CN112185483A - SVM (support vector machine) model-based water quality characteristic mineral water classification method - Google Patents

SVM (support vector machine) model-based water quality characteristic mineral water classification method Download PDF

Info

Publication number
CN112185483A
CN112185483A CN202011090044.XA CN202011090044A CN112185483A CN 112185483 A CN112185483 A CN 112185483A CN 202011090044 A CN202011090044 A CN 202011090044A CN 112185483 A CN112185483 A CN 112185483A
Authority
CN
China
Prior art keywords
data
model
water
mineral water
support vector
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
CN202011090044.XA
Other languages
Chinese (zh)
Inventor
单耀
王艺岚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
North China Institute of Science and Technology
Original Assignee
North China Institute of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by North China Institute of Science and Technology filed Critical North China Institute of Science and Technology
Priority to CN202011090044.XA priority Critical patent/CN112185483A/en
Publication of CN112185483A publication Critical patent/CN112185483A/en
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Computing Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a water quality characteristic mineral water classification method based on an SVM model, which comprises the following steps: collecting a water sample in a mineral water source; testing the water quality information of each group of water samples; establishing an Excel table by utilizing a plurality of groups of water quality information, and importing an R language; reducing the dimension of the data by using a principal component analysis method to obtain dimension reduction data; classifying the dimension reduction data by using a Gaussian mixture model to obtain classified data; marking the classified data, and selecting a plurality of groups of marking data which can be effectively distinguished; marking a plurality of groups of data according to the following steps of 7: 3 into a training data set and a test data set; performing feature selection on the training data set; establishing a support vector machine model; and using the support vector machine model for testing the data set, and evaluating the accuracy of the support vector machine model. According to the SVM model-based water quality characteristic mineral water classification method, the rationality and scientificity of mineral water classification are improved.

Description

SVM (support vector machine) model-based water quality characteristic mineral water classification method
Technical Field
The invention relates to the technical field of mineral water classification, in particular to a water quality characteristic mineral water classification method based on an SVM model.
Background
Mineral water is a precious water resource, and is beneficial to human bodies and suitable for long-term drinking, so that the mineral water has a great resource protection value and an economic value. The mineral water is mainly dissolved in inorganic substances, wherein the macroelement comprises K+、Na+、Ca2+、Mg2+、Cl-、SO42-、HCO3 -And the important trace elements include Se, Sr, Li, Zn, etc. These components represent the difference of water quality on one hand, and the difference of formation conditions and formation processes on the other hand, and are related to the geological conditions of the underground aquifer where the components are located.
The global mineral water sales increased at a rate of 6.4% per year in the last five years. The national standard of natural mineral water drinking (GB 8537-. From the viewpoint of current research and general acceptance, mineral water is roughly classified into metasilicic acid mineral water, strontium mineral water, zinc mineral water, lithium mineral water, selenium mineral water, bromine mineral water, iodine mineral water, carbonated mineral water, and the like. The classification method only inspects the characteristics of a certain aspect of the mineral water, but the inspection is not comprehensive and the classification is not reasonable.
The resource distribution of mineral water is regular. By establishing a classification model of mineral water, the method is helpful for more scientifically and effectively managing mineral water resources, guiding the public to reasonably identify and select the mineral water required by the public, and simultaneously has a basic guiding function for further analysis of the mineral water.
Disclosure of Invention
The invention provides a water quality characteristic mineral water classification method based on an SVM model, which can improve the rationality and scientificity of mineral water classification.
The water quality characteristic mineral water classification method based on the SVM model comprises the following steps: step S1: selecting more than three mineral water source places with certain distances, and collecting water samples in the mineral water source places, wherein the number of the water samples is at least 60 groups, and each water source place is not less than 20 groups; step S2: testing the water quality information of each group of water samples, wherein the water quality information comprises the content of macroelements, the content of trace elements, the pH value, total soluble solids, the value and the hardness of isotopes; step S3: establishing an Excel table by utilizing a plurality of groups of water quality information, converting the Excel table into a CSV table, and importing the CSV table into an R language; step S4: reducing the dimension of the data by using a principal component analysis method to obtain dimension reduction data; step S5: classifying the dimensionality reduction data by using a Gaussian mixture model to obtain classified data; step S6: marking the classified data, and selecting a plurality of groups of marking data which can be effectively distinguished; step S7: importing a plurality of groups of the marking data into an R language and dividing the marking data into a training data set and a testing data set according to the proportion of 7: 3; step S8: selecting characteristics of the training data set by adopting a random forest method, and selecting 3-6 parameters; step S9: applying a support vector machine model frame to the training data set subjected to the feature selection for training, and establishing a support vector machine model; step S10: applying the support vector machine model to the test data set, evaluating a correctness of the support vector machine model, and improving the support vector machine model.
According to the water quality characteristic mineral water classification method based on the SVM model, provided by the embodiment of the invention, in consideration of the difference of the importance of each discrimination parameter, a principal component analysis method and a Gaussian mixture model are used for carrying out characteristic selection on data, namely, more representative data can be selected from the angle of a sample, then R language is utilized and the selected data is divided into a training data set and a testing data set according to the proportion of 7: 3, after a support vector machine model is established by using the training data set, the support vector machine model is used for the testing data set, the accuracy of the support vector machine model is evaluated, and the support vector machine model is improved, so that the accuracy of the support vector machine model can be improved, and the rationality and the scientificity of mineral water classification are further improved.
According to some embodiments of the invention, after the step S2, and before the step S3, the method further comprises: and converting the content of the macroelements into equivalent concentration percentage, and converting the content of the trace elements into equivalent concentration.
According to some embodiments of the invention, the step S4 is performed in the psych package of the R language.
In some embodiments of the invention, the dimension of the dimension reduction data is 2-4.
According to some embodiments of the invention, said step S5 is done in the mcust package of said R language.
According to some embodiments of the invention, the tag data selected in the step S6 is 3-5 sets.
According to some embodiments of the invention, the establishing a support vector machine model is done in the e1071 package of the R language.
According to some embodiments of the invention, after the step S10, the method further comprises: the support vector machine model is used for verification of actual mineral water.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a water quality characteristic mineral water classification method based on an SVM model according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art may recognize the applicability of other processes and/or the use of other materials.
A water quality characteristic mineral water classifying method based on an SVM model according to an embodiment of the present invention will be described with reference to the accompanying drawings.
As shown in fig. 1, a water quality characteristic mineral water classification method based on an SVM model according to an embodiment of the present invention includes: step S1, step S2, step S3, step S4, step S5, step S6, step S7, step S8, step S9, and step S10.
Specifically, as shown in fig. 1, in step S1, more than three mineral water sources are selected at a certain distance, and water samples are collected at the mineral water sources, the number of the water samples is at least 60 groups, and each mineral water source is not less than 20 groups. It is understood that the number of water samples may be 60, 70, 80 or more. Therefore, the number of samples can be increased, and the accuracy of the model is improved.
As shown in fig. 1, in step S2, water quality information of each group of water samples is tested, and the water quality information includes content of macroelements, content of trace elements, pH value, total soluble solids, and isotope value and hardness.
It can be understood that the values and the hardness of the macroelement content, the trace element content, the pH value, the total soluble solid and the isotope of different types of water samples are different, and more classification bases can be provided for the classification of the water samples through the analysis of the values and the hardness of the macroelement content, the trace element content, the pH value, the total soluble solid and the isotope.
As shown in fig. 1, in step S3, an Excel table is created by using multiple sets of water quality information, the Excel table is converted into a CSV table, and the CSV table is imported into the R language.
As shown in fig. 1, in step S4, dimension reduction is performed on the data by using a principal component analysis method to obtain dimension reduced data; step S5, classifying the dimension reduction data by using a Gaussian mixture model to obtain classified data; step S6, labeling the classified data, selecting multiple groups of labeled data which can be effectively distinguished; step S7 is to lead multiple groups of marking data into R language and divide the marking data into training data set and testing data set according to the proportion of 7: 3; step S8, selecting 3-6 parameters for feature selection of the training data set by a random forest method; step S9, applying the support vector machine model framework to the training data set after feature selection for training, and establishing a support vector machine model; step S10 is to use the support vector machine model for the test data set, evaluate the correctness of the support vector machine model, and improve the support vector machine model. In one example of the present invention, the above-described building of the support vector machine model is done in the e1071 package of the R language.
According to the water quality characteristic mineral water classification method based on the SVM model, provided by the embodiment of the invention, in consideration of the difference of the importance of each discrimination parameter, a principal component analysis method and a Gaussian mixture model are used for carrying out characteristic selection on data, namely, more representative data can be selected from the angle of a sample, then R language is used and the selected data is divided into a training data set and a testing data set according to the proportion of 7: 3, after a support vector machine model is established by using the training data set, the support vector machine model is used for the testing data set, the accuracy of the support vector machine model is evaluated, and the support vector machine model is improved, so that the accuracy of the support vector machine model can be improved, and the rationality and the scientificity of mineral water classification are further improved.
According to some embodiments of the invention, after step S2, and before step S3, the method further comprises: the content of the macroelements is converted into the percentage of equivalent concentration, and the content of the microelements is converted into the equivalent concentration. Therefore, the calculation difficulty can be reduced, the calculation efficiency is improved, and the calculation time is saved. The pH, total soluble solids, isotope values and hardness remain unchanged from the original units.
According to some embodiments of the invention, step S4 is done in the psych package in the R language. Therefore, time can be saved, and accuracy of results can be improved.
Specifically, in one example of the present invention, CSV table data is imported into the R language, and the dimension of the data is reduced by a principal component analysis method, and the calculation method is as follows, and a psych package in the R language is used for calculation.
(1) And centralize data X in columns.
(2) And calculating a covariance matrix C of the sample matrix.
(3) And solving the eigenvalue and the eigenvector of the covariance matrix C of the sample set matrix X.
(4) And constructing a dimension reduction conversion matrix U. And (3) setting n variables in the original data, reducing the dimension of the data to m variables (m is less than n) according to the requirement (geochemical professional analysis) or the calculation result (covariance matrix), and forming a dimension reduction conversion matrix U by using eigenvectors corresponding to m eigenvalues before ranking according to the size arrangement.
(5) And obtaining a dimension reduction matrix Z of X by using the dimension reduction conversion formula Z as XU, wherein the matrix represents the information of the original data set by using lower dimension (variable).
In some embodiments of the invention, the dimensionality of the dimension reduction data is 2-4. Therefore, the calculation difficulty can be reduced while the result accuracy is ensured, the calculation efficiency is improved, and the calculation time is saved. For example, in one example of the invention, the dimensionality of the dimension reduction data is 2, 3, or 4.
According to some embodiments of the invention, step S5 is accomplished in the mCluster package in the R language. Therefore, time can be saved, and accuracy of results can be improved.
Specifically, in one example of the present invention, the data after dimension reduction is classified by a Gaussian Mixture Model (GMM), and the calculation method is as follows:
(1) and setting each group of water sample data to accord with Gaussian distribution, wherein the distribution function is shown as a formula (2-a):
Figure BDA0002721807030000051
where K is the number of classifications, πkIs the coefficient of mixing, μkTo expect, sigmakIs a covariance matrix, representing the probability that each sample belongs to the m (m-1, 2, …, k) th group,
Figure BDA0002721807030000052
(2) and the joint probability of the sample set X (n sample points) is shown as the formula (2-b):
Figure BDA0002721807030000053
(3) and the log-likelihood function is shown as formula (2-c):
Figure BDA0002721807030000054
and (5) solving the values of mu, sigma, pi and k in the model by using an EM algorithm.
According to some embodiments of the invention, the tag data selected in step S6 are 3-5 sets. Therefore, the calculation difficulty can be reduced while the result accuracy is ensured, the calculation efficiency is improved, and the calculation time is saved. For example, in one example of the present invention, the marker data is 3 groups, 4 groups, or 5 groups.
In an example of the present invention, in step S8, for convenience of use, constant elements are used as the characteristic parameters for modeling, and trace elements with distinct characteristics may be used as the characteristic parameters for modeling. The steps of feature selection are as follows:
(1) and setting the data set X to contain N samples, and randomly taking the N samples from the data set by using a self-service method (Bootstrap) and bagging the samples to serve as a training data set. In this process, the probability that each sample is not selected is p ═ 1-1/NN. When N tends to + ∞, p ≈ 0.37. This indicates that about 37% of the samples were not selected during bootstrap sampling, referred to as out-of-bag data (OOB). The in-bag data was used to train the model and the out-of-bag data was used to evaluate the model.
(2) And performing extraction for k times, so that k training data sets can be obtained. A decision tree is built with each training data set using a pruning-free approach. At the position of each node, M features are randomly selected from the total number M of features, the Gini index of each feature in the M features is calculated, the smaller the Gin index is, the better the distinguishing effect of the features is, and the optimal feature is selected as the branch node. A complete decision tree is built according to this strategy.
(3) And k decision trees can be obtained by using k data sets to form a random forest model. The quality of the model may be evaluated with the prediction accuracy of the out-of-bag data (OOB). Mean Square Error (MSE) of out-of-bag dataOOB) And a coefficient of determination (R)RF 2) Such as equations (1-a) and (1-b), where the smaller the mean square error, the larger the decision coefficient, indicating that the model is superior.
Figure BDA0002721807030000061
Figure BDA0002721807030000062
Wherein n is the number of data outside the bag, yi is the observed value of the data outside the bag,
Figure BDA0002721807030000063
is the predicted value of the model,
Figure BDA0002721807030000064
is the out-of-bag data prediction variance.
(4) Selecting an important predictive feature using the average impure reduction value. And (3) calculating the Gini index of each variable by applying a formula (1-c) at each node of each tree, calculating the Gini index of each characteristic on each node of each tree, averaging all the Gini indexes according to the characteristics, and calculating the average impure degree reduction value. Each feature is then ranked so that the importance of the features in the model can be scored to select the appropriate feature to model.
Figure BDA0002721807030000065
Where pi is the probability that a sample belongs to the ith branch, N is the total number of branches at the node, and IGini is the Gini index. Important variables are determined by integrating the analysis method of the random forest and the analysis of the geochemistry for modeling, the important variables are mainly selected from macroelements, and are assisted by microelements, isotopes and other parameters, and the number of the important variables is generally 3-6.
In one example of the present invention, there are 60 water samples, which can be labeled as four groups of types, each group being about 15 water samples, and the e1071 package of R is used for calculation, and the method for establishing the support vector machine model is as follows:
(1) computing each sample using a kernel Function (kernel) and projecting it into a higher dimensional space, the selectable kernel functions being a polynomial kernel Function (polynomial) and a Radial Basis kernel Function (Radial Basis Function);
(2) calculating the optimal segmentation hyperplane after each projection so as to maximize the interval between different groups;
(3) the calculation formula of the polynomial kernel function is as follows:
K(x，z)＝(γx·z+c)p (3-a)
the key parameters are gamma, a penalty parameter c and an index p, and the data can be better divided by changing the values of the parameters so as to obtain the optimal discrimination result. In general, the parameters may be initially set to γ ═ 1, c ═ 1, and p ═ 2, or adjusted based thereon. The polynomial kernel function has more parameters and is relatively easy to overfit, so the parameters are not suitable to be set too complicated, in addition, the function of the test data set is larger, and the model may need to be repeatedly modified according to the result of the test data set.
(4) A radial basis kernel function is a scalar function that is symmetric in the radial direction. Generally defined as a monotonic function of the euclidean distance between any point x in space and some center z, which can be written as k (| | x-z |). The most commonly used radial basis kernel function is the gaussian kernel function, which is calculated as:
K(x，z)＝exp(-γ||x-z||2) (3-b)
wherein z is the center of the kernel function, and gamma is the width parameter of the function, and the radial action range of the function is controlled. The key parameters are gamma and a penalty parameter c, and compared with a polynomial kernel function, the radial basis kernel function has fewer parameters and is more stable. In general, the parameter may be initially set to γ ═ 1 and c ═ 1, or adjusted based thereon.
It should be noted that, in step S10, the model is replaced and the misjudged data is analyzed, and unless an error is obvious, the data in the training data set is not deleted, and if a part of the data is deleted, the data needs to be trained again. Is to test the accuracy and generalization of the model using the data of the test data set.
According to some embodiments of the invention, after step S10, the method further comprises: the support vector machine model is verified for the actual spring water (step S11). Thus, the accuracy of the support vector machine model can be further verified using actual mineral water. In an example of the present invention, the model is adaptively modified according to the detection result, so that the reliability of the detection result can be further improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (8)

1. A water quality characteristic mineral water classification method based on an SVM model is characterized by comprising the following steps:
step S1: selecting more than three mineral water source places with certain distances, and collecting water samples in the mineral water source places, wherein the number of the water samples is at least 60 groups, and each water source place is not less than 20 groups;
step S2: testing the water quality information of each group of water samples, wherein the water quality information comprises the content of macroelements, the content of trace elements, the pH value, total soluble solids, the value and the hardness of isotopes;
step S3: establishing an Excel table by utilizing a plurality of groups of water quality information, converting the Excel table into a CSV table, and importing the CSV table into an R language;
step S4: reducing the dimension of the data by using a principal component analysis method to obtain dimension reduction data;
step S5: classifying the dimensionality reduction data by using a Gaussian mixture model to obtain classified data;
step S6: marking the classified data, and selecting a plurality of groups of marking data which can be effectively distinguished;
step S7: importing a plurality of groups of the mark data into an R language and according to 7: 3 into a training data set and a test data set;
step S8: selecting characteristics of the training data set by adopting a random forest method, and selecting 3-6 parameters;
step S9: applying a support vector machine model frame to the training data set subjected to the feature selection for training, and establishing a support vector machine model;
step S10: applying the support vector machine model to the test data set, evaluating a correctness of the support vector machine model, and improving the support vector machine model.
2. A SVM model based water quality characteristic mineral water classifying method according to claim 1, wherein after the step S2 and before the step S3, the method further comprises: and converting the content of the macroelements into equivalent concentration percentage, and converting the content of the trace elements into equivalent concentration.
3. A water quality characteristic mineral water classifying method according to an SVM model as claimed in claim 1, wherein the step S4 is performed using the psych pack of the R language.
4. A water quality characteristic mineral water classification method based on SVM model as claimed in claim 3 wherein dimension of said dimension reduction data is 2-4.
5. A water quality characteristic mineral water classifying method based on SVM model as claimed in claim 1 wherein the step S5 is completed using mclust package of the R language.
6. The SVM model-based water quality characteristic mineral water classifying method according to claim 1, wherein the labeled data selected in the step S6 is 3-5 groups.
7. A SVM model based water quality feature mineral water classification method according to claim 1, wherein said establishing a support vector machine model is done using e1071 package of said R language.
8. A SVM model based water quality characteristic mineral water classifying method according to claim 1, wherein after the step S9, the method further comprises: the support vector machine model is used for verification of actual mineral water.
CN202011090044.XA 2020-10-13 2020-10-13 SVM (support vector machine) model-based water quality characteristic mineral water classification method Withdrawn CN112185483A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011090044.XA CN112185483A (en) 2020-10-13 2020-10-13 SVM (support vector machine) model-based water quality characteristic mineral water classification method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011090044.XA CN112185483A (en) 2020-10-13 2020-10-13 SVM (support vector machine) model-based water quality characteristic mineral water classification method

Publications (1)

Publication Number Publication Date
CN112185483A true CN112185483A (en) 2021-01-05

Family

ID=73949566

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011090044.XA Withdrawn CN112185483A (en) 2020-10-13 2020-10-13 SVM (support vector machine) model-based water quality characteristic mineral water classification method

Country Status (1)

Country Link
CN (1) CN112185483A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112506116A (en) * 2021-02-04 2021-03-16 康玄谷(北京)国际生物科技有限公司 Information analysis system and method based on mineral water

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112506116A (en) * 2021-02-04 2021-03-16 康玄谷(北京)国际生物科技有限公司 Information analysis system and method based on mineral water
CN112506116B (en) * 2021-02-04 2021-06-01 康玄谷(北京)国际生物科技有限公司 Information analysis system and method based on mineral water

Similar Documents

Publication Publication Date Title
Bollen et al. National industrial structure and the global system
CN103632168B (en) Classifier integration method for machine learning
CN109255790A (en) A kind of automatic image marking method of Weakly supervised semantic segmentation
CN108985327B (en) Terrain matching area self-organization optimization classification method based on factor analysis
CN105069470A (en) Classification model training method and device
CN110309868A (en) In conjunction with the hyperspectral image classification method of unsupervised learning
CN112700325A (en) Method for predicting online credit return customers based on Stacking ensemble learning
CN111343147B (en) Network attack detection device and method based on deep learning
CN111814563B (en) Method and device for classifying planting structures
CN102279929A (en) Remote-sensing artificial ground object identifying method based on semantic tree model of object
CN110045227B (en) power distribution network fault diagnosis method based on random matrix and deep learning
CN106600046A (en) Multi-classifier fusion-based land unused condition prediction method and device
CN104463673A (en) P2P network credit risk assessment model based on support vector machine
CN105844303A (en) Sampling type clustering integration method based on local and global information
CN111797188B (en) Urban functional area quantitative identification method based on open source geospatial vector data
CN105868906A (en) Optimized method for analyzing maturity of regional development
CN114821340A (en) Land utilization classification method and system
CN112185483A (en) SVM (support vector machine) model-based water quality characteristic mineral water classification method
CN115293577A (en) Method for analyzing chemical control factors of underground water in alpine-cold flow region based on machine learning
CN105404858A (en) Vehicle type recognition method based on deep Fisher network
CN112185482A (en) GBDT model-based water quality characteristic mineral water classification method
CN111863135B (en) False positive structure variation filtering method, storage medium and computing device
CN111489792B (en) T cell receptor sequence classification method based on semi-supervised learning framework
Pozdnoukhov et al. Applying machine learning methods to avalanche forecasting
CN112185484A (en) AdaBoost model-based water quality characteristic mineral water classification method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20210105