Groundwater quality evaluation model based on multiscale fuzzy comprehensive evaluation and big data analysis method

The reasonable use of water resources has become an important issue for the sustainable development of humanity in the future. Many researches focus on groundwater quality inspection, but not groundwater quality assessment. This paper aims to study groundwater quality evaluation models based on multi-scale fuzzy comprehensive evaluation and big data analysis methods. We combines coarse-grained multi-scale fuzzy entropy and fuzzy comprehensive evaluation method to establish a groundwater quality evaluation model based on big data environment. The evaluation of groundwater samples from 327 test points in Huangpu District, Xuhui District, Hongkou District, and Putuo District of Shanghai was conducted. The results show that the overall condition of Shanghai groundwater is better, and more than 94% of samples quali ﬁ ed as drinking water sources. The method presented in this paper not only guarantees that the coarse-grained data on all scales are consistent with the length of the original data, but also avoids the phenomenon of data loss, which greatly improves the accuracy of subsequent algorithms.


Results from the 2018 World Health Organization and UNICEF Global Water Supply and Sanitation Evaluation
show that the population of 43% in rural Africa, 56% in Latin America, and 67% in Asia do not have access to good quality drinking water (Wu & Sun ). The main source of water in many places in rural areas is shallow groundwater. Shallow holes and mechanical or artificially excavated wells are mainly used to provide drinking water (Abbasnia et al. ). However, due to the continuous detection of toxic organic chemicals and high concentrations of pathogenic microorganisms from these drinking waters, the safety of water supply in these areas has attracted global attention. Recent studies by the World Health Organization have confirmed that arsenic (>0.01 mg/L) and fluoride (>1.5 mg/L) are higher in shallow groundwater in Argentina, Bangladesh, Cambodia, China, Mongolia, and Tanzania. In addition, coupled with nitrate pollution from human activities and agricultural production, attention to groundwater in developing countries has been strengthened (Gautam et al. ).   Based on the evaluation of groundwater remediation technology, an attribute system consisting of 15 remediation schemes was established, and each remediation scheme corresponds to ten attributes. The pairwise comparison between the selected schemes is represented by the value preference model, and the attribute weights are used in the internal hierarchy analytical score.
Based on the actual situation in Shanghai, this paper divides the quality of groundwater resources in Shanghai into five grades: very poor, poor, fair, good, and very good.
A reasonable determination was made. On this basis, the fuzzy mathematics evaluation method is used to divide the Shanghai groundwater quality into five levels of fuzzy evaluation. After data processing by MATLAB, the multi-scale principle is used to classify the index evaluation results.

PROPOSED METHOD Fuzzy comprehensive evaluation
Basic overview of fuzzy comprehensive evaluation Fuzzy evaluation refers to some concepts of fuzzy mathematics, and proposes some evaluation methods to solve the actual evaluation problems (Wang & An ). Specifically, the basis of fuzzy comprehensive evaluation is fuzzy mathematics. Some factors whose boundaries are unclear and insufficiently quantified are quantified based on fuzzy relations. It is a method that can comprehensively evaluate the membership status of the evaluation affairs from multiple factors (Dai & Zhao ). In the process of groundwater quality evaluation, there are many factors that affect groundwater quality, most of which cannot be completely determined, and it is difficult to describe them with mathematical language. Then, in the safety risk evaluation, different influencing factors will have different degrees of influence on it. This forces us to fully consider none of the factors when evaluating it as a whole, because this way we can obtain credible results. As a result, however, to solve this problem, fuzzy comprehensive evaluation method is a good choice.
The core of fuzzy comprehensive evaluation is to use B ¼ AOR for fuzzy transformation calculation. In the model formula: A can be considered as the set of weights of the evaluation factors, and the element a1(0 a 1 1) in the set is the weight value corresponding to the evaluation factors, which represents the single factor u 1 . The magnitude of the effect of the assessment factors on the calculation of water quality also reflects the u 1 assessment level to a certain extent, and the a 1 value is the weight value of each secondary evaluation index obtained by the above analytic hierarchy process, that is, each secondary evaluation. The

Process of fuzzy comprehensive evaluation method
(i) Establishment of fuzzy comprehensive evaluation index: The premise of considering the next comprehensive evaluation is to establish an evaluation index. A reasonable evaluation index will be beneficial to the evaluation process, and an unreasonable evaluation index will cause a large deviation in the evaluation results (Fan et al. ). How to establish a scientific and reasonable evaluation index is very important. Generally, according to the nature of the research target and the accident cases that occurred in the past, it is considered in various aspects in combination with relevant norms (Li et al. ).
For example, the establishment of a factor set: where the i-th factor is the factor in the highest level, and the n-th factor in the second level determines it, that is, First layer of the sub-weight set, Second layer of the sub-weight set, a ij is the weight of the determinant u ij in the second level.
(iii) The establishment of the judging set: Assuming p as the number of total judgments, then this judgment set can be established as,

Improve coarse graining
Determine the sequence {x kj } N iÀ1, (k ¼ 1,2,3, …,p) of p variables, and coarse-grain the original sequence at each scale.
The coarse-grained sequence is expressed as, Take scale 4 as an example, the difference between improved coarse graining and traditional coarse graining is shown in this case. The specific process is shown in Figure 1.

Big data technology system
The situational awareness big data technology system mainly includes four aspects: data collection and preprocessing, data storage and management, data analysis, and data display.
(i) Data collection and preprocessing: Situational awareness big data are complex and the data sources are diverse.
Big data processing first collects data from data sources and performs pre-processing operations. For the col-   Combined with different visualization graphics, the data observability is improved, so that users can quickly and accurately understand the system operational situation, thereby assisting users to make accurate decision-making.

Application advantages of big data in image processing
First, data technology can realize the reproduction of images, improve the sharpness of images, and not reduce the sharpness of images due to image copying and transmission. Second, in the application of big data technology, the accuracy of image processing can be guaranteed, and the image can be simulated by using two-bit data sets. With modern means, modern scanning technology enables the pixels of an image to be guaranteed. Third, the scope of application of image processing is wide. With the support of big data technology, images have different sources and can truly reflect the size of things. In aerial image processing and electron microscope image processing, the nature of things can be truly reflected through digital coding. Fourth, the flexibility of image processing is very high.
In the application of big data technology, image processing can be achieved by means of linear operations and non-linear processing, and digital images can be processed by means of logical relationships. Fifth, image processing under big data technology has great compression potential. In image processing, each pixel is not independent, and the relationship between pixels is very close. The gray-scale similarity between image pixels is large, which promotes image compression. Groundwater samples from the study area were collected in December 2019 and a representative group of 1,000 water samples was selected. All the samples were sent to a professional water quality testing laboratory to get the water quality data. The more uniform sampling distribution can basically represent the water quality of the groundwater in the study area.The sampling location is also the location of the spring water distribution in the study area.

EXPERIMENTS
Construction method of impact factor evaluation index system Establish a hierarchical hierarchical structure A hierarchical hierarchical structure is established to decompose a complex problem into the components of the index, and then continue to decompose until it can be analyzed intuitively. Finally,a hierarchical hierarchy is formed that has a dominating relationship.

Establishing the grid
The grid acquisition method is a thinking model of human judgment in structural theory. Elements and attributes together form a grid, and linear scales are used to express element attributes. Generally, a scale of 1-5 scales is used to indicate the five grades of the evaluation index, namely: particularly good V, relatively good IV, average III, poor II, and, extremely poor I, as shown in Figure 3. The water quality grade is classified according to the standard of Chinese National Environmental Quality Standard for Surface Water (GB 3838-2002). In this standard, the water grade can be qualified by the quality parameters such as temperature change, pH value, oxygen content, and heavy metal content.

Analyze the grid elements and judge the weights under a single criterion
Different experts are selected to evaluate the weights of the firstlevel indicators of the impact factor evaluation. Adopting the expert consultation method, the questionnaire to the experts is used to ask the experts to score and combine the results to get the final results. As for the evaluation index, assuming m experts score it, the expert score table is shown in Table 1, and the fuzzy Borda method is used to analyze the raster data. It has satisfactory consistency and has passed the consistency test. AHR software was used for analysis and calculation. The specific content is as follows.

Constructing the overall objective
A first-level indicator of the groundwater quality judgment matrix, performing weight calculation and consistency ratio test, the calculation process is as follows.
First, the maximum feature λ max and the feature vector W ¼ [w 1 ,w 2 , …,w n ] T of the judgment matrix are calculated so that both satisfy X w ¼ λ max W. The feature vector W ¼ [w 1 ,w 2 , …,w n ] T obtained after normalization of W is used as the ranking weight of the upper index X 1 ,X 2 , …,X n of this level of index.
The approximate calculation method is used to calculate λ max and W. The specific steps are: (i) The elements in the judgment matrix X are multiplied by rows, that is, (ii) Calculation of w i : (iii) Normalize w i to get w i : where, W ¼ [w 1 ,w 2 , …,w n ] T is the required feature vector.
(iv) Calculate the maximum feature root λ max : where (X w ) i represents the i-th element representing X w .

Multi-scale fuzzy entropy analysis
First, normalize the water quality data of each water intake to ensure that the amplitude and length of each water quality data are within the range of 0-1; the impact of the large value points on the whole is reduced. After that, coarse graining is performed according to the scale factor. The coarse graining uses a sliding window method. After coarse graining, the length of the data on each scale is the original sequence 1 (the scale factor). The coarse-grained time series uses traditional multiscale fuzzy entropy for feature extraction.The parameters selected for multiscale fuzzy entropy are: scale factor ω ¼ 1-10, embedding dimension m ¼ 2, delay vector tau ¼ 1, similarity tolerance r ¼ 0.2 × std (std represents the normalized standard deviation).
Multi-scale fuzzy entropy feature extraction results are shown in Figure 4.
As shown in Figure 4, on scales 1-2, normal water quality data and abnormal water quality data are far away from each other, and the variance curves have no overlapping parts.
Starting from scale 3, as the scale increases, the two data set variance curves. The overlapping components gradually increase, and the variance curves of the two data sets have  Factor i compared to j, one of which is slightly more important than the other 5 Factor i compared to j, one of which is more important than the other 7 Factor i compared to j, one of which is more important than the other 9 Factor i compared to j, one of which is more important than the other 2468 The middle number of the above two adjacent judgments Reciprocal The reciprocal of the comparison of the above two factors completely overlapped on scales 6 and 7. On the scales after scale 8, the complexity of the two data sets cannot be clearly distinguished. As a whole, the mean curve of the mean of the two data sets fluctuates greatly at each scale. With the increase of the scale, the complexity of the abnormal gait gradually decreases, while the complexity of the normal gait slowly decreases and gradually tends to smooth.

Time difference
In this paper, a weighted average fuzzy mathematical model is used, and the obtained membership matrix R and weight matrix A are multiplied according to Matlab software. Table 3 and Figure 5 show the water quality categories of the method in this paper before and after July 2019.
According to Figure 5, it is known that among the 1,000 which reduced the pollution to soil and water quality.

Comparative analysis of water sources
The classification ratio of comprehensive water quality evaluation results are given in Table 4. Figure 6(a) shows the comprehensive evaluation and grading ratio of groundwater quality in 52 administrative districts in Shanghai.
Grade V water quality accounts for 24% of the total number of evaluation objects. Grade IV water quality accounts for 34% of the total number of evaluation objects.
Grade III water quality accounts for 26% of the total number of evaluation objects. Grade II water quality accounts for 16% of the total number of evaluation objects.     The results show that the overall condition of Shanghai groundwater is better, and more than 94% are qualified drinking water sources. Finally, it compares the conclusions obtained with the fuzzy comprehensive evaluation method and analyzes it. It can be seen from the comprehensive comparison that the evaluation using the multi-scale fuzzy comprehensive evaluation method can more intuitively compare the differences in water quality between different administrative regions. The evaluation system in this paper is more comprehensive, and its evaluation results are more comprehensive and reasonable than the fuzzy comprehensive evaluation method.