CN109472396B - Mountain fire prediction method based on deep network learning - Google Patents
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Abstract
The invention discloses a mountain fire prediction method based on deep network learning, which can improve prediction efficiency and prediction accuracy. According to the mountain fire prediction method based on deep network learning, the mountain fire prediction early warning analysis is achieved by constructing an automatic feature extraction network in time and space dimensions and learning massive historical data. The bottleneck of the traditional learning mode on mass data is overcome, and the GPU and the distributed type are effectively utilized for rapid training; the traditional manual feature mode is abandoned, and feature automation is realized; the depth network is used for flexibly and effectively fusing the features with different dimensions, so that the prediction efficiency and the prediction accuracy of mountain fire risk prediction are improved. Is suitable for popularization and application in the technical field of data processing.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a mountain fire prediction method based on deep network learning.
Background
In natural disasters, mountain fires are a very special and damaging disaster; not only can have a great influence on ecology, but also can have great losses on people, animals and some 'assets' in places; towers, cables, substations as in electrical equipment; signal towers in communications, and the like. The generation mechanism of the mountain fire is very complex, and the mountain fire can only occur under the interaction of a plurality of factors.
With the development of technologies such as the Internet of things, the digitalization and the Internet of things, a large amount of data can be obtained in a reasonable mode, and natural disasters can be analyzed, predicted, early-warned and the like in a non-experimental mode by combining with the improvement of the computing capability. Second, the current data is not only big data, but also diversified and complicated. Similar to the problem of natural disasters, the method not only comprises the geospatial temperature, but also comprises the time dimension, and the effective combination modeling of the two dimensions is also a difficulty of many traditional mathematical models at present. Therefore, the invention mainly utilizes the deep learning of the current hot spot technology to solve the problem of time and space modeling, and combines the characteristics of mountain fire to apply in mountain fire prediction and early warning.
The traditional feature extraction mode is to extract in space and time dimensions respectively, and then to carry out subsequent analysis and modeling, so that the feature extraction mode has certain subjectivity, and effective features cannot be easily found to support a subsequent model, so that a model effect is poor.
Traditional machine learning models are based on patterns of artificial features, cannot make efficient use of spatial features, model in the time dimension, traditional time series cannot build long-term time dependencies, and time series are mainly also statistical-based. Such conventional machine learning algorithms are designed without spatial or temporal concepts, and are feature-based learning processes.
In feature fusion, each feature is typically added to each sample, and even if there is a global feature, this approach is not flexible enough and problems can occur when different types of features or inconsistent features are fused.
The traditional learning mode is based on a certain data volume, and problems can occur when the data volume is too small or too large. When the data amount is too small, the reliability of the result is low. However, the traditional learning method is very difficult to process mass data and has a certain bottleneck.
Disclosure of Invention
The invention aims to provide a forest fire prediction method based on deep network learning, which can improve prediction efficiency and prediction accuracy.
The technical scheme adopted for solving the technical problems is as follows: the mountain fire prediction method based on deep network learning comprises the following steps:
1) Establishing a forest fire risk prediction model by using a deep network learning method;
the mountain fire risk prediction model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
2) Aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected;
3) Processing the meteorological data acquired in the step 2) by a time resolution fusion method to obtain meteorological data taking a day as a unit;
4) The remote sensing data and the weather data which is obtained in the step 3) and takes the day as a unit are fused and matched with each other through a spatial data fusion method;
5) All the remote sensing data and the meteorological data obtained through the processing of the step 4) are converted into the space division ratio 500m by 500m; time resolution is 1 day, and data with a tiff format is formed;
6) And 5) processing the data obtained in the step 5) to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j |x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data;
7) And inputting the batch data into a convolution layer of the mountain fire risk prediction model, extracting the weather data of the current day from the batch data, and inputting the weather data into a full-connection layer of the mountain fire risk prediction model, so that the final mountain fire occurrence prediction probability can be obtained.
Further, in the step 3), the time resolution fusion method is specifically as follows:
will be on day dObservation data m of meteorological index m per hour d,i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d,i )(i∈0,…,23)
min_m d ＝min(m d,i )(i∈0,…,23)
range_m d ＝max(m d,i )-min(m d,i )(i∈0,…,23)
total_m d ＝sum(m d,i )(i∈0,…,23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d The total amount of the current day total_m is used for temperature and humidity d For rainfall levels.
Further, in the step 4), the spatial data fusion method adopts a close-proximity matching method or a krige interpolation method.
Further, in the step 1), the step of establishing the mountain fire risk prediction model by using the deep network learning method comprises the following steps:
A. aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected;
B. c, processing the meteorological data acquired in the step A by a time resolution fusion method to obtain meteorological data taking a day as a unit;
C. the remote sensing data and the weather data which is obtained in the step B and takes the day as a unit are fused and matched with each other through a space data fusion method;
D. all the remote sensing data and the meteorological data obtained through the processing in the step C are converted into the space division ratio 500 m-500 m; time resolution is 1 day, and data with a tiff format is formed;
E. and D, processing the data obtained in the step D to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j |x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data;
F. constructing a depth network model based on batch data, wherein the depth network model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
the convolution layer is formed by stacking a plurality of convolution units, and each convolution unit is obtained by adopting the following method: firstly, taking batch data as an input matrix to carry out convolution calculation to obtain a convolution result, wherein the convolution calculation formula is as follows: c=a×b, where x represents a convolution operation, a represents an input matrix and a B convolution kernel matrix, the convolution operation using the following discrete calculation formula:
where m, n are the size values of the convolution kernel. w (w) m,n The value of m and n positions in the convolution kernel, and b is a paranoid item; and then carrying out maximum pooling on the obtained convolution result to obtain a convolution unit
The fusion layer fuses the obtained convolution units by adopting an ADD method, specifically, the convolution units obtained by the convolution layer are subjected to flat expansion, new features are added, the shape of the new features is consistent with the size of an input matrix, and stack is carried out according to a time dimension to obtain a fusion matrix [ batch_size, time_steps, depth ], wherein depth=w×h;
the LSTM layer comprises an LSTMCell layer, a drope layer and a full-connect layer, and is used for fusing matrixes (batch_size, time_steps, depth)]After being input into the LSTMCell layer, the LSTM data is processed by the dropper layer and the full-connect layer in sequence and is output by using a result function, wherein the result function is output by adopting a vitamin H function, and the vitamin H function is as follows:
the full connection layer carries out fusion processing on the obtained LSTM data and the current day meteorological data extracted from the batch data;
G. and training the depth network model by adopting multiple epochs, training the depth network model by adopting multiple GPUs, updating parameters by the CPU, and training the network by adopting the GPUs to obtain a final mountain fire prediction model.
Further, in the step B, the time resolution fusion method is specifically as follows:
observation data m of d weather image index m in each hour d,i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d,i )(i∈0,…,23)
min_m d ＝min(m d,i )(i∈0,…,23)
range_m d ＝max(m d,i )-min(m d,i )(i∈0,…,23)
total_m d ＝sum(m d,i )(i∈0,…,23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d The total amount of the current day total_m is used for temperature and humidity d For rainfall levels.
Further, in the step C, the spatial data fusion method adopts a close-proximity matching method or a krige interpolation method.
The invention has the beneficial effects that: according to the mountain fire prediction method based on deep network learning, the mountain fire prediction early warning analysis is achieved by constructing an automatic feature extraction network in time and space dimensions and learning massive historical data. The bottleneck of the traditional learning mode on mass data is overcome, and the GPU and the distributed type are effectively utilized for rapid training; the traditional manual feature mode is abandoned, and feature automation is realized; the depth network is used for flexibly and effectively fusing the features with different dimensions, so that the prediction efficiency and the prediction accuracy of mountain fire risk prediction are improved.
Detailed Description
The mountain fire prediction method based on deep network learning comprises the following steps:
1) Establishing a forest fire risk prediction model by using a deep network learning method;
the mountain fire risk prediction model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
2) Aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected; the remote sensing data comprises: the water content FMC of the combustible, the load FL of the combustible and the type FT of the combustible; elevation, gradient, slope direction; the meteorological data comprise temperature, humidity, rainfall, wind speed and wind direction;
3) Processing the meteorological data acquired in the step 2) by a time resolution fusion method to obtain meteorological data taking a day as a unit; the time resolution fusion method is specifically as follows:
observation data m of d weather image index m in each hour d,i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d,i )(i∈0,…,23)
min_m d ＝min(m d,i )(i∈0,…,23)
range_m d ＝max(m d,i )-min(m d,i )(i∈0,…,23)
total_m d ＝sum(m d,i )(i∈0,…,23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d The total amount of the current day total_m is used for temperature and humidity d For rainfall levels;
4) The remote sensing data and the weather data which is obtained in the step 3) and takes the day as a unit are fused and matched with each other through a spatial data fusion method; the spatial data fusion method adopts an adjacent matching method or a krige interpolation method;
5) All the remote sensing data and the meteorological data obtained through the processing of the step 4) are converted into the space division ratio 500m by 500m; time resolution is 1 day, and data with a tiff format is formed;
6) And 5) processing the data obtained in the step 5) to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j |x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data; in the batch data assembling process, the positive example data are fewer, and in order to solve the proportion imbalance, the positive example data are assembled into each batch when each time of use;
7) And inputting the batch data into a convolution layer of the mountain fire risk prediction model, extracting the weather data of the current day from the batch data, and inputting the weather data into a full-connection layer of the mountain fire risk prediction model, so that the final mountain fire occurrence prediction probability can be obtained.
According to the mountain fire prediction method based on deep network learning, the mountain fire prediction early warning analysis is achieved by constructing an automatic feature extraction network in time and space dimensions and learning massive historical data. The bottleneck of the traditional learning mode on mass data is overcome, and the GPU and the distributed type are effectively utilized for rapid training; the traditional manual feature mode is abandoned, and feature automation is realized; the depth network is used for flexibly and effectively fusing the features with different dimensions, so that the prediction efficiency and the prediction accuracy of mountain fire risk prediction are improved.
In the above embodiment, in step 1), the establishing the forest fire risk prediction model by using the deep network learning method includes the steps of:
A. aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected; the remote sensing data comprises: the water content FMC of the combustible, the load FL of the combustible and the type FT of the combustible; elevation, gradient, slope direction; the meteorological data comprise temperature, humidity, rainfall, wind speed and wind direction;
B. c, processing the meteorological data acquired in the step A by a time resolution fusion method to obtain meteorological data taking a day as a unit; the time resolution fusion method is specifically as follows:
observation data m of d weather image index m in each hour d,i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d,i )(i∈0,…,23)
min_m d ＝min(m d,i )(i∈0,…,23)
range_m d ＝max(m d,i )-min(m d,i )(i∈0,…,23)
total_m d ＝sum(m d,i )(i∈0,…,23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d The total amount of the current day total_m is used for temperature and humidity d For rainfall levels;
C. the remote sensing data and the weather data which is obtained in the step B and takes the day as a unit are fused and matched with each other through a space data fusion method; the spatial data fusion method adopts an adjacent matching method or a krige interpolation method;
D. all the remote sensing data and the meteorological data obtained through the processing in the step C are converted into the space division ratio 500 m-500 m; time resolution is 1 day, and data with a tiff format is formed;
E. and D, processing the data obtained in the step D to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j |x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data; in the batch data assembling process, the positive example data are fewer, and in order to solve the proportion imbalance, the positive example data are assembled into each batch when each time of use;
F. constructing a depth network model based on batch data, wherein the depth network model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
the convolution layer is formed by stacking a plurality of convolution units, and each convolution unit is obtained by adopting the following method: firstly, taking batch data as an input matrix to carry out convolution calculation to obtain a convolution result, wherein the convolution calculation formula is as follows: c=a×b, where x represents a convolution operation, a represents an input matrix and a B convolution kernel matrix, the convolution operation using the following discrete calculation formula:
where m, n are the size values of the convolution kernel. w (w) m,n The value of m and n positions in the convolution kernel, and b is a paranoid item; then willThe obtained convolution result is maximally pooled to obtain a convolution unit
The fusion layer fuses the obtained convolution units by adopting an ADD method, specifically, the convolution units obtained by the convolution layer are subjected to flat expansion, new features are added, the shape of the new features is consistent with the size of an input matrix, and stack is carried out according to a time dimension to obtain a fusion matrix [ batch_size, time_steps, depth ], wherein depth=w×h;
the LSTM layer comprises an LSTMCell layer, a drope layer and a full-connect layer, and is used for fusing matrixes (batch_size, time_steps, depth)]After being input into the LSTMCell layer, the LSTM data is processed by the dropper layer and the full-connect layer in sequence and is output by using a result function, wherein the result function is output by adopting a vitamin H function, and the vitamin H function is as follows:
the full connection layer carries out fusion processing on the obtained LSTM data and the current day meteorological data extracted from the batch data;
G. and training the depth network model by adopting multiple epochs, training the depth network model by adopting multiple GPUs, updating parameters by the CPU, and training the network by adopting the GPUs to obtain a final mountain fire prediction model.
The mountain fire prediction model establishment method based on deep network learning achieves mountain fire prediction early warning analysis by constructing an automatic feature extraction network in time and space dimensions and learning massive historical data, overcomes the bottleneck of a traditional learning mode on massive data, and can effectively utilize a GPU and a distributed mode to perform rapid training; the traditional manual feature mode is abandoned, and feature automation is realized; the depth network is used for flexibly and effectively fusing the features with different dimensions, so that the prediction efficiency and the prediction accuracy of the forest fire risk prediction model are improved.
Claims (6)
1. The mountain fire prediction method based on deep network learning is characterized by comprising the following steps of:
1) Establishing a forest fire risk prediction model by using a deep network learning method;
the mountain fire risk prediction model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
2) Aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected;
3) Processing the meteorological data acquired in the step 2) by a time resolution fusion method to obtain meteorological data taking a day as a unit;
4) The remote sensing data and the weather data which is obtained in the step 3) and takes the day as a unit are fused and matched with each other through a spatial data fusion method;
5) All the remote sensing data and the meteorological data obtained through the processing of the step 4) are converted into the space division ratio 500m by 500m; time resolution is 1 day, and data with a tiff format is formed;
6) And 5) processing the data obtained in the step 5) to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j ||x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data;
7) And inputting the batch data into a convolution layer of the mountain fire risk prediction model, extracting the weather data of the current day from the batch data, and inputting the weather data into a full-connection layer of the mountain fire risk prediction model, so that the final mountain fire occurrence prediction probability can be obtained.
2. The mountain fire prediction method based on deep network learning as claimed in claim 1, wherein: in the step 3), the time resolution fusion method is specifically as follows:
observation data m of d weather image index m in each hour d，i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d，i )(i∈0，…，23)
min_m d ＝min(m d，i )(i∈0，…，23)
range_m d ＝max(m d，i )-min(m d，i )(i∈0，…，23)
total_m d ＝sum(m d，i )(i∈0，…，23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d The total amount of the current day total_m is used for temperature and humidity d For rainfall levels.
3. The mountain fire prediction method based on deep network learning as claimed in claim 2, wherein: in the step 4), the spatial data fusion method adopts a close-proximity matching method or a krige interpolation method.
4. The mountain fire prediction method based on deep network learning as claimed in claim 1, wherein: in the step 1), the step of establishing a mountain fire risk prediction model by using a deep network learning method comprises the following steps of:
A. aiming at the requirements of mountain fire risk prediction tasks, remote sensing data and meteorological data in a historical time from the current time to the front are collected;
B. c, processing the meteorological data acquired in the step A by a time resolution fusion method to obtain meteorological data taking a day as a unit;
C. the remote sensing data and the weather data which is obtained in the step B and takes the day as a unit are fused and matched with each other through a space data fusion method;
D. all the remote sensing data and the meteorological data obtained through the processing in the step C are converted into the space division ratio 500 m-500 m; time resolution is 1 day, and data with a tiff format is formed;
E. and D, processing the data obtained in the step D to obtain batch data, wherein the specific processing process is as follows:
a. analyzing tiff data, namely analyzing the tiff data into a matrix form; and filling the missing values of the data in a manner of filling through adjacent points: the method is specifically as follows:
x′ i，j ＝x i-1，j |x i，j-1 ；
b. dividing the remote sensing data in a matrix form into a plurality of remote sensing data matrixes, and recording longitude and latitude positions in each point;
c. the meteorological data are corresponding according to the longitude and latitude positions, and a meteorological data matrix with the same size as the remote sensing data matrix in the step b is generated;
d. combining the meteorological data matrix and the remote sensing data matrix to finally generate a large matrix, wherein the format of the matrix is as follows: time step number, matrix line number, matrix column number, feature number ];
e. generating a target sample matrix through historical fire data, wherein the size of the target sample matrix is consistent with that of the large matrix generated in the step d;
f. mapping the data of the target sample matrix and the data of the large matrix generated in the step d, and dividing the whole data into data sets with different sizes to form batch data;
F. constructing a depth network model based on batch data, wherein the depth network model comprises a convolution layer, a fusion layer, an LSTM layer and a full connection layer;
the convolution layer is formed by stacking a plurality of convolution units, and each convolution unit is obtained by adopting the following method: firstly, taking batch data as an input matrix to carry out convolution calculation to obtain a convolution result, wherein the convolution calculation formula is as follows: c=a×b, where x represents a convolution operation, a represents an input matrix and a B convolution kernel matrix, the convolution operation using the following discrete calculation formula:
where m, n are the size values of the convolution kernel, w m，n The value of m and n positions in the convolution kernel, and b is a paranoid item; and then carrying out maximum pooling on the obtained convolution result to obtain a convolution unit
The fusion layer fuses the obtained convolution units by adopting an ADD method, specifically, the convolution units obtained by the convolution layer are subjected to flat expansion, new features are added, the shape of the new features is consistent with the size of an input matrix, and stack is carried out according to a time dimension to obtain a fusion matrix [ batch_size, time_steps, depth ], wherein depth=w×h;
the LSTM layer comprises an LSTMCell layer, a drope layer and a full-connect layer, and is used for fusing matrixes (batch_size, time_steps, depth)]Inputting LSTMCell layer, sequentially passing through dropperThe layers and full-connect layers process and output LSTM data using a result function that is output using a vitamin H function, which is described below:
the full connection layer carries out fusion processing on the obtained LSTM data and the current day meteorological data extracted from the batch data;
G. and training the depth network model by adopting multiple epochs, training the depth network model by adopting multiple GPUs, updating parameters by the CPU, and training the network by adopting the GPUs to obtain a final mountain fire prediction model.
5. The mountain fire prediction method based on deep network learning as claimed in claim 4, wherein: in the step B, the time resolution fusion method is specifically as follows:
observation data m of d weather image index m in each hour d，i (i.epsilon.0, …, 23) to generate corresponding statistical indexes by aggregation according to days, including: maximum value of current day max_m d Minimum value min_m d Range_m d Total of current day total_m d ；
Wherein max_m d ＝max(m d，i )(i∈0，…，23)
min_m d ＝min(m d，i )(i∈0，…，23)
range_m d ＝max(m d，i )-min(m d，i )(i∈0，…，23)
total_m d ＝sum(m d，i )(i∈0，…，23)
Maximum max_m d Minimum value min_m d For temperature, humidity, rainfall, wind speed, range_m d Is used for temperature, humidity and dayTotal total_m d For rainfall levels.
6. The mountain fire prediction method based on deep network learning as claimed in claim 5, wherein: in the step C, the spatial data fusion method adopts a close-proximity matching method or a krige interpolation method.
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