CN110837924B - Water turbidity prediction method - Google Patents
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Abstract
The invention discloses a water quality turbidity prediction method, which is used for predicting the water quality turbidity of a water area to be detected, setting sampling points and boundary points in the water area to be detected, and acquiring a prediction point set N in the water area to be detected according to coordinates of the boundary points; then sequentially selecting points in the prediction point set N as interpolation points Z, and calculating water turbidity predicted values of the Z points corresponding to the two sampling points by using a linear interpolation method by taking the two sampling points in the sampling point set as reference points; and finally, sequencing each predicted value in the water quality turbidity predicted value result set corresponding to the Z point, selecting the front TR predicted values with large predicted values, and calculating the average value of the TR predicted values to be used as the final predicted value of the water quality turbidity of the Z point. The invention improves the accuracy of water quality parameter NTU prediction, and provides a novel prediction method with certain accuracy and convenience for the water quality prediction of small-area lakes.
Description
Technical Field
The application belongs to the technical field of water quality detection, and particularly relates to a water turbidity prediction method.
Background
Geographic Information System (GIS) is a comprehensive discipline that has been widely used in various fields in conjunction with geography and topography as well as remote sensing and computer disciplines, and is a computer System for inputting, storing, querying, analyzing and displaying Geographic data. Common statistical methods cannot monitor all points in a spatial deployment, but can obtain a certain number of spatial sample points in the whole water area, reflect partial or full features of spatial distribution through the spatial sample points, and predict unknown geospatial features through the method. In a sense, spatial interpolation can be considered as an effective method for predicting data values of unknown space by spatial data already owned.
In recent years, many domestic scholars have made many results on the research of a GIS-based spatial interpolation method. For example, in 2012, holena, southbound and the like proposed a collaborative kriging interpolation method introducing a monthly average total cloud amount influence factor for the defects of an inverse distance weight interpolation method, a spline function interpolation method and a common kriging interpolation method in the precipitation space interpolation, and experiments were performed using the inner Mongolia precipitation data as an example, and the results show that the collaborative kriging interpolation introducing the monthly average total cloud amount influence factor is improved in terms of precision and fitting degree. In 2013, Wuhongyan, Chengdongsheng, Wedney, Huxiao, Kudzuvine, Junge and the like use water quality factors in hong lake 2007 and Bing Cheng Wei in 2010 as research objects, and 5 models such as a global polynomial, a local polynomial, a radial basis function, an inverse distance weight, a common kriging interpolation method and the like which are commonly used in geostatistics are selected for researching and analyzing the water quality of the hong lake, and the mass concentration of the total nitrogen of the hong lake is found to have different spatial distribution characteristics. In 2017, Sunyinghui, Liao, Stazechwan, Hongliang and the like aim at the problem that the traditional water quality parameter statistical method based on discrete sampling points cannot analyze the indexes of the water quality parameters of the lake in detail, so that a Kriging interpolation algorithm and an inverse distance weighted interpolation algorithm are selected to research and analyze the spatial distribution rule of the water quality parameters in summer in Yangzhong sea, the accuracy of the Kriging interpolation algorithm is found to be higher than that of the IDW interpolation algorithm through cross validation, and the experimental result provides a theoretical basis for slightly higher Yangzhong seawater environment assessment and comprehensive improvement.
Many contributions have been made by many foreign scholars on the application of spatial interpolation methods, for example, 2018, S Prasetiyowati, Y sibaroi et al propose to predict the number of DHF patients in 2016 to 2018 using IDW and kriging based on data from 2010 to 2015 for the current problem of not high accuracy of predicting DHF disease transmission patterns, and from experimental results, it was shown that IDW and kriging predicted DHF disease transmission patterns are approximate patterns over a period of time. Jasim H S, Mustafa T and the like respectively use eight interpolation methods of reverse distance weighting, Thiessen polygon, trend surface analysis, local polynomial interpolation, thin plate spline, common kriging, general kriging, simple kriging and the like to carry out interpolation experiments on precipitation and temperature in an integration period, and the experimental results show that the prediction precision of the common kriging is higher.
However, the research methods in the prior art still have the problems of low prediction precision, low stability and the like.
Disclosure of Invention
The application aims to provide a water quality turbidity prediction method, which overcomes the problems of the traditional IDW algorithm and the traditional kriging algorithm, predicts the water quality parameters of the Linan east lake by using the interpolation algorithm, and compares the prediction results with the prediction results of the traditional IDW interpolation algorithm and the traditional kriging algorithm, and the result shows that the interpolation algorithm is slightly better than the traditional algorithm and can objectively reflect the water quality change trend of the Linan east lake.
In order to achieve the purpose, the technical scheme of the application is as follows:
a water quality turbidity prediction method is used for predicting the water quality turbidity of a water area to be detected and comprises the following steps:
setting sampling points and boundary points in a water area to be detected, and acquiring the water turbidity of the sampling points;
establishing a rectangular coordinate system according to the coordinates of the boundary points, acquiring a crossing point set of equidistant y-axis vertical lines and x-axis vertical lines in the water area to be detected, and removing a sampling point set from the crossing point set to obtain a prediction point set N to be interpolated;
sequentially selecting points in the prediction point set N as interpolation points Z, taking two sampling points in the sampling point set as reference points, calculating the water quality turbidity predicted values of the Z points corresponding to the two sampling points by using a linear interpolation method, traversing the combination of two different sampling points in the sampling point set, and obtaining a water quality turbidity predicted value result set corresponding to the Z points;
and sequencing each predicted value in the water quality turbidity predicted value result set corresponding to the Z point, selecting the front TR predicted values with large predicted values, and calculating the average value of the TR predicted values to be used as the final predicted value of the water quality turbidity of the Z point.
Further, the step of calculating the water turbidity prediction values of the Z point corresponding to the two sampling points by using the two sampling points in the sampling point set as reference points and using a linear interpolation method includes:
obtaining a straight line of the two sampling points according to the coordinates of the two sampling points, and calculating the slope of the straight line;
obtaining the vertical lines of two sampling point straight lines passing through the Z point by a point-slope equation method;
calculating the distance between two sampling points and the distance between one sampling point and a foot point of the vertical line and the straight line according to the straight line and the vertical line;
and performing interpolation calculation according to the water turbidity of the two sampling points, the distance between the two sampling points and the distance between one of the two sampling points and the foot point of the vertical line and the straight line, and calculating the water turbidity predicted values of the Z point corresponding to the two sampling points.
Further, the calculating the predicted value of the water turbidity corresponding to the two sampling points by performing interpolation calculation according to the water turbidity of the two sampling points, the distance between the two sampling points and the distance between one of the two sampling points and the foot point of the vertical line and the straight line, and calculating the predicted value of the water turbidity corresponding to the two sampling points by the Z point includes:
wherein, NTUDiAs predicted value of water turbidity at Z point, NTUA2Is the water turbidity, NTU, of the second of the two samplesA1The water quality turbidity of the first of the two sampling points, | A1A2| is the distance between the two sampling points, | A2Di | is the distance between the second sampling point and the plumbing point.
Further, the TR is a preset empirical value, and preferably, the TR is 14.
Further, sequencing each predicted value in the water turbidity predicted value result set corresponding to the Z point, selecting the first TR predicted values with large predicted values, and adopting a fast-sequencing TopK algorithm.
According to the water quality turbidity prediction method, the TopK algorithm is introduced into water quality prediction, and compared with the traditional kriging algorithm and the IDW inverse distance weighting algorithm, the accuracy of water quality parameter NTU prediction is improved.
Drawings
FIG. 1 is a flow chart of a water turbidity prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of interpolation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The application provides a water turbidity prediction method, as shown in fig. 1, comprising the following steps:
and step S1, setting sampling points and boundary points in the water area to be detected, and acquiring the water turbidity of the sampling points.
The embodiment sets sampling points in a water area to be detected, the water area to be detected can be a lake or a pond pit, the number of the sampling points can be more or less, the more the sampling points are set, the more accurate the subsequent calculation result is, and the final effect is considered, so that the number of the sampling points does not need to be too large. And after the sampling point is set, sampling the water turbidity of the sampling point to be used as a basis for subsequent calculation. For subsequent verification, verification points can be set, and the water quality turbidity of the verification points is also sampled to serve as the basis of the subsequent verification.
In this embodiment, the eastern Lin lake is taken as an example, the eastern Lin lake is located in the insane area of Hangzhou city and is a manually excavated lake, and the average water depth is 1.5 m. In consideration of the accuracy and convenience of the experiment, the east lake area in the campus of agriculture and forestry university in Zhejiang is selected as a research area, the east lake is located in the southwest side of the school, the floor area is about 44000 square meters, common aquatic plants and aquatic animals are also in water, and the water environment forming standard of a small micro water area is met.
Setting 60 sampling points in a test area of the lake surface of the Linan east lake, wherein the sampling points are 25 and are used as a sampling point set, 35 verification points form a verification point set, and 108 points are selected around the lake surface and are used as a boundary point set to define the boundary of the lake surface. And then, carrying a water quality monitoring instrument by using the unmanned mobile ship to obtain water quality parameter information of a research area, removing abnormal data from the obtained water quality parameter information, and using the processed data for subsequent prediction.
And step S2, establishing a rectangular coordinate system according to the coordinates of the boundary points, acquiring an intersection set of equidistant y-axis vertical lines and x-axis vertical lines in the water area to be detected, and removing the sampling point set from the intersection set to obtain a prediction point set N to be interpolated.
According to the embodiment, a rectangular coordinate system is established according to the geographic position coordinates of the boundary points, so that the intersection point set of equidistant y-axis vertical lines and x-axis vertical lines in the water area to be detected can be obtained. For example, a rectangular coordinate system may be established with an edge of the lake surface as the x-axis, so as to obtain the coordinates of the intersection point by making a perpendicular line to the x-axis and the y-axis according to the geographic position coordinates of the boundary points, which may be obtained according to basic geometric mathematics, and will not be described herein again.
In this embodiment, the sampling point set is removed from the intersection point set, and a prediction point set N to be interpolated is obtained.
And S3, sequentially selecting points in the prediction point set N as interpolation points Z, using two sampling points in the sampling point set as reference points, calculating the water quality turbidity predicted values of the Z points corresponding to the two sampling points by using a linear interpolation method, traversing the combination of two different sampling points in the sampling point set, and obtaining a water quality turbidity predicted value result set corresponding to the Z points.
In this embodiment, points in the prediction point set N are sequentially selected as interpolation points Z, a water turbidity prediction value of the Z point is obtained by the interpolation method of the present application, and all the points in the prediction point set N are traversed.
For the selected Z point, as shown in FIG. 2, assume A with reference to any two sampling points1、A2Wherein the coordinate of A1 in the rectangular coordinate system is (X)A1，YA1) A2 in a rectangular coordinate systemHas the coordinates of (X)A2，YA2) And acquiring the straight line of the two sampling points according to the coordinates of the two sampling points, and calculating the slope of the straight line.
The two-point equation method is used for expressing the A1A2 linear equation, namely the equation is as follows:
the finishing can be carried out as follows:
Y＝kX+b (2)
both parameters k and b can be obtained by the formula (1).
And obtaining the vertical lines of the straight lines of the two sampling points passing through the Z point by a point-slope equation method.
According to the oblique equation method, the vertical line equation of the A1A2 straight line passing through the Z point is as follows:
finishing to obtain:
and calculating the distance between two sampling points and the distance from one sampling point to the foot point of the vertical line and the straight line according to the straight line and the vertical line.
Combining the equations (2) and (3), the coordinate of the drop foot point Di can be obtained as (X)Di，YDi)。
According to the distance formula, one can find:
and carrying out interpolation calculation according to the calculation result, and calculating the water turbidity predicted values of the Z points corresponding to the two sampling points.
Let the water turbidity (NTU) value of A1 be NTUA1Let the NTU value of A2 be NTUA2Let the NTU value of Di position be NTUDiAccording to the formula, the NTU can be calculatedDi：
Wherein, NTUDiAs predicted water turbidity at Z point, NTUA2Is the water turbidity, NTU, of the second of the two samplesA1The water quality turbidity of the first of the two sampling points, | A1A2| is the distance between the two sampling points, | A2Di | is the distance between the second sampling point and the plumbing point. Will NTUDiThe predicted NTU as the insertion point Z was based on the A1 and A2 sampling points.
According to the method, every time two sampling points are selected as reference points, combinations of two different sampling points in the sampling point set are traversed, and a water turbidity predicted value result set corresponding to the Z point is obtained. It will be readily appreciated that different combinations of sample points in the set of sample points, that is to say that the two sample points in each combination are not identical, for example (a1, a2) and (a1, A3) are different, as are (a1, a2) and (A3, a 4). That is, if any two sampling points in the sampling point set are connected, each connection corresponds to one combination. However, if there are M such combinations, M predicted values at the Z point can be obtained by the method of the present application, and a water turbidity predicted value result set corresponding to the Z point is formed.
And S4, sequencing each predicted value in the water quality turbidity predicted value result set corresponding to the Z point, selecting the first TR predicted values with large predicted values, and calculating the average value of the TR predicted values as the final water quality turbidity predicted value of the Z point.
In this embodiment, a fast-draining ordering TopK algorithm is adopted, the first TR predicted values with large predicted values are selected from the predicted value result set, and the average value of the TR predicted values is calculated as the final predicted value of the water turbidity at the Z point.
And sequentially selecting points in the prediction point set N as interpolation points Z, and traversing all the points in the prediction point set N, thereby predicting the final water quality turbidity prediction value of the prediction points.
It should be noted that specific values of TR may be preset, in this embodiment, TR values 16, 15, 14, 13, and 12 are respectively selected for experiments, and finally, the predicted results are compared through the verification points, and the accuracy is the highest when the TR value is determined to be 14.
Finally, the predicted value of the verification point is taken out and compared and analyzed with the real sampling value of the verification point, so that the accuracy of the prediction method is verified, and the accuracy of the water quality parameter NTU prediction is improved by comparing the technical scheme with the traditional kriging algorithm and the IDW inverse distance weighting algorithm.
According to the method, a kriging interpolation algorithm and an IDW inverse distance interpolation algorithm are selected for comparison and verification, and the comparison results are as follows:
TABLE 1
Wherein Kriging is a Kriging interpolation algorithm, IDW is an IDW inverse distance interpolation algorithm, TRI14 is the method of the application, MAE is the average absolute error, and MRE is the average relative error.
The verification results show that all three algorithms can predict the turbidity parameter of water quality more effectively, but the absolute error of TRI14 is lower than that obtained by the former two methods, namely the predicted value is closer to the actual value. The average relative error and the average absolute error of the prediction results of the three algorithms on the water turbidity parameters are shown in table 1. From table 1, the average absolute error of the TRI algorithm is 3.09, which is lower than the average absolute error of the prediction result of the kriging interpolation algorithm of 4.16 and the average absolute error of the prediction result of the inverse distance weighting algorithm of 4.40. Meanwhile, the average relative error of prediction is greatly improved, and the average relative error of the obtained TRI algorithm is 1.93 percent, which is lower than the average relative error of the prediction result of the kriging interpolation algorithm by 2.57 percent and the average relative error of the prediction result of the inverse distance weighting algorithm by 2.72 percent. The results predicted by the TRI algorithm are less discrete and closer to the actual values. Therefore, the TRI algorithm of the application is more accurate than the prediction of the kriging interpolation algorithm and the IDW inverse distance weighting algorithm, and has certain research reference significance. The accuracy of the water quality parameter NTU is improved, so that the TRI algorithm provides a novel prediction method which is simple, convenient and has certain precision for the water quality prediction of small-area lakes.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. A water quality turbidity prediction method is used for predicting the water quality turbidity of a water area to be detected, and is characterized by comprising the following steps:
setting sampling points and boundary points in a water area to be detected, and acquiring the water turbidity of the sampling points;
establishing a rectangular coordinate system according to the coordinates of the boundary points, acquiring an intersection set of equidistant y-axis vertical lines and x-axis vertical lines in a water area to be detected, and removing a sampling point set from the intersection set to obtain a prediction point set N to be interpolated;
sequentially selecting points in the prediction point set N as interpolation points Z, taking two sampling points in the sampling point set as reference points, calculating the water quality turbidity predicted values of the Z points corresponding to the two sampling points by using a linear interpolation method, traversing the combination of two different sampling points in the sampling point set, and obtaining a water quality turbidity predicted value result set corresponding to the Z points;
sequencing each predicted value in a water quality turbidity predicted value result set corresponding to the Z point, selecting the first TR predicted values with large predicted values, and calculating the average value of the TR predicted values to be used as the final predicted value of the water quality turbidity of the Z point;
the method for calculating the water turbidity predicted values of the Z points corresponding to the two sampling points by using the two sampling points in the sampling point set as reference points and using a linear interpolation method comprises the following steps:
obtaining a straight line of the two sampling points according to the coordinates of the two sampling points, and calculating the slope of the straight line;
obtaining the vertical lines of two sampling point straight lines passing through the Z point by a point-slope equation method;
calculating the distance between two sampling points and the distance between one sampling point and a foot point of the vertical line and the straight line according to the straight line and the vertical line;
and performing interpolation calculation according to the water turbidity of the two sampling points, the distance between the two sampling points and the distance between one of the two sampling points and the foot point of the vertical line and the straight line, and calculating the water turbidity predicted values of the Z point corresponding to the two sampling points.
2. The method for predicting the water turbidity according to claim 1, wherein the step of calculating the predicted value of the water turbidity corresponding to the two sampling points at the Z point by performing interpolation calculation according to the water turbidity of the two sampling points, the distance between the two sampling points and the distance between one of the two sampling points and the foot point of the vertical line and the straight line comprises the following steps:
wherein, NTUDiAs predicted value of water turbidity at Z point, NTUA2Is the water turbidity, NTU, of the second of the two samplesA1The water quality turbidity of the first of the two sampling points, | A1A2| is the distance between the two sampling points, | A2Di | is the distance between the second sampling point and the plumbing point.
3. A method of predicting turbidity in water according to claim 1, wherein said TR is a predetermined empirical value.
4. The method for predicting turbidity of water according to claim 3, wherein said TR is 14.
5. The method for predicting the turbidity of water according to claim 1, wherein each predicted value in the result set of the water turbidity predicted values corresponding to the Z point is sorted, the first TR predicted values with larger predicted values are selected, and a fast-sorting TopK algorithm is adopted.
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