CN113673155B - Water area sand content inversion method based on support vector machine - Google Patents
Water area sand content inversion method based on support vector machine Download PDFInfo
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- CN113673155B CN113673155B CN202110944949.7A CN202110944949A CN113673155B CN 113673155 B CN113673155 B CN 113673155B CN 202110944949 A CN202110944949 A CN 202110944949A CN 113673155 B CN113673155 B CN 113673155B
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- 239000004576 sand Substances 0.000 title claims abstract description 57
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000012706 support-vector machine Methods 0.000 title claims abstract description 17
- 101100041681 Takifugu rubripes sand gene Proteins 0.000 claims abstract description 56
- 238000012549 training Methods 0.000 claims abstract description 20
- 238000012360 testing method Methods 0.000 claims abstract description 14
- 238000005259 measurement Methods 0.000 claims abstract description 8
- 238000012216 screening Methods 0.000 claims abstract description 8
- 238000007689 inspection Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 abstract description 4
- 230000001360 synchronised effect Effects 0.000 abstract description 2
- 239000013049 sediment Substances 0.000 description 5
- 238000011835 investigation Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
Abstract
The invention discloses a water area sand content inversion method based on a support vector machine, which belongs to the field of geographic information technology and water environment monitoring, and comprises the specific operation steps of firstly screening sensitive wave bands with different levels of sand content by utilizing a data set formed by measured data and multispectral remote sensing data; then, taking the point scale as a training scale and a testing scale, selecting an optimal parameter by using training data, and outputting an optimal model for inversion of sand contents at different depths by using a testing data set; and then upscaling the inverse prediction model of the sand content at different depths, of which the point scale is higher than the threshold value, to the scale of the whole water area, and calculating the spatial distribution of the sand content at different depths of the whole water area. The invention is not limited by the on-site monitoring environment, solves the limitation of manual point scale measurement, and can realize inversion of the sand content of the upper layer of the water area in a large-area synchronous remote manner.
Description
Technical Field
The invention belongs to the field of geographic information technology and water environment monitoring, and particularly relates to a water area sand content inversion method based on a support vector machine.
Background
The content of suspended sediment in the water body directly influences the optical properties of the water body, such as transparency, turbidity, water color and the like, and also influences the ecological conditions of the water body and the erosion and deposition change process of a channel. Therefore, the method has qualitative and quantitative knowledge on the suspended sand content of the water area at the Yangtze river mouth, and has important significance on the safe operation of the Yangtze river channel and the construction of coastal zone engineering. The conventional investigation method is to sample and analyze point by using a ship. However, in the conventional investigation method, large-area sediment sampling is needed to obtain large-scale sand content distribution in the estuary area, the investigation speed is slow, the period is long, and the observation cost is high. Moreover, the point scale acquisition mode can only acquire data of a small number of points which are very discrete in time and space distribution. The satellite remote sensing technology has the advantages of wide information coverage area, multiple time phases and multiple wave bands, can reflect the water body condition of a large area of a water area, has instant synchronization signals, has relatively short period for repeatedly acquiring data, and can effectively monitor the distribution and dynamic change of the sand content. In addition, the satellite remote sensing data can objectively and truly reflect the information of the water body, so that the space and time change rule of the suspended sediment is inverted. In recent years, the increase of the number of high-resolution remote sensing satellites at home and abroad provides more data support for inverting the concentration of suspended sediment in a water area. A plurality of scholars at home and abroad use remote sensing images to research the sand content information of different water areas, but because the sand content information of the actual water body is difficult to acquire and is difficult to be consistent with the shooting time of a satellite, the number of actually measured data samples is small, and therefore, a plurality of scholars usually adopt linear models, index models or empirical formulas of different areas to simulate the sand content of the water body. However, the linear model, the exponential model and the simple nonlinear model cannot accurately reflect the real spatial distribution relation of the suspended sediment concentration of the water body.
Disclosure of Invention
The invention aims to provide a water sand content inversion method based on a support vector machine, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a water area sand content inversion method based on a support vector machine comprises the following specific operation steps:
step 1, carrying out spatial position matching on actually measured sand content data obtained by point scale and high-resolution remote sensing multispectral data of corresponding time, extracting multispectral information of corresponding points, and forming a training and testing data set consisting of different levels of sand content and spectral information.
And 2, determining the optimal parameters of the training model by adopting a two-time lattice point search and ten-fold cross inspection method based on the training data set, and outputting the optimal inversion model based on the test data set.
And 3, taking a single grid where the actual measurement points are located as a calculation unit, calculating the sand content of different levels by using the optimal model, performing precision verification, and after the verification is passed, upscaling the grid unit to the whole water area to form a spatial distribution calculation result of the sand content of the water area.
As a further scheme of the invention: the step S1 comprises the following specific steps:
1) Inputting the longitude and latitude of the water area real measuring point into geographic information software, and carrying out spatial position matching on the actually measured sand content data obtained by the point scale and the high-resolution remote sensing multispectral data of corresponding time;
2) Selecting data of each wave band of the image grid corresponding to the actual measurement points, and forming a test data set with actually measured sand content of different layers;
3) And (3) adopting a support vector machine model training model, sequentially removing each wave band, checking the model training precision after the wave bands are removed, and selecting the wave bands with the precision reduced by more than a certain threshold value as sensitive wave bands.
As a further scheme of the invention: the step S2 comprises the following specific steps:
1) Setting two parameter screening sections in different intervals by depending on a support vector machine model, and acquiring the optimal value of each parameter by adopting a ten-fold cross inspection method;
2) And outputting an optimal model capable of inverting the sand content at different depths as an inversion model by utilizing the test data set and combining the optimal parameters.
As a still further scheme of the invention: the step S3 comprises the following specific steps:
1) Taking the grid as a unit to perform inversion prediction of sand contents at different depths;
2) Taking the numerical values of the sand contents at different depths of the actual measuring points as a reference, and verifying the accuracy of the sand contents at different depths predicted by the model; setting a certain threshold value, and returning to the first step to restart the training if the precision is lower than the threshold value;
3) And (4) upscaling the inverse prediction model of the sand content at different depths, of which the point scale is higher than the threshold value, to the scale of the whole water area, and calculating the spatial distribution of the sand content at different depths of the whole water area.
Compared with the prior art, the method is not limited by the environment of on-site monitoring, solves the limitation of manual point scale measurement, and can realize inversion of the sand content of the upper layer of the water area in a large-area synchronous and remote manner.
Drawings
Fig. 1 is a schematic flow chart of a water sand content inversion method based on a support vector machine.
Fig. 2 is a schematic diagram of inversion of sand content in a water area based on a support vector machine.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Referring to fig. 1-2, a water sand content inversion method based on a support vector machine includes the following steps:
s1, screening of sensitive wave bands: and screening out sensitive wave bands with different levels of sand content by utilizing a data set formed by the measured data and the multispectral remote sensing data.
To further explain the working process, the step S1 includes the following specific steps:
1) And inputting the longitude and latitude of the water area actual measurement point into geographic information software, so that the actual measurement sand content data obtained by the point scale and the high-resolution remote sensing multispectral data of corresponding time are subjected to spatial position matching.
2) Selecting data of each wave band of the image grid corresponding to the actual measurement points, and forming a test data set with the actually measured sand content of different layers;
3) And (3) adopting a support vector machine model training model, sequentially removing each wave band, checking the model training precision after the wave band is removed, and selecting the wave band with the precision reduced by more than a certain threshold value as a sensitive wave band.
S2, outputting an optimal inversion model: and (3) selecting the optimal parameters by using the training data and outputting the optimal model inverted by the sand contents at different depths by using the test data set by using the point scale as the training and testing scale.
To further explain the working process, the step S2 includes the following specific steps:
1) Setting two parameter screening sections in different intervals by depending on a support vector machine model, and acquiring the optimal value of each parameter by adopting a ten-fold cross inspection method;
2) And outputting an optimal model capable of inverting the sand content at different depths as an inversion model by using the test data set and combining the optimal parameters.
And S3, upscaling the training model of the point scale to the surface scale to obtain a spatial distribution inversion result of the sand content of the large-area water area.
To further explain the working process, step S3 includes the following specific steps:
1) Taking the grid as a unit to perform inversion prediction of sand contents at different depths;
2) Taking the numerical values of the sand contents at different depths of the actual measuring points as a reference, and verifying the accuracy of the sand contents at different depths predicted by the model; setting a certain threshold value, and returning to the first step to restart the training if the precision is lower than the threshold value;
3) And (4) upscaling the inverse prediction model of the sand content at different depths, of which the point scale is higher than the threshold value, to the scale of the whole water area, and calculating the spatial distribution of the sand content at different depths of the whole water area.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
Although the preferred embodiments of the present patent have been described in detail, the present patent is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present patent within the knowledge of those skilled in the art.
Claims (2)
1. A water sand content inversion method based on a support vector machine is characterized by comprising the following specific operation steps:
s1, screening of sensitive wave bands: screening out sensitive wave bands with different levels of sand content by utilizing a data set formed by the measured data and the multispectral remote sensing data, and specifically comprising the following steps:
inputting the longitude and latitude of the real-time measuring points of the water area into geographic information software, and carrying out spatial position matching on the measured sand content data obtained by the point scale and the high-resolution remote sensing multispectral data at corresponding time;
selecting data of each wave band of the image grid corresponding to the actual measurement points, and forming a test data set with actually measured sand content of different layers;
adopting a support vector machine model training model, sequentially removing each wave band, checking the model training precision after the wave band is removed, and selecting the wave band with the precision reduced by more than a certain threshold value as a sensitive wave band;
s2, outputting an optimal inversion model: selecting the optimal parameters by using the training data and outputting the optimal model inverted by the sand contents at different depths by using the test data set by using the point scale as the training scale and the test scale;
s3, upscaling the training model of the point scale to the surface scale to obtain a spatial distribution inversion result of the sand content of the large-area water area, which comprises the following steps:
taking the grid as a unit to perform inversion prediction of sand contents at different depths;
verifying the accuracy of the sand contents at different depths predicted by the model by taking the numerical values of the sand contents at different depths of the actual measuring points as a reference; a certain threshold value is set, and if the precision is lower than the threshold value, the training is restarted by returning to the first step;
and (4) upscaling the inversion prediction model of the sand content at different depths, of which the point size is higher than the threshold value, to the size of the whole water area, and calculating the spatial distribution of the sand content at different depths of the whole water area.
2. The water sand content inversion method based on the support vector machine as claimed in claim 1, wherein the step S2 comprises the following specific steps:
1) Setting two parameter screening sections in different intervals by depending on a support vector machine model, and acquiring the optimal value of each parameter by adopting a ten-fold cross inspection method;
2) And outputting an optimal model capable of inverting the sand content at different depths as an inversion model by using the test data set and combining the optimal parameters.
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