CN111722306A - Typhoon landing intensity prediction method and system based on TCN network model - Google Patents
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Abstract
The invention provides a method and a system for predicting typhoon landing intensity based on a TCN network model, wherein the prediction method comprises the following steps: acquiring historical data of typhoon occurrence, wherein the historical data comprises typhoon factors when a set distance is reserved between a typhoon center and a landing place and typhoon landing strength; establishing a TCN network model, and training the established TCN network model by adopting the historical data to obtain a trained TCN network model; and acquiring a typhoon running path, acquiring a typhoon factor when a set distance exists between a typhoon center and a landing place, and combining the trained TCN network model to obtain the landing strength. The technical scheme provided by the invention can solve the problem of inaccurate prediction of the typhoon landing intensity in the prior art.
Description
Technical Field
The invention belongs to the technical field of typhoon landing intensity prediction, and particularly relates to a method and a system for predicting typhoon landing intensity based on a TCN (traffic control network) model.
Background
Typhoon, as an extreme weather event, not only can affect activities on the sea, but also can cause significant loss to the lives of people and urban economy in coastal areas. Thus, typhoon research and prediction has been a major concern in various coastal countries.
The Chinese patent application publication No. CN109902885A discloses a typhoon prediction method based on deep learning hybrid CNN-LSTM, which uses a historical tropical cyclone optimal path data set and global atmospheric marine variable data, extracts spatial features of the atmospheric variable data by using 3DCNN (three-dimensional CNN), extracts spatial features of the marine variable data by using 2DCNN (two-dimensional CNN), and extracts time series information in the occurrence and development process of typhoon by using LSTM, thereby constructing a typhoon prediction model of hybrid CNN-LSTM, and predicting whether the typhoon is formed and the path and intensity after the typhoon is formed by using the model.
The technical scheme disclosed in the patent application document is to predict the possibility of typhoon formation and the path and strength after formation according to the environmental factors of typhoon occurrence, but the prediction method is complex and the landing strength of typhoon cannot be accurately predicted.
The Chinese patent application publication No. CN104932035A discloses a typhoon intensity forecasting method and system, which is used for forecasting the typhoon intensity through a multiple linear regression model, and the forecasting method is poor in accuracy and cannot accurately forecast the landing intensity of the typhoon.
Disclosure of Invention
The invention aims to provide a typhoon landing intensity prediction method and a system based on a TCN network model, which aim to solve the problem that the typhoon landing intensity cannot be accurately predicted in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a typhoon landing intensity prediction method based on a TCN network model comprises the following steps:
(1) acquiring historical data of typhoon occurrence, wherein the historical data comprises typhoon factors when a set distance is reserved between a typhoon center and a landing place and typhoon landing strength;
(2) establishing a TCN network model, and training the established TCN network model by adopting the historical data to obtain a trained TCN network model;
(3) and acquiring a typhoon running path, acquiring a typhoon factor when a set distance exists between a typhoon center and a landing place, and combining the trained TCN network model to obtain the landing strength.
Further, the method for acquiring the typhoon running path in the step (3) comprises the following steps: acquiring the running track of each typhoon in historical data; matching the running track of the current typhoon with the running tracks of the typhoons in the historical data to obtain the running track of the typhoon with the highest similarity, and taking the running track of the typhoon as the running track of the current typhoon.
Further, the method for judging the track similarity of the two segments of typhoon motions comprises the following steps: converting longitude and latitude coordinates of the typhoon running track into a universal transverse-axis mercator projection coordinate system; calculating the space distance between each point in the two sections of running tracks; calculating the predicted distance between each point in the two sections of running tracks; calculating the distance between each section in the two sections of running tracks; calculating the accumulated distance of the two sections of running tracks; and normalizing the obtained accumulated distance, wherein the greater the accumulated distance is, the higher the similarity of the two running tracks is.
Further, the convolutional neural network model in the step (2) is
The output of the TCN network model is
o＝RELU(z+F(z))
Wherein d is an expansion factor, the value of d is {1,2, …,2n }, and n is the number of hidden layers in the TCN network model; k is the number of filters, f (i) is the ith filter, F (z) is the result of the dilation causal convolution, ziThe ith typhoon factor z is the input to the dilation causal convolution, and F (z) is the result of the dilation causal convolution.
Further, the typhoon factor comprises the radial wind intensity, the latitudinal wind intensity, the potential height and the relative humidity of the typhoon.
A system for typhoon-landing intensity prediction based on a TCN network model, comprising a processor and a memory, the memory having stored thereon a computer program for execution on the processor; when the processor executes the calculation program, the typhoon intensity prediction method is realized as follows:
(1) acquiring historical data of typhoon occurrence, wherein the historical data comprises typhoon factors when a set distance is reserved between a typhoon center and a landing place and typhoon landing strength;
(2) establishing a TCN network model, and training the established TCN network model by adopting the historical data to obtain a trained TCN network model;
(3) and acquiring a typhoon running path, acquiring a typhoon factor when a set distance exists between a typhoon center and a landing place, and combining the trained TCN network model to obtain the landing strength.
Further, the method for acquiring the typhoon running path in the step (3) comprises the following steps: acquiring the running track of each typhoon in historical data; matching the running track of the current typhoon with the running tracks of the typhoons in the historical data to obtain the running track of the typhoon with the highest similarity, and taking the running track of the typhoon as the running track of the current typhoon.
Further, the method for judging the track similarity of the two segments of typhoon motions comprises the following steps: converting longitude and latitude coordinates of the typhoon running track into a universal transverse-axis mercator projection coordinate system; calculating the space distance between each point in the two sections of running tracks; calculating the predicted distance between each point in the two sections of running tracks; calculating the distance between each section in the two sections of running tracks; calculating the accumulated distance of the two sections of running tracks; and normalizing the obtained accumulated distance, wherein the greater the accumulated distance is, the higher the similarity of the two running tracks is.
Further, the convolutional neural network model in the step (2) is
The output of the TCN network model is
o＝RELU(z+F(z))
Wherein d is an expansion factor, the value of d is {1,2, …,2n }, and n is the number of hidden layers in the TCN network model; k is the number of filters, f (i) is the ith filter, F (z) is the result of the dilation causal convolution, ziThe ith typhoon factor z is the input to the dilation causal convolution, and F (z) is the result of the dilation causal convolution.
Further, the typhoon factor comprises the radial wind intensity, the latitudinal wind intensity, the potential height and the relative humidity of the typhoon.
According to the technical scheme provided by the invention, a TCN network model for predicting typhoon landing intensity is established, a typhoon running path is obtained, a typhoon factor is obtained when a set distance is reserved between a typhoon center and a landing place, and the trained TCN network model is combined to obtain the landing intensity. According to the technical scheme provided by the invention, the typhoon landing intensity can be predicted according to the typhoon factor when the distance between the typhoon center and the landing place is set, and the problem of inaccurate prediction of the typhoon landing intensity in the prior art is solved.
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FIG. 1 is a flow chart of a method for predicting typhoon landing intensity based on a TCN network model in an embodiment of the method of the invention;
fig. 2 is a schematic diagram of a TCN network model in an embodiment of the method of the present invention.
Detailed Description
The invention provides a method and a system for predicting typhoon landing intensity based on a TCN (train communication network) model, which are used for predicting reservoir disaster and solving the problem of inaccurate prediction of reservoir disaster in the prior art.
The method comprises the following steps:
the flow of the method for predicting typhoon landing intensity based on the TCN network model provided by this embodiment is shown in fig. 1, and the method includes the following steps:
the method comprises the following steps: historical data of typhoon occurrences is obtained.
The acquired historical data of typhoon occurrence comprises typhoon factors influencing typhoon landing intensity and intensity levels of typhoon landing when a set distance is reserved between a typhoon center and a landing place.
In this embodiment, the typhoon factor affecting the typhoon landing intensity includes the radial wind intensity, the latitudinal wind intensity, the potential height, and the relative humidity of the typhoon.
Step two: and establishing a TCN network model, and training the established TCN network model by adopting historical data of typhoon occurrence to obtain a trained TCN neural network model.
Step three: the method comprises the steps of predicting the running track of typhoon to obtain the running track, obtaining typhoon factors when a set distance is reserved between the center of the typhoon and a landing place, and finally predicting the landing intensity of the typhoon by combining a trained TCN network model.
As shown in fig. 2, the TCN network model established in step two of this embodiment calculates the target sequence of each group by using dilation causal convolution, where the formula used in the calculation formula is:
let the input quantity of the established TCN network model be z1、z2、z3And z4The sequence formed is Z ═ (Z)1，z2，z3，z4) Wherein z is1Is radial strength of wind, z2Is the intensity of the weft wind, z3Is the potential height, z4Is the relative humidity. Filter F ═ F1，f2，…，fk) Calculating the target sequence corresponding to each group by using the expansion causal convolution, wherein the calculation formula is as follows:
wherein d is an expansion factor, the value of d is {1,2, …,2n }, and n is the number of hidden layers in the TCN network model; k is the number of filters, f (i) is the ith filter, F (z) is the result of the dilation causal convolution, ziIs the ith typhoon factor.
The output of the TCN network model is
o＝RELU(z+F(z))
z is the input to the dilated causal convolution, and F (z) is the result of the dilated causal convolution.
In the third step of this embodiment, the method for predicting the typhoon running track includes:
and acquiring the running track of each typhoon from historical data of typhoon occurrence.
Acquiring a running path of the current typhoon, and taking the running path as a running track of the current typhoon;
matching the running track of the current typhoon with the running tracks of the typhoons in the historical library to obtain the running track of the typhoon with the highest similarity, and taking the running track of the typhoon as the running track of the current typhoon.
The specific steps for judging the track similarity of the two segments of typhoon motions are as follows:
converting longitude and latitude coordinates of the typhoon running track into a universal transverse-axis mercator projection coordinate system;
calculating the space distance between each point in the two sections of running tracks;
calculating the predicted distance between each point in the two sections of running tracks;
calculating the distance between each section in the two sections of running tracks;
calculating the accumulated distance of the two sections of running tracks;
and normalizing the obtained accumulated distance to be between [0 and 1], wherein 0 represents that the two running tracks are completely dissimilar, and 1 represents that the two running tracks are completely similar, so that the running track with the highest similarity to the current typhoon running track in the historical data is obtained and is taken as the running track of the current typhoon.
The specific steps of calculating the space distance between each point in the two typhoon running tracks are as follows:
setting the running track of one typhoon as a running track L1, setting the running track of the other typhoon as a running track L2, selecting the ith track point in the running track L1 and the jth track point in the running track L2, judging whether the distance between the point segment from the ith track point to the jth track point is equal to the distance between the jth track point and the ith track point, and correcting if the distances are not equal to each other.
The specific steps of calculating the predicted distance between each point in the travel locus L1 and the travel locus L2 are as follows:
an ith track point P in the running track L1 is takeni(xi,yi) J-th track point SP in the travel track L2j(xj,yj) (ii) a Comparing point trajectories PiWith locus point SPjThe time sequence relation of (1) is that the trace point with larger time is A, the trace point with smaller time is B, and the time difference is delta t-tA-tB；
Calculating a predicted position B' of the track point B; supposing that the track point B is a motion point, traversing the time of each track point on the motion track where the track point B is located, and searching for tBAnd when the track point B is positioned between the two track points at + delta t, the predicted position of the track point B is obtained. Suppose at tBWhen point B is between the i-1 th and i-th track points at + Δ t, the spatial coordinates (x) of the predicted position B' for track point BB′,yB′) The calculation formula is as follows:
the motion between any two track points on the track is assumed to be uniform linear motion, so that the motion speed between the two track points can be obtained, and the solving formula is as follows:
if time tB+ Δ t is not present on the corresponding track, then B' is calculated as follows:
wherein N is the total number of points of the corresponding running track of the track point B;
when the predicted distance between the track point A and the track point B is calculated, the calculation formula is as follows:
distt(A,B)＝dist(A,B′)
where dist (A, B ') is the Euclidean distance of A and B' in space coordinates.
The specific steps of calculating the distance between each segment in the running locus L1 and the running locus 2 are as follows:
let SmFor the m-th section of the travel path L1, SSnIs the o-th segment of the travel locus L2; first calculate SmAnd SSnAngle theta, S ofmTwo end points of (A) are Pm(xm,ym) And Pm+1(xm+1,ym+1)，SSoAre SPo(xo,yo) And SPn+1(xo+1,yo+1) The calculation formula of the angle theta is as follows:
θ＝|arctan2(ym+1-ym,xm+1-xm)-arctan2(yo+1-yo,xo+1-xo)|
the spatiotemporal distance between two segments is the sum of the spatiotemporal distances of the two end points of the segment, hence SmAnd SSoInter-segment distance dist ofs(Sm,SSo) The calculation formula of (2) is as follows:
dists(Sm,SSo)＝f(θ)(distst(Pm,SPo)+distst(Pm+1,SPo+1))
solving f (theta) by the calculation formula:
where ω is the negative factor of the shape of the adjustable parameter, the larger ω is the insensitive to the shape factor, and ω is 1, dist in this embodimentsmid(Sm,SSo) Is the midpoint SmAnd SSoSpace-time distance of midpoints, distmax(R, S) is the maximum space-time distance between any two points of the movement locus L1 and the movement locus L2.
The embodiment of the system is as follows:
the embodiment provides a typhoon login strength prediction system based on a TCN network model, which includes a processor and a memory, where the memory stores a computer program for execution on the processor, and when the processor executes the computer program, the prediction method based on the TCN network model typhoon login strength provided in the above method embodiments is implemented.
The embodiments of the present invention disclosed above are intended merely to help clarify the technical solutions of the present invention, and it is not intended to describe all the details of the invention nor to limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A typhoon landing intensity prediction method based on a TCN network model is characterized by comprising the following steps:
(1) acquiring historical data of typhoon occurrence, wherein the historical data comprises typhoon factors when a set distance is reserved between a typhoon center and a landing place and typhoon landing strength;
(2) establishing a TCN network model, and training the established TCN network model by adopting the historical data to obtain a trained TCN network model;
(3) and acquiring a typhoon running path, acquiring a typhoon factor when a set distance exists between a typhoon center and a landing place, and combining the trained TCN network model to obtain the landing strength.
2. The method for predicting typhoon landing intensity based on the TCN network model as claimed in claim 1, wherein the method for acquiring the typhoon running path in the step (3) is as follows: acquiring the running track of each typhoon in historical data; matching the running track of the current typhoon with the running tracks of the typhoons in the historical data to obtain the running track of the typhoon with the highest similarity, and taking the running track of the typhoon as the running track of the current typhoon.
3. The method for predicting typhoon landing intensity based on the TCN network model as claimed in claim 2, wherein the method for judging the track similarity of the two segments of typhoon motions comprises the following steps: converting longitude and latitude coordinates of the typhoon running track into a universal transverse-axis mercator projection coordinate system; calculating the space distance between each point in the two sections of running tracks; calculating the predicted distance between each point in the two sections of running tracks; calculating the distance between each section in the two sections of running tracks; calculating the accumulated distance of the two sections of running tracks; and normalizing the obtained accumulated distance, wherein the greater the accumulated distance is, the higher the similarity of the two running tracks is.
4. The method for predicting typhoon landing intensity based on TCN network model according to claim 1, wherein the convolutional neural network model in step (2) is
The output of the TCN network model is
o＝RELU(z+F(z))
Wherein d is an expansion factor, the value of d is {1,2, …,2n }, and n is the number of hidden layers in the TCN network model; k is the number of filters, f (i) is the ith filter, F (z) is the result of the dilation causal convolution, ziThe ith typhoon factor z is the input to the dilation causal convolution, and F (z) is the result of the dilation causal convolution.
5. The TCN network model-based typhoon landing intensity prediction method according to claim 1, wherein the typhoon factors comprise radial wind intensity, latitudinal wind intensity, potential altitude and relative humidity of typhoon.
6. A system for typhoon-landing intensity prediction based on a TCN network model, comprising a processor and a memory, the memory having stored thereon a computer program for execution on the processor; wherein the processor implements the method for predicting typhoon intensity as follows when executing the calculation program:
(1) acquiring historical data of typhoon occurrence, wherein the historical data comprises typhoon factors when a set distance is reserved between a typhoon center and a landing place and typhoon landing strength;
(2) establishing a TCN network model, and training the established TCN network model by adopting the historical data to obtain a trained TCN network model;
(3) and acquiring a typhoon running path, acquiring a typhoon factor when a set distance exists between a typhoon center and a landing place, and combining the trained TCN network model to obtain the landing strength.
7. The system for predicting typhoon landing intensity based on the TCN network model as claimed in claim 6, wherein the method for obtaining the typhoon running path in the step (3) is as follows: acquiring the running track of each typhoon in historical data; matching the running track of the current typhoon with the running tracks of the typhoons in the historical data to obtain the running track of the typhoon with the highest similarity, and taking the running track of the typhoon as the running track of the current typhoon.
8. The system for predicting typhoon landing intensity based on the TCN network model of claim 7, wherein the method for judging the track similarity of the two segments of typhoon motions comprises the following steps: converting longitude and latitude coordinates of the typhoon running track into a universal transverse-axis mercator projection coordinate system; calculating the space distance between each point in the two sections of running tracks; calculating the predicted distance between each point in the two sections of running tracks; calculating the distance between each section in the two sections of running tracks; calculating the accumulated distance of the two sections of running tracks; and normalizing the obtained accumulated distance, wherein the greater the accumulated distance is, the higher the similarity of the two running tracks is.
9. The TCN-network-model-based typhoon-landing intensity prediction system according to claim 6, wherein the convolutional neural network model in the step (2) is
The output of the TCN network model is
o＝RELU(z+F(z))
Wherein d is an expansion factor, the value of d is {1,2, …,2n }, and n is the number of hidden layers in the TCN network model; k is the number of filters, f (i) is the ith filter, F (z) is the result of the dilation causal convolution, ziThe ith typhoon factor z is the input to the dilation causal convolution, and F (z) is the result of the dilation causal convolution.
10. The TCN-network-model-based typhoon landing intensity prediction system of claim 6, wherein the typhoon factors comprise radial wind intensity, latitudinal wind intensity, potential altitude and relative humidity of typhoons.
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| CN116822140B (en) * | 2023-04-25 | 2024-02-27 | 中国气象局上海台风研究所(上海市气象科学研究所) | Space-time integrated similar typhoon influence inversion method and system combined with weather mechanism |
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