is applied, so that the method is proved to be practical. The method provided by the invention has a wide application prospect in development ofa water quality assessment model and actual assessment of water quality.
Description
technical field [0001] The invention relates to the field of water quality assessment, more specifically, to a method for processing double uncertainty of water quality monitoring data in water quality assessment. Background technique [0002] The assessment of surface water quality is of great significance to the life safety of the public and the health of the ecosystem. In order to quantitatively calibrate the water quality, Horton gave the Water Quality Index (WQI) in 1965. Since then, scholars have developed many water quality indexes for various water bodies under different geographical and climatic conditions, or according to different demand goals. Water quality assessment models based on WQI. However, these models require accurate WQI monitoring data when used in water quality assessment; however, they are affected by objective factors such as wind direction, hydrology, and pollutantdiffusion, as well as subjective factors such as sampling time, location, and method...
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