CN112255611A - Intelligent downburst identification method based on radar detection data - Google Patents
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Abstract
The embodiment of the invention discloses an intelligent downburst identification method based on radar detection data in the technical field of atmospheric science, which comprises the following steps: s1, establishing radar detection data D (t) changing along with radar detection time t, and defining radar detection data of 1-n time periods before time t from D (t-1), D (t-2) to D (t-n) respectively; s2, establishing a radar echo image Pa (D (t)) and a radar radial velocity image Pb (D (t)) which are changed along with the radar detection data D (t). The invention avoids falling into local extreme value and seeks optimal parameter by obtaining effective information as much as possible from limited data set; the multiple models can be used for model integration, and accuracy and stability of model identification are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of atmospheric science, in particular to a downburst intelligent identification method based on radar detection data.
Background
Downburst forms in strong convection storms, which form strong divergent straight strong winds when they reach the ground. When the wind power generation device is used, local wind speed sharp increase and strong blast cut are caused, so that the taking off and landing of an aviation airplane and the navigation of a steamship are possibly influenced, the crash of the aviation airplane is seriously caused, the steamship is overturned, serious property loss is caused, and even the life is threatened. On radar intensity echoes, bow-shaped echoes are a significant indicator of downburst. If there is a strong rear inflow rush at the center of the bow echo, a strong downburst wind tends to occur. On a radar radial velocity diagram, downburst appears as radial velocity divergence, namely, echoes in a shape of a 'bulls eye' are presented, and the range and the intensity of the downburst can be roughly judged through the distance between extreme values of a pair of radial velocity centers.
The existing identification of downburst mainly depends on subjective analysis of the characteristics by weather service personnel to make identification and early warning measures. Accurate identification is difficult due to the small time and spatial dimensions of downburst.
Based on the method, the intelligent downburst identification method based on the radar detection data is designed to solve the problems.
Disclosure of Invention
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data, which aims to solve the technical problems in the background art.
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data. In one possible embodiment, the method comprises the following steps:
s1, establishing radar detection data D (t) changing along with radar detection time t, and defining radar detection data of 1-n time periods before time t from D (t-1), D (t-2) to D (t-n) respectively;
s2, establishing a radar echo image Pa (D (t)) and a radar radial velocity image Pb (D (t)) which change along with radar detection data D (t), and recording a radar echo time sequence image formed by Pa (D (t)), Pa (D (t-1)), Pa (D (t-2)) and Pa (D (t-n)) as PA (D, t); recording one radar radial velocity time sequence image consisting of Pb (D (t)), Pb (D (t-1)), Pb (D (t-2)) and Pb (D (t-n)) as PB (D, t);
s3, establishing a data pair between a radar echo time sequence image PA (D, t), a radar radial velocity time sequence image PB (D, t) and a ground meteorological station observation wind speed information label Lab (t) of whether the downburst occurs or not, and recording the occurrence of the downburst as T (t) 01 and the non-downburst as T (t) 10;
s4, converting the wind speed information observed by the ground meteorological station into observation time consistent with radar detection time;
s5, collecting radar detection data and ground meteorological data in a specified time period to perform processing calculation, and acquiring all data pairs in the time period to further obtain a use case data set DS which can only be identified for downburst;
s6, adjusting the probability that the downburst flow does not exist in the model training result;
s7, constructing a downburst flow identification network model DBIM based on a deep neural network, taking a radar echo time sequence image and a radial velocity field time sequence image as input, and fusing a radar echo time-space sequence;
s8, finding out the optimal network hyper-parameter at the close time, and carrying out grouping inspection optimization on the DBIM network model;
and S9, introducing a class weight to the loss function of the DBIM, and giving a penalty term under the condition that the downburst actually exists but the model is identified as not existing to improve the sensitivity to T (t) 01 in training, wherein the improved loss function is as follows:
where yi is an indicative function of downburst weather, ti is an output of the CNN model corresponding to downburst weather, and represents a probability that the region is identified as downburst weather;
s10, substituting the data set DS into the DBIM model to train the neural network, and obtaining a model for intelligently identifying downburst;
s11, using the real-time radar detection data as the input of the model according to the model for intelligently identifying the downburst, and after model calculation, obtaining the result DBIM _ Output (D (t0)) -01 of the model Output of classification identification, which indicates that the downburst is identified, or the result DBIM _ Output (D (t0)) -10, which indicates that the downburst is not identified.
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data. In a possible scheme, the requirements that the downburst flow t (t) ═ 01 in S3 simultaneously satisfy the conditions that:
a. the instantaneous wind speed reached or exceeded WS 1;
b. wind speed changes over WS2 in the past IM1 minutes;
c. in the last 1 hour of the wind speed observed by the meteorological station, the phenomenon that the storm core or the strong echo center is rapidly descended is identified from radar detection data of each time, and the descent speed reaches or exceeds WS 3.
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data. In one possible approach, the algorithm for conversion in S4 is an interpolation algorithm.
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data. In a possible solution, the building of the downburst identification network model DBIM in S7 includes the following steps:
a. decomposing the data set into a radar echo time sequence image and a radial velocity field time sequence image, wherein each group of images comprises 8 elevation planes and 6 adjacent moments, and the size of each image is 100 multiplied by 100 pixels;
b. processing a radar echo image, designing a 4-channel sparse structure to generate dense data, wherein each channel comprises 1-2 convolution layers, the sizes of convolution kernels are preferably 1 × 1, 3 × 3 and 5 × 5, and the sizes of input images and output images are kept consistent by extracting echo intensity space information under different time scales and properly filling the 4 channels;
c. and connecting the output of each channel in the channel dimension to obtain a four-dimensional vector, and outputting the four-dimensional vector to a subsequent layer.
The embodiment of the invention provides an intelligent downburst identification method based on radar detection data. In a possible solution, the optimization of the DBIM network model in S8 includes the following steps:
a. randomly splitting an original data set DS into K parts;
b. selecting any one as a verification set, taking the rest as training sets, training the DBIM model to obtain a model with network parameters, testing on the verification set, and storing an evaluation index E of the model;
c. repeating the step b for K times to ensure that all subsets have one chance as a verification set;
d. and taking the mean value of each group of evaluation indexes as the accurate evaluation of the model and as the comprehensive evaluation index of the DBIM model under the current grouping verification.
Based on the scheme, the intelligent downburst identification model based on the convolutional neural network has the advantages that weather radar data is used as input, and a function mapping relation between a radar echo image and a radial velocity field image and the condition whether downburst occurs or not is searched through self-learning of the deep neural network. By applying a series of optimization techniques, the problem that the recognition result is biased to the probable event in the sample due to too few cases of downburst in the data set is solved. Meanwhile, the packet inspection is adopted, and the advantages are that: on one hand, effective information as much as possible can be obtained from a limited data set, local extreme values are avoided, and optimal parameters are sought; on the other hand, a plurality of models can be used for model integration, and accuracy and stability of model identification are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of an identification method of the present invention;
FIGS. 2-3 are diagrams of neural network structures for intelligent identification of downburst in accordance with the present invention;
FIG. 4 is a table diagram of a partial neural network structure parameter for intelligent identification of downburst according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," "circumferential," and the like are used in the indicated orientations and positional relationships based on the drawings for convenience in describing and simplifying the description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
In the present invention, unless otherwise specifically stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally formed; the connection can be mechanical connection, electrical connection or communication connection; either directly or indirectly through intervening media, either internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1-4 illustrate an intelligent downburst identification method based on radar detection data according to the present invention; the method comprises the following steps:
s1, establishing radar detection data D (t) changing along with radar detection time t, and defining radar detection data of 1-n time periods before time t from D (t-1), D (t-2) to D (t-n) respectively;
s2, establishing a radar echo image Pa (D (t)) and a radar radial velocity image Pb (D (t)) which change along with radar detection data D (t), and recording a radar echo time sequence image formed by Pa (D (t)), Pa (D (t-1)), Pa (D (t-2)) and Pa (D (t-n)) as PA (D, t); recording one radar radial velocity time sequence image consisting of Pb (D (t)), Pb (D (t-1)), Pb (D (t-2)) and Pb (D (t-n)) as PB (D, t);
s3, establishing a data pair between a radar echo time sequence image PA (D, t), a radar radial velocity time sequence image PB (D, t) and a ground meteorological station observation wind speed information label Lab (t) of whether the downburst occurs or not, and recording the occurrence of the downburst as T (t) 01 and the non-downburst as T (t) 10;
s4, converting the wind speed information observed by the ground meteorological station into observation time consistent with radar detection time;
s5, collecting radar detection data and ground meteorological data in a specified time period to perform processing calculation, and acquiring all data pairs in the time period to further obtain a use case data set DS which can only be identified for downburst;
s6, adjusting the probability that the downburst flow does not exist in the model training result;
s7, constructing a downburst flow identification network model DBIM based on a deep neural network, taking a radar echo time sequence image and a radial velocity field time sequence image as input, and fusing a radar echo time-space sequence;
s8, finding out the optimal network hyper-parameter at the close time, and carrying out grouping inspection optimization on the DBIM network model;
and S9, introducing a class weight to the loss function of the DBIM, and giving a penalty term under the condition that the downburst actually exists but the model is identified as not existing to improve the sensitivity to T (t) 01 in training, wherein the improved loss function is as follows:
where yi is an indicative function of downburst weather, ti is an output of the CNN model corresponding to downburst weather, and represents a probability that the region is identified as downburst weather;
s10, substituting the data set DS into the DBIM model to train the neural network, and obtaining a model for intelligently identifying downburst;
s11, using the real-time radar detection data as the input of the model according to the model for intelligently identifying the downburst, and after model calculation, obtaining the result DBIM _ Output (D (t0)) -01 of the model Output of classification identification, which indicates that the downburst is identified, or the result DBIM _ Output (D (t0)) -10, which indicates that the downburst is not identified.
Through the above, it can be easily found that, in the process of identifying the downburst by using the intelligent downburst identification method based on radar detection data, one piece of radar detection data is defined as d (t), wherein t represents the radar detection time. And D (t-1), D (t-2) and D (t-n) are respectively defined to represent radar detection data of 1 time period, 2 time periods and n time periods before the t moment, wherein n is a positive integer of [1,100], and a radar echo image at the t moment is defined as Pa (D (t)), and Pa (D (t)), Pa (D (t-1)), Pa (D (t-2)), … and Pa (D (t-n)) form a radar echo time sequence image and are marked as PA (D, t). The radar radial velocity image at time t is Pb (D (t)), and one radar radial velocity time-series image is constituted by Pb (D (t)), Pb (D (t-1)), Pb (D (t-2)), …, and Pb (D (t-n)), and is denoted as Pb (D, t). The PA (D, t) and the PB (D, t) are used for recording some characteristics of particles such as water vapor in the atmosphere in time and space, and both the radar echo image and the radar radial velocity image can be obtained by a meteorology-disclosed algorithm or professional meteorology software; and establishing a data pair between the radar echo time sequence image PA (D, t) and the radar radial velocity time sequence image PB (D, t) and the label Lab (t) for judging whether the downburst occurs or not. Wherein Lab (t) is the wind speed information observed by the ground meteorological station; because the time period of radar detection is different from the time period observed by the ground meteorological station, the wind speed information observed by the ground meteorological station is converted into the observation time consistent with the radar detection time by adopting an interpolation algorithm; processing and calculating radar detection data and ground meteorological station data for a long period of time (such as the last year and the whole year) to generate all 'data pairs' in the period of time, so as to obtain a use case data set for intelligent identification of downburst, and recording the use case data set as DS; since the occurrence of downburst belongs to a small probability event, the number of use cases in DS where t (t) is 01 is much smaller than the number of use cases where t (t) is 10. In order to prevent the following calculation step from biasing the result of model training to the general event, that is, no downburst occurs, and thus affecting the accuracy of recognition, the processing method of the step is as follows: generating a batch of new use cases by small-amplitude translation, rotation, deformation, noise increase and other modes for radar echo images and radial velocity images with possible downburst characteristics, and adding the new use cases to a data set DS, so that the proportion of the downburst identified in the DS is increased; constructing a downburst flow identification network model which is based on a deep neural network, takes a radar echo time sequence image and a radial velocity field time sequence image as input, integrates various characteristics of a radar echo time-space sequence, and is marked as DBIM, wherein the network structure of the model is shown in figure 2; since the occurrence of downburst has a certain seasonal characteristic, data sets based on a large number of historical meteorological data are organized chronologically. In order to improve the generalization performance of the model, a better network hyper-parameter is found in a similar training time, and an optimized DBIM network model is obtained; to help the DBIM to have sensitivity to T (t) ═ 01 in the training process, the step introduces class weight in the loss function of the DBIM, and gives a larger penalty term to the situation that the downburst actually exists but the model identifies that the downburst does not exist, and w is a judgment weight term, namely the penalty term. The larger the value is, the more radar images can be judged by the model to be downburst, so that the higher false alarm rate is caused, and the recognition success rate is correspondingly improved; substituting the data set DS into a DBIM model to train a neural network, and finally obtaining a model for intelligently identifying downburst flows; then, the real-time radar detection data is used as the input of the model, and after model calculation, the result DBIM _ Output (D (t0)) -01 or DBIM _ Output (D (t0)) -10 of classification recognition can be obtained. Where DBIM _ Output represents the result of the model Output and D (t0) represents the real-time radar detection data. Output result 01 indicates that the occurrence of downburst was recognized, and output result 10 indicates that the occurrence of downburst was not recognized.
The invention provides an intelligent downburst identification model based on a convolutional neural network. The model takes weather radar data as input, and seeks a functional mapping relation between a radar echo image and a radial velocity field image and the condition whether downburst occurs or not through self-learning of a deep neural network. By applying a series of optimization techniques, the problem that the recognition result is biased to the probable event in the sample due to too few cases of downburst in the data set is solved. Meanwhile, the packet inspection is adopted, and the advantages are that: on one hand, effective information as much as possible can be obtained from a limited data set, local extreme values are avoided, and optimal parameters are sought; on the other hand, a plurality of models can be used for model integration, and accuracy and stability of model identification are improved.
Optionally, the condition that the downburst flow t (t) ═ 01 in S3 needs to be satisfied at the same time includes:
a. the instantaneous wind speed reached or exceeded WS 1;
b. wind speed changes over WS2 in the past IM1 minutes;
c. in the last 1 hour of the wind speed observed by the meteorological station, the phenomenon that the storm core or the strong echo center is rapidly descended is identified from radar detection data of each time, and the descent speed reaches or exceeds WS 3. It should be noted that in this embodiment, WS1 ∈ [17.2,100], has a unit of meter/second; WS 2E [11.7,100] with the unit of meter/second; IM1 is within the scope of [5,120] in minutes; WS3 ∈ [20,200], in units of kilometers per hour; when the 3 conditions are met, marking that downburst flow happens at the moment, and recording as T (t) 01; otherwise, the flow is marked as no downburst and is marked as t (t) 10.
Further, the algorithm converted in S4 is an interpolation algorithm.
More specifically, the building of the downburst flow identification network model DBIM in S7 includes the following steps:
a. decomposing the data set into a radar echo time sequence image and a radial velocity field time sequence image, wherein each group of images comprises 8 elevation planes and 6 adjacent moments, and the size of each image is 100 multiplied by 100 pixels;
b. processing a radar echo image, designing a 4-channel sparse structure to generate dense data, wherein each channel comprises 1-2 convolution layers, the sizes of convolution kernels are preferably 1 × 1, 3 × 3 and 5 × 5, and the sizes of input images and output images are kept consistent by extracting echo intensity space information under different time scales and properly filling the 4 channels;
c. connecting the output of each channel in channel dimension to obtain a four-dimensional vector, and outputting the four-dimensional vector to a subsequent layer; the structure of these layers is shown in fig. 4, Conv denotes convolution layers, and the parenthesized numerals denote the window size or the number of outputs of convolution kernels. The input layer is a four-dimensional vector, the functions of other layers are consistent with the meanings of English names, wherein, a Drapout discarding layer, Max Pooling is a maximum Pooling layer, Full Connection is a Full-Connection layer, and a Flatten layer is used for converting the multi-dimensional vector of the previous layer into a one-dimensional vector, and the layers are also some main basic units in the neural network; and processing the time sequence image of the radial velocity field by using the same network model until the time sequence image of the radial velocity field and the time sequence image of the radial velocity field are respectively subjected to final convolution Conv (3 multiplied by 64) and Dropout, then entering a Flatten layer and a full connection layer, and finally outputting the data in a 2-classification one-hot coding data form.
Further, the optimizing the DBIM network model in S8 includes the following steps:
a. randomly splitting an original data set DS into K parts;
b. selecting any one as a verification set, taking the rest as training sets, training the DBIM model to obtain a model with network parameters, testing on the verification set, and storing an evaluation index E of the model;
c. repeating the step b for K times to ensure that all subsets have one chance as a verification set;
d. taking the mean value of each group of evaluation indexes as the accurate evaluation of the model and as the comprehensive evaluation index of the DBIM model under the current grouping verification; and comprehensive evaluation index of DBIM model
In the present invention, unless otherwise explicitly specified or limited, the first feature "on" or "under" the second feature may be directly contacting the first feature and the second feature or indirectly contacting the first feature and the second feature through an intermediate.
Also, a first feature "on," "above," and "over" a second feature may mean that the first feature is directly above or obliquely above the second feature, or that only the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lower level than the second feature.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example" or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. An intelligent downburst identification method based on radar detection data is characterized by comprising the following steps:
s1, establishing radar detection data D (t) changing along with radar detection time t, and defining radar detection data of 1-n time periods before time t from D (t-1), D (t-2) to D (t-n) respectively;
s2, establishing a radar echo image Pa (D (t)) and a radar radial velocity image Pb (D (t)) which change along with radar detection data D (t), and recording a radar echo time sequence image formed by Pa (D (t)), Pa (D (t-1)), Pa (D (t-2)) and Pa (D (t-n)) as PA (D, t); recording one radar radial velocity time sequence image consisting of Pb (D (t)), Pb (D (t-1)), Pb (D (t-2)) and Pb (D (t-n)) as PB (D, t);
s3, establishing a data pair between a radar echo time sequence image PA (D, t), a radar radial velocity time sequence image PB (D, t) and a ground meteorological station observation wind speed information label Lab (t) of whether the downburst occurs or not, and recording the occurrence of the downburst as T (t) 01 and the non-downburst as T (t) 10;
s4, converting the wind speed information observed by the ground meteorological station into observation time consistent with radar detection time;
s5, collecting radar detection data and ground meteorological data in a specified time period to perform processing calculation, and acquiring all data pairs in the time period to further obtain a use case data set DS which can only be identified for downburst;
s6, adjusting the probability that the downburst flow does not exist in the model training result;
s7, constructing a downburst flow identification network model DBIM based on a deep neural network, taking a radar echo time sequence image and a radial velocity field time sequence image as input, and fusing a radar echo time-space sequence;
s8, finding out the optimal network hyper-parameter at the close time, and carrying out grouping inspection optimization on the DBIM network model;
and S9, introducing a class weight to the loss function of the DBIM, and giving a penalty term under the condition that the downburst actually exists but the model is identified as not existing to improve the sensitivity to T (t) 01 in training, wherein the improved loss function is as follows:
where yi is an indicative function of downburst weather, ti is an output of the CNN model corresponding to downburst weather, and represents a probability that the region is identified as downburst weather;
s10, substituting the data set DS into the DBIM model to train the neural network, and obtaining a model for intelligently identifying downburst;
s11, using the real-time radar detection data as the input of the model according to the model for intelligently identifying the downburst, and after model calculation, obtaining the result DBIM _ Output (D (t0)) -01 of the model Output of classification identification, which indicates that the downburst is identified, or the result DBIM _ Output (D (t0)) -10, which indicates that the downburst is not identified.
2. The method for intelligently identifying downburst according to claim 1, wherein the condition that downburst t (t) ═ 01 in S3 is simultaneously satisfied includes:
a. the instantaneous wind speed reached or exceeded WS 1;
b. wind speed changes over WS2 in the past IM1 minutes;
c. in the last 1 hour of the wind speed observed by the meteorological station, the phenomenon that the storm core or the strong echo center is rapidly descended is identified from radar detection data of each time, and the descent speed reaches or exceeds WS 3.
3. The intelligent thunderstorm flow identification method based on radar detection data as claimed in claim 1, wherein the algorithm converted in S4 is an interpolation algorithm.
4. The intelligent thunderstorm flow identification method based on radar detection data according to claim 1, wherein the building of the thunderstorm flow identification network model DBIM in S7 includes the following steps:
a. decomposing the data set into a radar echo time sequence image and a radial velocity field time sequence image, wherein each group of images comprises 8 elevation planes and 6 adjacent moments, and the size of each image is 100 multiplied by 100 pixels;
b. processing a radar echo image, designing a 4-channel sparse structure to generate dense data, wherein each channel comprises 1-2 convolution layers, the sizes of convolution kernels are preferably 1 × 1, 3 × 3 and 5 × 5, and the sizes of input images and output images are kept consistent by extracting echo intensity space information under different time scales and properly filling the 4 channels;
c. and connecting the output of each channel in the channel dimension to obtain a four-dimensional vector, and outputting the four-dimensional vector to a subsequent layer.
5. The intelligent thunderstorm flow identification method based on radar detection data as claimed in claim 1, wherein the optimization of the DBIM network model in S8 includes the following steps:
a. randomly splitting an original data set DS into K parts;
b. selecting any one as a verification set, taking the rest as training sets, training the DBIM model to obtain a model with network parameters, testing on the verification set, and storing an evaluation index E of the model;
c. repeating the step b for K times to ensure that all subsets have one chance as a verification set;
d. and taking the mean value of each group of evaluation indexes as the accurate evaluation of the model and as the comprehensive evaluation index of the DBIM model under the current grouping verification.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112946655A (en) * | 2021-01-27 | 2021-06-11 | 江苏省气象科学研究所 | Downburst flow identification method and device based on near-surface divergence field and storage medium |
| CN115169431A (en) * | 2022-05-16 | 2022-10-11 | 湖南师范大学 | Thunderstorm identification method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5796611A (en) * | 1994-10-04 | 1998-08-18 | Nippon Telegraph And Telephone Corporation | Weather forecast apparatus and method based on recognition of echo patterns of radar images |
| CN108254750A (en) * | 2017-11-24 | 2018-07-06 | 南京信息工程大学 | A kind of downburst intelligent recognition method for early warning based on Radar Data |
| CN109765559A (en) * | 2019-02-02 | 2019-05-17 | 中国水利水电科学研究院 | A kind of mountain area disastrous rainstorm recognition methods based on Doppler radar and deep learning |
| CN111796343A (en) * | 2020-06-15 | 2020-10-20 | 兰州中心气象台(兰州干旱生态环境监测预测中心) | Strong convection weather short-time approaching recognition method based on artificial intelligence algorithm |
-
2020
- 2020-10-28 CN CN202011171264.5A patent/CN112255611B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5796611A (en) * | 1994-10-04 | 1998-08-18 | Nippon Telegraph And Telephone Corporation | Weather forecast apparatus and method based on recognition of echo patterns of radar images |
| CN108254750A (en) * | 2017-11-24 | 2018-07-06 | 南京信息工程大学 | A kind of downburst intelligent recognition method for early warning based on Radar Data |
| CN109765559A (en) * | 2019-02-02 | 2019-05-17 | 中国水利水电科学研究院 | A kind of mountain area disastrous rainstorm recognition methods based on Doppler radar and deep learning |
| CN111796343A (en) * | 2020-06-15 | 2020-10-20 | 兰州中心气象台(兰州干旱生态环境监测预测中心) | Strong convection weather short-time approaching recognition method based on artificial intelligence algorithm |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112946655A (en) * | 2021-01-27 | 2021-06-11 | 江苏省气象科学研究所 | Downburst flow identification method and device based on near-surface divergence field and storage medium |
| CN112946655B (en) * | 2021-01-27 | 2022-05-13 | 江苏省气象科学研究所 | Downburst flow identification method and device based on near-surface divergence field and storage medium |
| CN115169431A (en) * | 2022-05-16 | 2022-10-11 | 湖南师范大学 | Thunderstorm identification method and system |
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