CN109472396A - Mountain fire prediction technique based on depth e-learning - Google Patents
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Abstract
The invention discloses a kind of mountain fire prediction techniques based on depth e-learning that can be improved forecasting efficiency and forecasting accuracy.The mountain fire prediction technique based on depth e-learning learns mass historical data by the Automatic Feature Extraction network on building time and Spatial Dimension, reaches the analysis of mountain fire prediction and warning.Bottleneck of traditional mode of learning in mass data is overcome, GPU and distribution can be efficiently used quickly to be trained；The mode of traditional artificial feature has been abandoned, has realized feature automation；Depth network has been used flexibly to carry out effective integration to different dimensions feature, to improve the forecasting efficiency and forecasting accuracy of mountain fire risk profile.It is suitble to promote and apply in technical field of data processing.
Description
Technical field
The present invention relates to technical field of data processing, especially a kind of mountain fire prediction technique based on depth e-learning.
Background technique
In natural calamity, mountain fire is a kind of very special and very damaging disaster；Ecology can not only be caused
Significant impact, also can be to the people of spot, animal and some " assets " cause heavy losses；Such as the tower in power equipment, line
Cable, substation；Signal tower in communication etc..And the genesis mechanism of mountain fire is extremely complex, just can under many factors interaction
Mountain fire occurs.
With the development of the technologies such as Internet of Things, digitlization, Internet of Things, make it possible to get largely by reasonable manner
Data can carry out prediction, pre- to natural calamity by way of non-experiment in conjunction with the promotion of present computing capability
It is alert etc..Secondly, present data are not only big data, and data diversification, data are many and diverse.Similar natural calamity is asked
Topic, not only contains geographical space temperature, further comprises time dimension, the two dimensions it is effective combine modeling be also it is present very
The difficult point of more traditional mathematics models.Therefore, the present invention mainly utilizes current hot spot technology " deep learning " to solve time and sky
Between the problem of modeling, and the characteristics of combine mountain fire, applied in mountain fire prediction and warning.
Traditional characteristic extracting mode is to extract respectively in room and time dimension, then carry out subsequent analysis and build
Mould, there are certain subjectivities for the mode of this feature extraction, and are not easily found effective feature to support subsequent mould
It is poor to will lead to modelling effect for type.
Traditional machine learning model is the mode based on manual features, and feature spatially cannot be carried out to effectively benefit
With being modeled on time dimension, traditional time series cannot establish long-term time dependence, and time series is mainly still
Based on statistics.The machine learning algorithm of this quasi-tradition designs upper no space or the concept of time, is all based on the study of feature
Process.
On Fusion Features, each feature can be typically added on each sample, even if there is feature of overall importance, this
Kind mode is inflexible, when different types of feature or inconsistent feature are also possible that problem when merging.
Traditional mode of learning is on the basis of certain data volume, and data volume is too small or big city excessively goes wrong.Work as number
It is too small according to measuring, it is very low to the confidence level of result.But traditional mode of learning is again extremely difficult to the processing of mass data, has certain
Bottleneck.
Summary of the invention
Technical problem to be solved by the invention is to provide it is a kind of can be improved forecasting efficiency and forecasting accuracy based on
The mountain fire prediction technique of depth e-learning.
The technical solution adopted by the present invention to solve the technical problems are as follows: should the mountain fire prediction side based on depth e-learning
Method, comprising the following steps:
1), mountain fire risk forecast model is established using depth online learning methods；
The mountain fire risk forecast model includes convolutional layer, fused layer, LSTM layers, full articulamentum；
2), for the needs of mountain fire risk profile task, acquisition is since current time forward in the phase of history time
Remotely-sensed data and meteorological data；
3), handle to obtain the gas as unit of day by temporal resolution fusion method to the meteorological data of 2) step acquisition
Image data；
4), the meteorological data as unit of day for obtaining remotely-sensed data and 3) step passes through spatial data fusion method
Realize the fusion matching of spatial data；
5), by space is all converted to by remotely-sensed data that 4) step process obtains, meteorological data, rate is 500m* respectively
500m；Temporal resolution is 1 day, and format is the data of tiff format；
6) data that step 5) obtains, are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, filling are carried out to data again
Mode be to be filled by adjacent point: it is specific as follows shown in:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude in each point
Position；
C, meteorological data is corresponded to according to longitude and latitude position, is generated identical as remotely-sensed data matrix size in b step
Meteorological data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows:
[time step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix is generated by history fire point data, is generated in the size and step d of target sample matrix
Large-scale matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and will be entire
Data are divided into different size of data set, can form batch data；
7), by the convolutional layer of batch data input mountain fire risk forecast model, and the same day is extracted from batch data
Meteorological data inputs the full articulamentum of mountain fire risk forecast model, and final mountain fire Occurrence forecast probability can be obtained.
Further, the temporal resolution fusion method is described in detail below in 3) step:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out polymerization generation accordingly
Statistical indicator, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem is used for
Temperature, humidity, as daily amount total_mdFor rainfall level.
Further, the spatial data fusion method is used close to matching method or the interpolation side krige in 4) step
Method.
Further, in 1) step, using depth online learning methods establish mountain fire risk forecast model include with
Lower step:
A, for the needs of mountain fire risk profile task, acquisition is distant in the phase of history time forward since current time
Feel data and meteorological data；
B, handle to obtain the meteorology as unit of day by temporal resolution fusion method to the meteorological data of step A acquisition
Data；
C, the meteorological data as unit of day for obtaining remotely-sensed data and step B is real by spatial data fusion method
The fusion matching of existing spatial data；
D, the remotely-sensed data, the meteorological data that handle by step C are all converted to space rate is 500m* respectively
500m；Temporal resolution is 1 day, and format is the data of tiff format；
E, the data that step D is obtained are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, filling are carried out to data again
Mode be to be filled by adjacent point: it is specific as follows shown in:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude in each point
Position；
C, meteorological data is corresponded to according to longitude and latitude position, is generated identical as remotely-sensed data matrix size in b step
Meteorological data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows:
[time step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix is generated by history fire point data, is generated in the size and step d of target sample matrix
Large-scale matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and will be entire
Data are divided into different size of data set, can form batch data；
F, based on batch data construct depth network model, the depth network model include convolutional layer, fused layer,
LSTM layers, full articulamentum；
The convolutional layer is stacked by multiple convolution units, and each convolution unit obtains with the following method: first
Batch data are subjected to convolutional calculation as input matrix and obtain convolution results, the convolutional calculation formula are as follows: C=A*B,
Middle * indicates that convolution algorithm, A indicate input matrix and a B convolution nuclear matrix, and the convolution algorithm uses following discrete calculation
Formula:Wherein m, n are the sizes values of convolution kernel.wm,nFor m in convolution kernel, the position n
Value, b be bigoted item；Then obtained convolution results are subjected to maximum pond and obtain convolution unit
The fused layer merges obtained convolution unit using ADD method, specifically, the volume that convolutional layer is obtained
Product unit carries out flatten expansion, and new feature is added, and the shape of the new feature is consistent with the matrix size of input,
Stack is carried out according still further to time dimension, obtains fusion matrix [batch_size, time_steps, depth], wherein depth=
w*h；
Described LSTM layers includes LSTMCell layers, dropper layers and full-connect layers, will merge matrix [batch_
Size, time_steps, depth] it inputs after LSTMCell layers and successively passes through at dropper layers and full-connect layers again
It manages and is exported to obtain LSTM data using result function, the result function is using dimension tanh function output, the dimension
Tanh function is as described below:
The full articulamentum merges the same day meteorological data for obtaining LSTM data and extracting from batch data
Processing；
G, depth network model is trained using more epoch, and carries out the instruction of depth network model using more GPU
Practice, CPU is responsible for the update of parameter, and GPU carries out the training of network, obtains final mountain fire prediction model.
Further, the temporal resolution fusion method is described in detail below in step B:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out polymerization generation accordingly
Statistical indicator, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem is used for
Temperature, humidity, as daily amount total_mdFor rainfall level.
Further, the spatial data fusion method is used close to matching method or the interpolation side krige in step C
Method.
Beneficial effects of the present invention: building time and space dimension should be passed through based on the mountain fire prediction technique of depth e-learning
Automatic Feature Extraction network on degree, and mass historical data is learnt, reach the analysis of mountain fire prediction and warning.Overcome biography
Bottleneck of the system mode of learning in mass data, can efficiently use GPU and distribution quickly to be trained；Traditional people is abandoned
The mode of work feature realizes feature automation；Depth network has been used flexibly to carry out effective integration to different dimensions feature,
To improve the forecasting efficiency and forecasting accuracy of mountain fire risk profile.
Specific embodiment
The mountain fire prediction technique based on depth e-learning, comprising the following steps:
1), mountain fire risk forecast model is established using depth online learning methods；
The mountain fire risk forecast model includes convolutional layer, fused layer, LSTM layers, full articulamentum；
2), for the needs of mountain fire risk profile task, acquisition is since current time forward in the phase of history time
Remotely-sensed data and meteorological data；The remotely-sensed data includes: Fuel loads FMC, combustible load FL, fuel type
FT；Elevation, the gradient, slope aspect；The meteorological data includes temperature, humidity, rainfall, wind speed, wind direction；
3), handle to obtain the gas as unit of day by temporal resolution fusion method to the meteorological data of 2) step acquisition
Image data；The temporal resolution fusion method is described in detail below:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out polymerization generation accordingly
Statistical indicator, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem is used for
Temperature, humidity, as daily amount total_mdFor rainfall level；
4), the meteorological data as unit of day for obtaining remotely-sensed data and 3) step passes through spatial data fusion method
Realize the fusion matching of spatial data；The spatial data fusion method is used close to matching method or krige interpolation method；
5), by space is all converted to by remotely-sensed data that 4) step process obtains, meteorological data, rate is 500m* respectively
500m；Temporal resolution is 1 day, and format is the data of tiff format；
6) data that step 5) obtains, are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, filling are carried out to data again
Mode be to be filled by adjacent point: it is specific as follows shown in:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude in each point
Position；
C, meteorological data is corresponded to according to longitude and latitude position, is generated identical as remotely-sensed data matrix size in b step
Meteorological data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows:
[time step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix is generated by history fire point data, is generated in the size and step d of target sample matrix
Large-scale matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and will be entire
Data are divided into different size of data set, can form batch data；During batch data assembling, positive example data are less,
In order to solve proportional imbalance, all positive example data be assembled into each batch in each use；
7), by the convolutional layer of batch data input mountain fire risk forecast model, and the same day is extracted from batch data
Meteorological data inputs the full articulamentum of mountain fire risk forecast model, and final mountain fire Occurrence forecast probability can be obtained.
This is mentioned based on the mountain fire prediction technique of depth e-learning by the automated characterization on building time and Spatial Dimension
Network is taken, and mass historical data is learnt, reaches the analysis of mountain fire prediction and warning.Traditional mode of learning is overcome in magnanimity
Bottleneck in data can efficiently use GPU and distribution quickly to be trained；The mode of traditional artificial feature has been abandoned, it is real
Existing feature automation；Depth network has been used flexibly to carry out effective integration to different dimensions feature, to improve mountain fire
The forecasting efficiency and forecasting accuracy of risk profile.
In the above-described embodiment, in 1) step, mountain fire risk forecast model is established using depth online learning methods
The following steps are included:
A, for the needs of mountain fire risk profile task, acquisition is distant in the phase of history time forward since current time
Feel data and meteorological data；The remotely-sensed data includes: Fuel loads FMC, combustible load FL, fuel type FT；
Elevation, the gradient, slope aspect；The meteorological data includes temperature, humidity, rainfall, wind speed, wind direction；
B, handle to obtain the meteorology as unit of day by temporal resolution fusion method to the meteorological data of step A acquisition
Data；The temporal resolution fusion method is described in detail below:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out polymerization generation accordingly
Statistical indicator, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem is used for
Temperature, humidity, as daily amount total_mdFor rainfall level；
C, the meteorological data as unit of day for obtaining remotely-sensed data and step B is real by spatial data fusion method
The fusion matching of existing spatial data；The spatial data fusion method is used close to matching method or krige interpolation method；
D, the remotely-sensed data, the meteorological data that handle by step C are all converted to space rate is 500m* respectively
500m；Temporal resolution is 1 day, and format is the data of tiff format；
E, the data that step D is obtained are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, filling are carried out to data again
Mode be to be filled by adjacent point: it is specific as follows shown in:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude in each point
Position；
C, meteorological data is corresponded to according to longitude and latitude position, is generated identical as remotely-sensed data matrix size in b step
Meteorological data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows:
[time step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix is generated by history fire point data, is generated in the size and step d of target sample matrix
Large-scale matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and will be entire
Data are divided into different size of data set, can form batch data；During batch data assembling, positive example data are less,
In order to solve proportional imbalance, all positive example data be assembled into each batch in each use；
F, based on batch data construct depth network model, the depth network model include convolutional layer, fused layer,
LSTM layers, full articulamentum；
The convolutional layer is stacked by multiple convolution units, and each convolution unit obtains with the following method: first
Batch data are subjected to convolutional calculation as input matrix and obtain convolution results, the convolutional calculation formula are as follows: C=A*B,
Middle * indicates that convolution algorithm, A indicate input matrix and a B convolution nuclear matrix, and the convolution algorithm uses following discrete calculation
Formula:Wherein m, n are the sizes values of convolution kernel.wm,nFor m in convolution kernel, the position n
Value, b be bigoted item；Then obtained convolution results are subjected to maximum pond and obtain convolution unit
The fused layer merges obtained convolution unit using ADD method, specifically, the volume that convolutional layer is obtained
Product unit carries out flatten expansion, and new feature is added, and the shape of the new feature is consistent with the matrix size of input,
Stack is carried out according still further to time dimension, obtains fusion matrix [batch_size, time_steps, depth], wherein depth=
w*h；
Described LSTM layers includes LSTMCell layers, dropper layers and full-connect layers, will merge matrix [batch_
Size, time_steps, depth] it inputs after LSTMCell layers and successively passes through at dropper layers and full-connect layers again
It manages and is exported to obtain LSTM data using result function, the result function is using dimension tanh function output, the dimension
Tanh function is as described below:
The full articulamentum merges the same day meteorological data for obtaining LSTM data and extracting from batch data
Processing；
G, depth network model is trained using more epoch, and carries out the instruction of depth network model using more GPU
Practice, CPU is responsible for the update of parameter, and GPU carries out the training of network, obtains final mountain fire prediction model.
The mountain fire prediction model method for building up based on depth e-learning passes through oneself on building time and Spatial Dimension
Dynamic feature extraction network, and mass historical data is learnt, reach the analysis of mountain fire prediction and warning, overcomes traditional study side
Bottleneck of the formula in mass data can efficiently use GPU and distribution quickly to be trained；Traditional artificial feature is abandoned
Mode realizes feature automation；Depth network has been used flexibly to carry out effective integration to different dimensions feature, to be promoted
The forecasting efficiency and forecasting accuracy of mountain fire risk forecast model.
Claims (6)
1. the mountain fire prediction technique based on depth e-learning, it is characterised in that the following steps are included:
1), mountain fire risk forecast model is established using depth online learning methods；
The mountain fire risk forecast model includes convolutional layer, fused layer, LSTM layers, full articulamentum；
2), for the needs of mountain fire risk profile task, the remote sensing in the phase of history time forward is acquired since current time
Data and meteorological data；
3), handle to obtain the meteorological number as unit of day by temporal resolution fusion method to the meteorological data of 2) step acquisition
According to；
4), the meteorological data as unit of day for obtaining remotely-sensed data and 3) step is realized by spatial data fusion method
The fusion of spatial data matches；
5), by space is all converted to by remotely-sensed data that 4) step process obtains, meteorological data, rate is 500m* respectively
500m；Temporal resolution is 1 day, and format is the data of tiff format；
6) data that step 5) obtains, are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, the side of filling are carried out to data again
Formula is filled by adjacent point: shown in specific as follows:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude position in each point
It sets；
C, meteorological data is corresponded to according to longitude and latitude position, generates gas identical with remotely-sensed data matrix size in b step
Image data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows: [time
Step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix, the large size generated in the size and step d of target sample matrix are generated by history fire point data
Matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and by entire data
It is divided into different size of data set, batch data can be formed；
7), batch data are inputted to the convolutional layer of mountain fire risk forecast model, and extract the meteorology on the same day from batch data
Data input the full articulamentum of mountain fire risk forecast model, and final mountain fire Occurrence forecast probability can be obtained.
2. as described in claim 1 based on the mountain fire prediction technique of depth e-learning, it is characterised in that: in 3) step,
The temporal resolution fusion method is described in detail below:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out the corresponding statistics of polymerization generation
Index, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem for temperature,
Humidity, as daily amount total_mdFor rainfall level.
3. as claimed in claim 2 based on the mountain fire prediction technique of depth e-learning, it is characterised in that: in 4) step,
The spatial data fusion method is used close to matching method or krige interpolation method.
4. as described in claim 1 based on the mountain fire prediction technique of depth e-learning, it is characterised in that: in 1) step,
Establish mountain fire risk forecast model using depth online learning methods the following steps are included:
A, for the needs of mountain fire risk profile task, the remote sensing number in the phase of history time forward is acquired since current time
According to and meteorological data；
B, handle to obtain the meteorological number as unit of day by temporal resolution fusion method to the meteorological data of step A acquisition
According to；
C, the meteorological data as unit of day for obtaining remotely-sensed data and step B is realized empty by spatial data fusion method
Between data fusion matching；
D, the remotely-sensed data, the meteorological data that handle by step C are all converted to space rate is 500m*500m respectively；
Temporal resolution is 1 day, and format is the data of tiff format；
E, the data that step D is obtained are obtained into batch data by following processing, concrete processing procedure is as described below:
A, tiff data are resolved to matrix form by the parsing of tiff data；Missing Data Filling, the side of filling are carried out to data again
Formula is filled by adjacent point: shown in specific as follows:
x′I, j=xI-1, j|xI, j-1；
B, the remotely-sensed data of matrix form is divided into multiple remotely-sensed data matrixes, and records the longitude and latitude position in each point
It sets；
C, meteorological data is corresponded to according to longitude and latitude position, generates gas identical with remotely-sensed data matrix size in b step
Image data matrix；
D, meteorological data matrix and remotely-sensed data matrix are merged and ultimately generates large-scale matrix, the format of matrix are as follows: [time
Step number, matrix line number, matrix columns, characteristic]；
E, target sample matrix, the large size generated in the size and step d of target sample matrix are generated by history fire point data
Matrix is consistent；
F, the data of the large-scale matrix generated in the data of target sample matrix and step d are mapped, and by entire data
It is divided into different size of data set, batch data can be formed；
F, based on batch data construct depth network model, the depth network model include convolutional layer, fused layer, LSTM layers,
Full articulamentum；
The convolutional layer is stacked by multiple convolution units, and each convolution unit obtains with the following method: first will
Batch data carry out convolutional calculation as input matrix and obtain convolution results, the convolutional calculation formula are as follows: C=A*B, wherein *
Indicate that convolution algorithm, A indicate input matrix and a B convolution nuclear matrix, the convolution algorithm is public using following discrete calculation
Formula:Wherein m, n are the sizes values of convolution kernel.wm,nFor m in convolution kernel, the position n
Value, b are bigoted item；Then obtained convolution results are subjected to maximum pond and obtain convolution unit
The fused layer merges obtained convolution unit using ADD method, specifically, the convolution list that convolutional layer is obtained
Member carries out flatten expansion, and new feature is added, and the shape of the new feature is consistent with the matrix size of input, then presses
Stack is carried out according to time dimension, obtains fusion matrix [batch_size, time_steps, depth], wherein depth=w*h；
Described LSTM layers include LSTMCell layer, dropper layers and full-connect layers, will merge matrix [batch_size,
Time_steps, depth] it inputs after LSTMCell layers and successively passes through dropper layers and full-connect layers processing again and make
It is exported to obtain LSTM data with result function, the result function is using dimension tanh function output, the dimension tanh function
It is as described below:
The same day meteorological data for obtaining LSTM data and extracting from batch data is carried out fusion place by the full articulamentum
Reason；
G, depth network model is trained using more epoch, and carries out the training of depth network model using more GPU,
CPU is responsible for the update of parameter, and GPU carries out the training of network, obtains final mountain fire prediction model.
5. as claimed in claim 4 based on the mountain fire prediction technique of depth e-learning, it is characterised in that: in step B, institute
It is described in detail below to state temporal resolution fusion method:
By the d days meteorological index m in observation data m hourlyd,i(i ∈ 0 ..., 23) daily carry out the corresponding statistics of polymerization generation
Index, comprising: same day maximum value max_md, minimum value min_md, very poor value range_md, as daily amount total_md；
Wherein, max_md=max (md,i)(i∈0,…,23)
min_md=min (md,i)(i∈0,…,23)
range_md=max (md,i)-min(md,i)(i∈0,…,23)
total_md=sum (md,i)(i∈0,…,23)
Maximum value max_md, minimum value min_mdFor temperature, humidity, rainfall, wind speed, very poor value range_mdSystem for temperature,
Humidity, as daily amount total_mdFor rainfall level.
6. as claimed in claim 5 based on the mountain fire prediction technique of depth e-learning, it is characterised in that: in step C, institute
Spatial data fusion method is stated to use close to matching method or krige interpolation method.
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