Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

In the process of mine water inrush disaster prevention, accurate and rapid identication of water inrush source type is of great signicance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as timeconsuming and complex detection. erefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone ssure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of dierent water samples are collected by the Raman spectroscopy system. To eliminate the inuence of laser power uctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), rst derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, themarine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classication models, respectively. Experiments show that SNV has the best preprocessing eect on the raw Raman spectrum, which can eectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identication. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened byMPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DTwater source identication models is 7.61 times faster than that of all Raman data. e results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can eectively reduce the redundancy of Raman spectral data and greatly improve the speed of Raman spectral analysis, which is of great signicance to ensure the real-time detection of the mine water source.


Introduction
With the increasing depth of coal mining, the coal mine production process will face gas, water, re, coal dust, roof disasters, and other coal mine disasters [1,2], and mine water disaster has become the second largest disaster after the mine gas disaster [3,4]. To e ectively guarantee the safe production of coal mines and reduce the occurrence of mine water disasters, it is an important prerequisite for the prevention and control of water disasters to accurately identify the types of mine water inrush sources [5,6]. At present, there are many methods used to distinguish mine water inrush sources, such as groundwater level dynamics [7], hydrochemical analysis [8], GIS theoretical analysis [9], and so on [10]. Among them, the hydrochemical analysis method is one of the most widely used method [11], but the hydrochemical method needs to obtain the hydrochemical parameters of the mine water source (such as pH value, ion concentration, and conductivity) in the harsh experimental environment. In addition, the hydrochemical analysis takes a long time and is not suitable for the solution of online identification of mine water source.
As one of the branches of spectral analysis methods, Raman spectroscopy [12,13] has many advantages, such as high precision, high sensitivity, and no consumption of samples to be tested. It has been widely used in the fields of agriculture, food industry [14], biomedical [15], and so on. Bock and Gierlinger [16] analyzed the Raman spectra of coniferyl alcohol, abietin, and coniferyl aldehyde, and found that the Raman spectra of coniferyl alcohol showed direction dependence.
e Raman spectra of abietin and coniferyl alcohol were very similar, and the Raman spectra of coniferyl aldehyde were affected by the molecular crystal order. Gao et al. [17] compared the ability of near-infrared Fourier transform Raman (1064 nm) and visible Raman (532 nm) to distinguish animal fat. It was found that there were significant differences in the shoulder peak intensity ratio of animal fat in different species at 1653/1745 and 1653/ 1441 cm −1 , as well as 1298 and 1264 cm −1 . After collecting Raman spectra, Zhao et al. [18] integrated nine supervised machine learning algorithms into a system to realize the rapid analysis of edible oil and compared the analysis results with gas chromatography. It was found that these machine learning algorithms combined with Raman spectra had higher analysis accuracy and faster analysis speed. Han et al. [19] briefly described the latest development of machine learning in vibration spectrum prediction, and comprehensively discussed the relationship between machine learning methods and vibration spectrum, especially for Raman spectroscopy. In addition, the future development trend was analyzed. Considering that machine learning is shaping our lives in many ways, Lussier et al. [20] briefly summarized the most common machine learning techniques used in Raman, the guidelines for new users to implement machine learning in data analysis, and the modern applications of machine learning in Raman spectroscopy. In general, Raman spectroscopy has achieved many successful applications. However, Raman spectroscopy data usually have large dimensions and the analysis process is complex. To solve this problem, it is necessary to carry out the feature screening of Raman spectroscopy to reduce the redundancy of Raman spectroscopy data, to improve the real-time performance of spectral analysis, which is of great significance to ensure the real-time performance of Raman spectroscopy detection of mine water sources.
MPA is a new meta-heuristic optimization algorithm proposed by Faramarzi et al. [21] in 2020. Its inspiration comes from the survival of the sea fittest theory, that is, marine predators choose the best foraging strategy between the Levy walk or brown walk, which has the characteristics of strong optimization ability. MPA is widely used in model parameter optimization [22], optimal scheduling [23], feature selection [24], and many other fields. Ramezani et al. [25] improved the raw MPA based on the opposition learning method, chaotic map, population adaptation, and the switching between exploration and utilization stages, and evaluated the performance of the improved algorithm based on the actual optimization problem of proportion integral differential (PID) control applied to direct current (DC) motor. Aiming at the problem of parameter estimation of photovoltaic systems, Abdel-Basset et al. [26] proposed an improved algorithm based on the new meta-heuristic ocean predator algorithm to extract the optimal value of photovoltaic parameters. e specific idea is to improve the estimation accuracy of photovoltaic parameters by using two different ways to deal with the solutions within the population based on the average fitness of the population. Islam et al. [27] proposed a natural-inspired meta-heuristic MPA to solve the optimal power flow (OPF) problem. e proposed method was tested on IEEE 30 bus test system and proved to be feasible and effective. Considering that feature selection is a necessary process to reduce the high-dimensional data set, Abd Elaziz et al. [28] proposed a feature selection strategy based on MPA, which improved the search ability of the algorithm by introducing a sine cosine algorithm, and tested and verified on University of California Irvine (UCI) datasets.
After considering these factors comprehensively, this paper proposes a Raman spectrum feature Raman shift screening method based on MPA and applies it to the rapid identification of mine water source. After obtaining the Raman spectrum data of the mine water inrush source, the raw Raman spectra of mine water inrush sample are smoothed to reduce the noise interference. en, MPA is used to screen Raman spectrum characteristic shifts of the mine water inrush source, removed the redundant information in the Raman spectrum, and reduced the dimension of Raman spectrum data. Taking the selected characteristic Raman shifts as the input of different classifiers, the recognition model of mine water inrush source is constructed, and the feasibility of MPA screening the characteristic Raman shifts of mine water inrush source Raman spectrum is discussed.

Experimental Part
e purpose of this paper is to screen the characteristic Raman shifts of Raman spectrum of mine water inrush source. Firstly, the Raman spectra of water samples are obtained by the Raman spectroscopy system, then the raw Raman spectra are preprocessed, and then the characteristic Raman shift information of the Raman spectrum of water samples is screened by MPA. Finally, the selected characteristic Raman displacement information is used as the input of different classifiers to verify the feasibility of MPA to screen the characteristic Raman displacement of the mine water inrush source. In particular, we use a laptop equipped with Intel Core i7-8565U as the data processing platform and use MATLAB R2020a to complete the data analysis process. e specific experimental process is shown in Figure 1.

e Experimental Material.
In this paper, the goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water collected in the Zhangji mine in Huainan in October 2021 were taken as experimental materials. e information of the mine water sample collection points is shown in Table 1. If the water inrush disaster occurs in the coal mines, the water source of different geological layers usually overlaps. erefore, it is of great significance to study the Raman characteristics of mixed water sources of different geological layers. Based on a single mine water source, four kinds of mixed water samples were obtained by mixing roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water with goaf water at the volume ratio of 1 : 1. Finally, we can get 4 types of mixed water and 5 types of single water, a total of 9 water sources. All nine sources have 160 samples, so the total number of water samples is 1440. 112 samples from each water source type were selected as the training samples and 48 samples were selected as the test samples. e accurate collection of Raman spectrum data of different mine water samples is very important to ensure the reliability and authenticity of Raman spectrum analysis. erefore, we stored all collected mine water inrush source samples in a dark and sealed way.

Raman Spectrum Acquisition.
To collect the Raman spectrum of the mine water source, a Raman spectrum acquisition system was built, as shown in Figure 2. Laser 785-5HFUO laser (Shanghai Ruhai Photoelectric Technology Co. Ltd.) is used as the excitation light source of mine water sample Raman spectrum. e peak wavelength of the laser is 785 ± 0.5 nm and the excitation power is adjustable in the range of 0∼500 mW. Considering the convenience of Raman spectrum measurement and the practicality of mine water disaster warning in the future, RPB-785-1.5-FS immersible Raman probe (Shanghai Ruhai Photoelectric Technology Co. Ltd.) is adopted to directly put into the samples to be measured to measure Raman spectra. e generated Raman spectra are collected by an XR3000 optical fiber spectrometer (Shanghai Ruhai Photoelectric Technology Co. Ltd.), which is equipped with a 2048 × 64 plane array near-infrared enhanced CCD, with a spectral detection range of 780∼1070 nm. In the process of collecting the Raman spectra of mine water samples, the laser power is set to 25 mW and the integration time of the spectrometer is set to 500 ms. e Raman spectra are recorded by Uspectral-PLUS (version 5.2.0) software. At the same time, the whole spectrum acquisition process was carried out in the dark room.

Raman Spectrum Pretreatment.
Because the spectral signals collected by the spectrometer contain not only useful information but also random errors, there is noise interference in the measured spectral curve, so it is necessary to preprocess the collected raw spectral data. Common spectral preprocessing methods include detrend correction (DC), multiplicative scatter correction (MSc), standard normal variable transformation (SNV), first derivative (FD), and mean centering (MC).

MPA Characteristic Raman Shift Screening.
MPA is used to screen the characteristic Raman shifts of the mine water source. e basic idea is to determine the parameters to be optimized according to the identification problem of mine water inrush source, that is, the characteristic Raman shifts of mine water samples, and each spatial position of predator contains a group of characteristic Raman displacements. e spatial position of the predator is measured by the fitness function, and the predator-prey strategy is used to continuously update the predator position until the best predator spatial position is obtained, that is, the best set of characteristic Raman shifts of the problem to be optimized is obtained.
e screening process of characteristic Raman shifts is as follows: Step 1: define the fitness function. Since MPA is a process of solving the minimum value, the classification error (E) of the test set of the mine water source classification model is taken as the fitness function, that is, the objective function is where N correct is the number of correctly predicted samples in the test set and N test is the total number of samples in the test set.
Step 2: set the algorithm parameters and initialize the population. Define the population number n, the maximum number of iterations T, effect coefficient of the fish coagulation device FAD and the constant P.
Step 3: build the prey matrix and elite matrix. e position of the randomly initialized population is shown as where X ij represents the i-th dimension coordinate of the j-th population, X ub and X lb is the upper and lower boundaries of the search space, and rand is a random number in the [0, 1] interval. Based on the location of the search agent, the prey matrix is constructed: MPA, inspired by the natural selection law of survival of the fittest, believes that the top hunters are gifted in hunting. erefore, the optimal predator is regarded as the ontology to copy n − 1 identical predators, and the matrix constructed by n top predators is called the elite matrix, as shown as follows: where X I represents the optimal predator individual vector. Search agents include predators and prey. So, they are all looking for their own food. At the same time, after each iteration, the elite positions are updated according to the adaptation.
Step 4: according to the iteration stage, the predator selects the corresponding update method to update the predator position.
Stage 1: at the initial stage of iteration (the current generation is less than 1/3 of the maximum number of iterations, i.e., t < (1/3)T), conduct a global search and update Prey through Brownian random walk.
where stepsize i is the step size, and the vector R B is a random number vector caused by the normal distribution of Brownian motion, ⊗ representing element by element multiplication. P is a constant and P � 0.5 is a step factor, R ∈ [0, 1] is a random number vector, R B ⊗ Prey i and simulates the high-speed movement of prey. Stage 2: the middle stage of the iteration (the current generation is greater than 1/3 of the maximum number and less than 2/3 of the maximum number of iterations, that is, 1/3T < t < (1/3)T), and this stage is an intermittent stage between the exploration and development stages. e population is divided into two parts, in which the prey makes levy motion, which is responsible for the development of the algorithm in the search space, and the predator makes Brownian motion, which is responsible for the exploration of the algorithm in the search space. Development and exploration behaviors can be expressed as follows: e location of the development team shall be updated as follows: where R L is a random number vector based on Levy flight distribution, which is used to describe Levy flight motion. e exploration group moves through equation (7) update mode: , . . . , n,  Figure 2: Diagram of the Raman spectrum acquisition system. Among them, CF is an adaptive parameter used to control the moving step of the predator, and its calculation method is shown as follows: where t is the current number of iterations and T is the maximum number of iterations. Stage 3: in the later stage of iteration (the current generation is greater than 2/3 of the maximum number of iterations, i.e., t > 2/3T), mainly improve the local development of the algorithm. At this time, the predator's strategy is levy motion: Step 5: calculate the fitness value and update the optimal position of the predator.
Step 6: fish aggregation device (FAD) and eddy current effect. Considering that the external environment may affect the movement of the population to a certain extent, Faramazi et al. [21] proposed a position update strategy based on vortex formation and fish aggregation device to avoid the result falling into the local optimal solution. By using this strategy, MPA effectively overcomes the problem of premature convergence and avoids local extremum in the optimization process. e strategy can be expressed as shown as follows: Among them, FAD is the probability of affecting the search process, and the value is 0.2. X lb and X ub are the vectors composed of the minimum and maximum values of the search boundary, respectively, r is a random value, r ∈ [0, 1], r1, and r2 represent the random index of the prey matrix, and U is a binary vector.
Step 7: judge whether the stop conditions are met. If not, return to step 3 for repeated execution. Otherwise, output the optimal predator fitness value and its spatial position, which is the best set of characteristic Raman shifts.

Raman Spectrum Classification.
To realize the classification and identification of Raman spectrum data, the basic classification algorithms such as back propagation neural network (BP), k-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) are used to construct the Raman spectrum classification models, to realize the recognition of mine water source Raman spectrum. In particular, to ensure the reliability of the mine water source classification models, a 10-fold cross-validation method is adopted in the process of model training.

Raw Raman Spectrum.
e Raman spectrum data of mine water samples are collected by XR3000 optical fiber spectrometer, and the Raman spectra of 1440 samples of nine water samples (160 samples of each water sample) are measured experimentally, as shown in Figure 3. It can be seen from the figure that unmixed goaf water has a good identification degree, but it becomes difficult to distinguish when mixing occurs. e mixed water samples with similar mixing proportion overlap slightly, so it is necessary to classify and identify with the help of the chemometrics method.
To more intuitively understand the differences in the Raman spectrum of different water samples, basic classification algorithms such as BP neural network (BP), k-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) are used as classifiers. Take the raw Raman spectrum data as the input, observe the classification effect of mine water source Raman spectrum under different classifiers, and count the classification accuracy and analysis time of different classifiers. e relevant results are shown in Table 2.
It can be seen from Table 2 that there are some differences in the classification results obtained by using different classifiers for the raw frontal Raman spectrum data. However, we note that BP, KNN, SVM, and DT have high classification accuracy, and the classification accuracy of all algorithms for the test set is higher than 94.00%. e difference in the analysis results of different classification algorithms for the raw Raman spectrum data is mainly reflected in the difference in the classifier model itself. In addition, the raw Raman spectrum data have a certain noise, and the anti-interference ability of different classification algorithms for noise is also different. In addition, the raw Raman spectrum data has a large dimension (2048 dimensions), and the analysis process takes a long time. It can be seen that there are significant differences in Raman spectrum analysis time between different classifiers. Among them, the analysis time of KNN is the shortest (0.31 s), the analysis time of BP is the longest (50.18 s), and the average analysis time of the four classifiers is 19.32 s. In general, although the four basic classification algorithms have good classification accuracy for the raw Raman spectra, the recognition accuracy of the Raman spectrum of mine water source still has room for further improvement. erefore, it is very necessary to preprocess the raw Raman spectra.

Smoothing Pretreatment.
To eliminate the noise interference, reduce the error, and retain the useful information in the spectral curve, DC, SC, SNV, FD, and MC are used to preprocess the raw Raman spectral data of the mine water source, respectively, and the preprocessed spectral information is used as the input of classifier to obtain the classification accuracy of the preprocessed Raman spectra under the four algorithm models, as shown in Table 3.
By observing Table 3, it can be found that after smoothing and preprocessing the raw Raman spectrum of Scientific Programming the mine water source, the classification accuracy of the Raman spectrum under different classifiers has been improved to a certain extent. In particular, we find that the average recognition accuracy of the four classifiers is different from that of the processed Raman spectrum data. When using SNV processing, the average recognition accuracy of the five classifiers can reach the maximum, that is, 98.25%, which shows that SNV is more suitable for smoothing the raw Raman spectrum of mine water inrush samples. When SVM is used as the classifier of Raman spectrum data, the four different classifiers can achieve 99.44% recognition accuracy, which shows that among the four classification algorithms, the SVM algorithm is more suitable for analyzing the preprocessed Raman spectra. Although the identification accuracy of the preprocessed Raman spectrum has been improved to a certain extent, its data dimension has not changed (still 2048 dimension), and the time of spectral analysis is still relatively long. erefore, it is necessary to use a certain feature selection strategy to screen the Raman shifts of the Raman spectrum, effectively reduce the dimension of the spectral data, and improve the speed of data analysis.

MPA Raman Shift Screening.
ere are 2048 spectral data points in the Raman spectrum data of mine water samples. Due to a large amount of data, long operation time, and certain collinearity, the prediction ability of the model may not be high enough when building the mine water source identification model. erefore, MPA is used to screen the Raman shifts of water sample Raman spectrum data, and the characteristic Raman shift points with high importance in Raman spectrum data are extracted. After the raw Raman spectrum data is preprocessed by MA, MPA is used to screen the characteristic Raman shifts. Finally, the KNN classification model is established by using the screened characteristic Raman displacement data (KNN is selected as the classifier to reduce the time of MPA iterative screening of characteristic Raman shifts). e number of Raman displacement variables corresponding to the smallest classification error value is the final screening result. In particular, in the process of characteristic Raman shift screening using MPA, the population number is set to 10, the maximum number of iterations is set to 100, the fish aggregating devices effect is set to 0.2, and the constant is set to 0.5. Figure 4 shows the variation trend of classification error when extracting different numbers of Raman displacement variables. From the figure, it can be seen that the classification error has reached the minimum value of 0.80% after 55 iterations. At this time, the number of characteristic Raman shifts is 226. In other words, when 226 characteristic Raman shifts are selected, the accurate identification of the Raman spectrum of mine water samples can be realized.
Compared with all 2048 Raman spectrum data points, 226 characteristic Raman shifts screened by MPA reduce the number of Raman shifts to the original 11.04%. e preferred 226 characteristic Raman shifts are shown in Figure 5. In the figure, they are represented by red squares. It can be seen that the screened characteristic Raman shifts effectively cover the positions of peaks and

Spectral Analysis after Screening.
To further verify the effectiveness and reliability of the characteristic Raman displacement screened by MPA, the screened characteristic Raman displacement data is used as the input information for mine water source identification. e identification models of mine water source types are constructed by using BP, KNN, SVM, and DT classification algorithms, respectively, and the analysis results of the four classification models are recorded. e classification accuracy and analysis time are shown in Table 4.
It can be seen from the above table that the four different classification algorithms have excellent identification performance for the filtered characteristic Raman displacement data. Among them, the identification accuracy of SVM and DT can reach 100.00%, and that of BP can also reach 98.61%. When we observe the analysis time, it is obvious that the analysis time is greatly reduced, mainly because the dimension of Raman spectrum data is reduced, which makes the analysis speed faster. Compared with the average analysis time of 2048 spectral data in Table 2, it can be seen that the average analysis time of mine water source identification using the screened characteristic Raman displacement data is reduced from 19.32 s to 2.54 s, that is, the analysis speed of the screened characteristic Raman data is 7.61 times that of the full Raman displacement data. In conclusion, using MPA to screen the characteristic Raman shifts of the Raman spectrum of mine water source is effective, and can greatly improve the analysis speed, which is of great significance to speed up the research process of the Raman spectrum identification model of mine water source and realize the online identification of mine water inrush source.

Conclusion
Given the urgent need of mine water inrush disaster early warning, a new method of mine water source identification based on Raman spectrum and MPA is proposed in this paper. Taking the goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, surface water, and their mixed water samples as the research objects, the Raman spectrum data of different water samples are collected by Raman spectrum system. In conclusion, MPA is adopted to screen the characteristic Raman displacement of mine water source the Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly improve the real-time performance of Raman spectral analysis, which is of great significance to ensure the speed of mine water source Raman spectral detection.
At present, the research work of this paper still has some limitations, which are mainly reflected in that the current mine water source comes from the Huainan mining area, and the sample size of Raman spectrum data is relatively small. In the future, we will collect mine water samples from Shanxi and Inner Mongolia, collect their Raman spectra, and further enrich the Raman spectrum database of mine water sources.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that they have no conflicts of interest.