CN107403188A - A kind of quality evaluation method and device - Google Patents
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Abstract
The present invention, which provides a kind of quality evaluation method and device, wherein methods described, to be included：S1, all kinds of water quality index values of acquisition are combined into comprehensive index value using PCA；S2, according to the comprehensive index value, the RBF neural optimized using ant group algorithm obtains water quality assessment result.By the present invention in that all kinds of water quality index values of acquisition are combined into comprehensive index value with PCA, dimensionality reduction is carried out to all kinds of water quality index values according to the correlation between all kinds of water quality index, data structure is simplified, improves arithmetic speed；Meanwhile according to the comprehensive index value, the RBF neural optimized using ant group algorithm obtains water quality assessment result, fast convergence rate, network structure are simple, strong robustness and are not easy to be absorbed in local minimum point.
Description
Technical Field
The invention relates to the field of hydrological prediction, in particular to a water quality evaluation method and a water quality evaluation device.
Background
The water quality evaluation refers to selecting corresponding water quality parameters, water quality standards and evaluation methods according to evaluation targets, and evaluating the water quality utilization value and the water treatment requirements. The water quality evaluation is a basic work for reasonably developing, utilizing and protecting water resources. And adopting corresponding water quality standards according to different evaluation types. The water quality evaluation methods are more, such as a single factor evaluation method, a comprehensive evaluation method, a discriminant analysis method, a gray clustering method and the like.
Neural networks developed recently are widely used in the fields of signal processing, feature extraction, pattern recognition, nonlinear prediction and the like. There are many studies on water quality evaluation by using a neural network, for example, a water quality evaluation model based on fuzzy artificial neural network recognition is proposed by aged sunshine and the like; the method comprises the steps of providing a BP (Back Propagation) neural network-based water quality evaluation model by Zhuqiande et al for example in the snow barrage in the southern small town of the lake basin, and analyzing the application of the BP neural network in the comprehensive water quality evaluation; wang Dong et al propose an improved raw water quality evaluation method Based on PSO-RBF (Radial basis function Based on Particle Swarm Optimization) neural network model, and analyze its application; zhang et al propose a new neural network model T-S (Takagi and Sugeno) fuzzy neural network to evaluate the quality of groundwater; a model which uses a neural network optimized by an artificial bee colony algorithm for water quality evaluation and prediction research is provided for na and the like; the general BP model is improved by Sunyongquan and the like, a fuzzy BP neural network model is provided, the quality of lake water is evaluated by respectively utilizing the BP model and the improved fuzzy BP model, and the evaluation results are compared and analyzed.
In the prior art, the values of various water quality indexes are directly used as input, and the correlation with different degrees among the various water quality indexes is ignored, so that the accuracy of water quality evaluation is influenced.
Disclosure of Invention
In order to overcome the problem that the accuracy of water quality evaluation is influenced by directly taking the values of various water quality indexes as input without considering the correlation among various water quality indexes or at least partially solve the problem, the invention provides a water quality evaluation method and a water quality evaluation device.
According to a first aspect of the present invention, there is provided a water quality evaluation method comprising:
s1, combining the obtained water quality index values into a comprehensive index value by using a principal component analysis method;
and S2, obtaining a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
Specifically, the step S1 specifically includes:
standardizing the water quality index value to obtain a matrix of the standardized water quality index value;
acquiring the accumulated contribution rate of each principal component according to the eigenvalue of the matrix;
and taking the value of the principal component of which the characteristic value is greater than a first preset threshold value and the accumulated contribution rate is greater than a second preset threshold value as the comprehensive index value.
Specifically, the step S2 specifically includes:
s21, clustering the comprehensive index value by using the ant colony algorithm to obtain a clustering center, and taking the clustering center as the center of the RBF neural network;
s22, obtaining the weight from the hidden layer to the output layer in the RBF neural network by using a back propagation algorithm;
s23, according to the output of the hidden unit of the hidden layer, clipping the hidden unit.
Specifically, the step S21 specifically includes:
s211, according to the path information quantity between any two comprehensive indexes, acquiring the probability of clustering one comprehensive index to the other comprehensive index in the two comprehensive indexes, and if the probability is judged to be larger than a third threshold value, classifying the two comprehensive indexes into one class;
s212, acquiring a clustering center of each class and a total error of all the classes, and if the total error is judged to be less than or equal to a fourth preset threshold, taking the clustering center as the center of the RBF neural network; or,
if the total error is judged to be larger than the fourth preset threshold, acquiring a new path information quantity according to the distance from the comprehensive index to the clustering center and the modified pheromone persistence coefficient, and iteratively executing clustering and determining the clustering center by using the new path information quantity until the total error is smaller than or equal to the fourth preset threshold.
Specifically, the step S23 specifically includes:
acquiring an output value of each hidden unit of the hidden layer, and normalizing the output value;
and if the normalized output value is judged to be smaller than a fifth preset threshold value, removing the hidden unit corresponding to the output value.
Specifically, the method further includes, before the step S211:
initializing the path information quantity between any two comprehensive indexes according to the Euclidean distance between the two comprehensive indexes.
Specifically, the step S2 is followed by:
obtaining water quality evaluation results of a plurality of positions in one area according to the steps S1 and S2;
and acquiring a relation curve between the coordinates of the positions and the water quality evaluation result according to the coordinates of each position and the water quality evaluation result of each position.
According to a second aspect of the present invention, there is provided a water quality evaluating apparatus comprising:
the combination unit is used for combining the acquired various water quality index values into a comprehensive index value by using a principal component analysis method;
and the acquisition unit is used for acquiring a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
According to a third aspect of the present invention, there is provided a water quality evaluating apparatus comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein,
the processor, the memory and the communication interface complete mutual communication through the bus;
the communication interface is used for information transmission between the test equipment and the communication equipment of the display device;
the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the method as previously described.
According to a fourth aspect of the invention, there is provided a non-transitory computer readable storage medium storing a computer program of the method as described above.
The invention provides a water quality evaluation method and a device, the method combines various acquired water quality index values into a comprehensive index value by using a principal component analysis method, and reduces the dimension of various water quality index values according to the mutual relation among various water quality indexes, thereby simplifying the data structure and improving the operation speed. Meanwhile, according to the comprehensive index value, the water quality evaluation result is obtained by using the RBF neural network optimized by the ant colony algorithm, the convergence speed is high, the network structure is simple, the robustness is strong, and the local minimum point is not easy to fall into.
Drawings
FIG. 1 is a flow chart of a water quality evaluation method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a prior art RBF neural network;
FIG. 3 is a flow chart of a water quality evaluation method according to another embodiment of the present invention;
FIG. 4 is a flow chart of a water quality evaluation method according to another embodiment of the present invention;
fig. 5 is a block diagram of a water quality evaluation apparatus according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In an embodiment of the present invention, a water quality evaluation method is provided, and fig. 1 is a flow chart of the water quality evaluation method provided in the embodiment of the present invention, as shown in fig. 1, the method includes: s1, combining the obtained water quality index values into a comprehensive index value by using a principal component analysis method; and S2, obtaining a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
Specifically, in S1, the water quality index includes physical and chemical properties of water quality, and the water quality index may be a value of a water quality index collected in a preset time period with a preset time period as a cycle, such as 23 water quality index values collected in 4 years with each month as a cycle. And considering the mutual relation among the water quality indexes, combining the various water quality indexes into a comprehensive index by using the principal component analysis method, and combining the obtained values of the various water quality indexes into a value of the comprehensive index. The principal component analysis method is a multivariate mathematical statistical method mainly used for reducing dimension, and converts a plurality of water quality indexes into a few comprehensive indexes. The water quality indexes have correlation, one group of related water quality indexes are converted into another group of unrelated indexes through linear change by the principal component analysis method, and the total variance of the water quality indexes is kept unchanged in the conversion process. And arranging the transformed water quality indexes according to the sequence that the variances are sequentially decreased, wherein the water quality index after the first transformation has the largest variance and is called as a first main component, the variance of a second main component is larger, and is not related to the first main component, and so on.
And S2, taking the comprehensive index value as the input of the RBF neural network model after the ant colony algorithm optimization, and obtaining a water quality evaluation result.The ant colony algorithm is also called as ant algorithm, and is a probability algorithm for searching an optimized path in a graph. The ant colony algorithm is firstly proposed by Dorigo and the like for the first time, is a bionic optimization algorithm developed based on collective foraging behavior of a biological ant colony system, and has the advantages of parallel distributed computation, strong global optimization capability, strong adaptability, easiness in combination with other algorithms and the like. The RBF neural network is a forward network composed of three layers, as shown in fig. 2, wherein the first layer is an input layer, and the number of nodes is equal to the input dimension; the second layer is a hidden layer, and the number of nodes depends on the complexity of the problem; the third layer is an output layer, and the number of nodes is equal to the dimension of the output data. x is the number of1、x2...xnTo input data, w1、W2...wnAre the weights from the hidden layer to the output layer. The output y of the RBF neural network is obtained by the following formula:
wherein X ═ { X ═ X1,x2...，xnIs the input vector; omegakThe connection weight value of the kth hidden layer neuron and the output layer neuron; phi is akFor the output of the kth hidden layer neuron, it is obtained by:
μkis the center of the RBF neural network, σkIs the variance. The number of hidden layer neurons is determined according to the complexity of the problem, and although more hidden layer neurons can make the RBF neural network more accurate, too many hidden layer neurons can make the RBF neural network training time too long and generate an overfitting problem.
In the embodiment, the obtained various water quality index values are combined into the comprehensive index value by using a principal component analysis method, and the dimension reduction is performed on the various water quality index values according to the interrelation among the various water quality indexes, so that the data structure is simplified, and the operation speed is increased. Meanwhile, according to the comprehensive index value, the water quality evaluation result is obtained by using the RBF neural network optimized by the ant colony algorithm, the convergence speed is high, the network structure is simple, the robustness is strong, and the local minimum point is not easy to fall into.
On the basis of the foregoing embodiment, the step S1 specifically includes: standardizing the water quality index value to obtain a matrix of the standardized water quality index value; acquiring the accumulated contribution rate of each principal component according to the eigenvalue of the matrix; and taking the value of the principal component of which the characteristic value is greater than a first preset threshold value and the accumulated contribution rate is greater than a second preset threshold value as the comprehensive index value.
Specifically, the water quality index value is normalized to unify units of each water quality index. And acquiring a correlation coefficient matrix according to the normalized matrix of the water quality index value. And acquiring the characteristic value according to the matrix of the water quality index value and the correlation coefficient matrix. And acquiring the contribution rate of each principal component according to the characteristic value, and acquiring the accumulated contribution rate of each principal component according to the contribution rate. And taking the value of the principal component of which the characteristic value is greater than a first preset threshold value and the accumulated contribution rate is greater than a second preset threshold value as the comprehensive index value. The first preset threshold is 1, and the range of the second preset threshold is 85% -95%.
In the embodiment, the obtained various water quality index values are combined into the comprehensive index value by using a principal component analysis method, and the dimension reduction is performed on the various water quality index values according to the interrelation among the various water quality indexes, so that the data structure is simplified, and the operation speed is increased.
Fig. 3 is a flowchart of a water quality evaluation method according to an embodiment of the present invention, and as shown in fig. 3, on the basis of the foregoing embodiment, the step S2 in this embodiment specifically includes: s21, clustering the comprehensive index value by using the ant colony algorithm to obtain a clustering center, and taking the clustering center as the center of the RBF neural network; s22, obtaining the weight from the hidden layer to the output layer in the RBF neural network by using a back propagation algorithm; s23, according to the output of the hidden unit of the hidden layer, clipping the hidden unit.
Specifically, in S21, the ant colony algorithm is used to cluster the synthetic index values, and a cluster center of the synthetic index values is obtained. And taking the clustering centers as the centers of the RBF neural network, wherein the number of the clustering centers is the same as that of the RBF neural network centers. In S22, the RBF neural network is trained by using a back propagation algorithm with cross entropy as an objective function, and the weight from a hidden layer to an output layer in the RBF neural network is obtained. In S23, the hidden units are clipped according to the output of each hidden unit of the hidden layer. The clipping is to remove the hidden unit.
According to the embodiment, the water quality evaluation result is obtained by using the ant colony optimization RBF neural network according to the comprehensive index value, the convergence speed is high, the network structure is simple, the robustness is strong, and the local minimum point is not easy to fall into.
Fig. 4 is a flowchart of a water quality evaluation method according to an embodiment of the present invention, and as shown in fig. 4, on the basis of the foregoing embodiment, the step S21 in this embodiment specifically includes: s211, according to the path information quantity between any two comprehensive indexes, acquiring the probability of clustering one comprehensive index to the other comprehensive index in the two comprehensive indexes, and if the probability is judged to be larger than a third threshold value, classifying the two comprehensive indexes into one class; s212, acquiring a clustering center of each class and a total error of all the classes, and if the total error is judged to be less than or equal to a fourth preset threshold, taking the clustering center as the center of the RBF neural network; or, if the total error is judged to be greater than the fourth preset threshold, acquiring a new path information quantity according to the distance from the comprehensive index to the clustering center and the modified pheromone persistence coefficient, and iteratively performing clustering and determining the clustering center by using the new path information quantity until the total error is less than or equal to the fourth preset threshold.
Specifically, in S211, for any two comprehensive indicators in the two comprehensive indicators, the probability from one comprehensive indicator to the other comprehensive indicator in the two comprehensive indicators is obtained according to the path information amount between the two comprehensive indicators. Comprehensive index xiClustering to composite index xjProbability p ofijObtained by the following formula:
wherein, tauijAnd n is the number of the comprehensive indexes. If p isijIf the value is larger than the third preset threshold value, x is addediAnd xjAre classified into one class, otherwise x is notiAnd xjAre classified into one category.
In S212, a cluster center of each class is obtained, wherein the cluster center cjObtained by the following formula:
wherein J is the index xjNumber of composite indices, x, classified into one classkIs the value of the kth composite indicator. The overall error for all classes is obtained by:
wherein x iskiIs equal to xjIth value, x of Kth composite index classified as onejiIs xjM is the number of values of a composite index, and K is the number of cluster centers. And if the total error is judged to be less than or equal to a fourth preset threshold value, taking the clustering center as the center of the RBF neural network. If the total error is judgedAnd if the difference is greater than the fourth preset threshold, acquiring a new path information quantity according to the distance from the comprehensive index to the clustering center and the modified pheromone persistence coefficient, and iteratively performing clustering and determining the clustering center by using the new path information quantity until the total error is less than or equal to the fourth preset threshold. Obtaining the distance d between each comprehensive index and a new clustering centerijModifying the persistence coefficient rho of the pheromone, the new path information content of said formulaObtained by the following formula:
wherein,q is a constant for the amount of path information in the last iteration.
On the basis of the foregoing embodiment, step S23 in this embodiment specifically includes: acquiring an output value of each hidden unit of the hidden layer, and normalizing the output value; and if the normalized output value is judged to be smaller than a fifth preset threshold value, removing the hidden unit corresponding to the output value.
Specifically, obtaining an output value of each hidden unit of the hidden layer, and normalizing the output values includes: obtaining a maximum value of the output values, and dividing the output value of each implicit unit by the maximum value. And if the normalized output value is judged to be smaller than a fifth preset threshold value, removing the hidden unit corresponding to the output value.
In the embodiment, the hidden units with output values not meeting the conditions are removed by optimizing the hidden units, so that the structure of the RBF neural network is simplified and the operation speed is increased under the condition of ensuring the accuracy of the RBF neural network.
On the basis of the foregoing embodiment, in this embodiment, before the step S211, the method further includes: initializing the path information quantity between any two comprehensive indexes according to the Euclidean distance between the two comprehensive indexes.
Specifically, any two composite indices x are calculated by the following formulaiAnd xjHas an Euclidean distance d betweenij：
dij＝||(xi-xj)||2，i，j＝1，2...n，
Wherein n is the number of the comprehensive indexes. Path information amount tau for said two synthetic indexesijAnd (3) initializing:
wherein r is a sixth preset threshold.
On the basis of the foregoing embodiment, the step S2 in this embodiment further includes: obtaining water quality evaluation results of a plurality of positions in one area according to the steps S1 and S2; and acquiring a relation curve between the coordinates of the positions and the water quality evaluation result according to the coordinates of the positions and the water quality evaluation result of each position.
Specifically, water quality index values of a plurality of positions in an area are obtained, a principal component analysis method is used for obtaining a comprehensive index value of each position, and an ant colony optimization RBF neural network is used for obtaining a water quality evaluation result of each position. And acquiring a relation curve between the coordinates of the positions and the water quality evaluation result according to the coordinates of the positions and the water quality evaluation result of each position. Through the relation curve, the position with poor water quality evaluation result in the area can be obtained, the position with poor water quality evaluation result is combined with the nearby human activities, and protective measures are taken for the environment in time.
In the embodiment, the relation curve between the coordinates of the positions and the water quality evaluation result is obtained by obtaining the water quality evaluation results of a plurality of positions in one area and according to the coordinates of each position and the water quality evaluation result of each position, so that the position with poor water quality evaluation result can be obtained, and the environment can be protected in time.
In another embodiment of the present invention, a water quality evaluating apparatus is provided, and fig. 5 is a structural block diagram of the water quality evaluating apparatus provided in the embodiment of the present invention, as shown in fig. 5, the apparatus includes a combining unit 1 and an obtaining unit 2, wherein:
the combination unit 1 is used for combining various acquired water quality index values into a comprehensive index value by using a principal component analysis method; the acquisition unit 2 is configured to acquire a water quality evaluation result by using an ant colony optimization-based RBF neural network according to the comprehensive index value.
Specifically, the water quality index includes physical and chemical properties of water quality, and the water quality index value may be a value of a water quality index collected in a preset time period with a preset duration as a cycle. In consideration of the correlation between the water quality indexes, the combination unit 1 combines the various water quality indexes into a comprehensive index by using the principal component analysis method, and combines the obtained values of the various water quality indexes into a value of the comprehensive index. The principal component analysis method is a multivariate mathematical statistical method mainly used for reducing dimension, and converts a plurality of water quality indexes into a few comprehensive indexes. The water quality indexes have correlation, one group of related water quality indexes are converted into another group of unrelated indexes through linear change by the principal component analysis method, and the total variance of the water quality indexes is kept unchanged in the conversion process. And arranging the transformed water quality indexes according to the sequence that the variances are sequentially decreased, wherein the water quality index after the first transformation has the largest variance and is called as a first main component, the variance of a second main component is larger, and is not related to the first main component, and so on.
And the acquisition unit 2 takes the comprehensive index value as the input of the RBF neural network model optimized by the ant colony algorithm to acquire a water quality evaluation result. The ant colony algorithm is also called as ant algorithm, and is a probability algorithm for searching an optimized path in a graph. The ant colony algorithm is firstly proposed by Dorigo and the like for the first time, is a bionic optimization algorithm developed based on collective foraging behavior of a biological ant colony system, and has the advantages of parallel distributed computation, strong global optimization capability, strong adaptability, easiness in combination with other algorithms and the like. The RBF neural network is a forward network formed by three layers, wherein the first layer is an input layer, and the number of nodes is equal to the input dimension; the second layer is a hidden layer, and the number of nodes depends on the complexity of the problem; the third layer is an output layer, and the number of nodes is equal to the dimension of the output data. The number of hidden layer neurons is determined according to the complexity of the problem, and although more hidden layer neurons can make the RBF neural network more accurate, too many hidden layer neurons can make the RBF neural network training time too long and generate an overfitting problem.
In the embodiment, the obtained various water quality index values are combined into the comprehensive index value by using a principal component analysis method, and the dimension reduction is performed on the various water quality index values according to the interrelation among the various water quality indexes, so that the data structure is simplified, and the operation speed is increased. Meanwhile, according to the comprehensive index value, the water quality evaluation result is obtained by using the RBF neural network optimized by the ant colony algorithm, the convergence speed is high, the network structure is simple, the robustness is strong, and the local minimum point is not easy to fall into.
This embodiment provides a water quality evaluation device, water quality evaluation device includes: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for information transmission between the test equipment and the communication equipment of the display device; the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method provided by the method embodiments, for example, the method includes: s1, combining the obtained water quality index values into a comprehensive index value by using a principal component analysis method; and S2, obtaining a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including: s1, combining the obtained water quality index values into a comprehensive index value by using a principal component analysis method; and S2, obtaining a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the test equipment and the like of the display device are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A water quality evaluation method is characterized by comprising the following steps:
s1, combining the obtained water quality index values into a comprehensive index value by using a principal component analysis method;
and S2, acquiring a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
2. The water quality evaluation method according to claim 1, wherein the step S1 specifically includes:
standardizing the water quality index value to obtain a matrix of the standardized water quality index value;
acquiring the accumulated contribution rate of each principal component according to the eigenvalue of the matrix;
and taking the value of the principal component of which the characteristic value is greater than a first preset threshold value and the accumulated contribution rate is greater than a second preset threshold value as the comprehensive index value.
3. The water quality evaluation method according to claim 1, wherein the step S2 specifically includes:
s21, clustering the comprehensive index value by using the ant colony algorithm to obtain a clustering center, and taking the clustering center as the center of the RBF neural network;
s22, obtaining the weight from the hidden layer to the output layer in the RBF neural network by using a back propagation algorithm;
s23, according to the output of the hidden unit of the hidden layer, clipping the hidden unit.
4. The water quality evaluation method according to claim 3, wherein the step S21 specifically comprises:
s211, according to the path information quantity between any two comprehensive indexes, acquiring the probability of clustering one comprehensive index to the other comprehensive index in the two comprehensive indexes, and if the probability is judged to be larger than a third threshold value, classifying the two comprehensive indexes into one class;
s212, acquiring a clustering center of each class and a total error of all the classes, and if the total error is judged to be less than or equal to a fourth preset threshold, taking the clustering center as the center of the RBF neural network; or,
if the total error is judged to be larger than the fourth preset threshold, acquiring a new path information quantity according to the distance from the comprehensive index to the clustering center and the modified pheromone persistence coefficient, and iteratively executing clustering and determining the clustering center by using the new path information quantity until the total error is smaller than or equal to the fourth preset threshold.
5. The water quality evaluation method according to claim 3, wherein the step S23 specifically comprises:
acquiring an output value of each hidden unit of the hidden layer, and normalizing the output value;
and if the normalized output value is judged to be smaller than a fifth preset threshold value, removing the hidden unit corresponding to the output value.
6. The water quality evaluation method according to claim 4, further comprising, before step S211:
initializing the path information quantity between any two comprehensive indexes according to the Euclidean distance between the two comprehensive indexes.
7. The water quality evaluation method according to claim 1, further comprising, after step S2:
obtaining water quality evaluation results of a plurality of positions in one area according to the steps S1 and S2;
and acquiring a relation curve between the coordinates of the positions and the water quality evaluation result according to the coordinates of the positions and the water quality evaluation result of each position.
8. A water quality evaluation device is characterized by comprising:
the combination unit is used for combining the acquired various water quality index values into a comprehensive index value by using a principal component analysis method;
and the acquisition unit is used for acquiring a water quality evaluation result by using the RBF neural network optimized by the ant colony algorithm according to the comprehensive index value.
9. A water quality evaluation apparatus characterized by comprising:
at least one processor, at least one memory, a communication interface, and a bus; wherein,
the processor, the memory and the communication interface complete mutual communication through the bus;
the communication interface is used for information transmission between the test equipment and the communication equipment of the display device;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 7.
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