CN106128035B - The geological disaster forecasting method merged based on neural network and multi-parameter information - Google Patents
The geological disaster forecasting method merged based on neural network and multi-parameter information Download PDFInfo
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Abstract
The invention discloses the geological disaster forecasting method based on neural network and multi-parameter information fusion, the geological disaster forecasting model based on neural network and multi-parameter information fusion is initially set up；Then set up the relationship between geological disaster probability of happening and geological disaster grade；Data acquisition finally is carried out using multi-parameter geological disaster monitoring system, realizes the forecast of geological disaster.The present invention carries out the foundation of forecasting model using RBF neural, corresponding geological disaster probability of happening under conditions present can be calculated, and geological disaster grade can be extrapolated according to probability of happening, prevent and reduce the generation of disaster so as to take the measure of corresponding grade；Fully considering influences the Multiple factors of callapsed landslide, Geological Hazards of debris, and more accurate foundation is provided to the decision that gives a forecast, and improving traditionally quality supervision examining system can only gathered data and the defect that cannot be analyzed.
Description
Technical field
The invention belongs to geological disaster forecasting technical field, it is related to the geology merged based on neural network and multi-parameter information
Hazard forecasting method.
Background technology
In recent years, Chinese large-sized geological disaster Frequent Accidents, such as mud-rock flow, callapsed landslide, the hair of these geological disasters
It is raw with randomness and sudden and huge, it frequently can lead to very serious casualties and economic loss.China
It is more men of mountain country, many places all have the hidden danger of geological disaster, if cannot solve the problem of forecast, geological disaster
More people will also be influenced.
The local conclusion obtained after various analyses that geology disaster accident occurs is as follows：1) cause of accident is main
By Geological Evolution and meteorological effect, in addition also individual accidents are caused by belonging to human factor；2) it causes huge
The result of casualties and economic loss is primarily due to no reliable monitoring device；3) area having is laid with monitoring and sets
It is standby, due to unsuitable forecasting procedure so that its maximum effect cannot be played by acquiring the data come, and lead to the wave of resource
Take.
By investigation and analysis and research, it is found that it is following several the factor for influencing callapsed landslide generation has：Rainfall, soil contain
Water rate, crack, pore water pressure and sedimentation variation etc.；The factor that influence mud-rock flow occurs has following several：Rainfall, soil contain
Water rate, mud position, mud speed, infrasonic sound and ground sound etc..The corresponding data of these factors can be collected by sensor, next very heavy
The work wanted is exactly to analyze these data, obtains its relationship between geological disaster probability of happening, if it is possible to logical
The mode for crossing founding mathematical models forecasts geological disaster, then can before geological disaster occurs to geological disaster into
Row early warning, to avoid serious casualties and economic loss.
Invention content
The object of the present invention is to provide the geological disaster forecasting methods based on neural network and multi-parameter information fusion, establish
Multi-parameter geological disaster monitoring system and geological disaster forecasting model realize analysis, rule statistics and geology to multi-parameter
The early warning of disaster.
The technical solution adopted in the present invention is the geological disaster forecasting side merged based on neural network and multi-parameter information
Method is specifically implemented according to the following steps：
Step 1, the geological disaster forecasting model merged based on neural network and multi-parameter information is established；
Step 2, the relationship between geological disaster probability of happening and geological disaster grade is established；
Step 3, data acquisition is carried out using multi-parameter geological disaster monitoring system, by collected data input step 1
With the forecast in step 2, realizing geological disaster.
It is of the invention to be further characterized in that,
Step 1 geological disaster forecasting model foundation process is：
Step 1.1：Training data arranges and the setting of threshold value；
Step 1.2：The foundation of forecasting model based on radial base (RBF) neural network；
The detailed process of step 1.1 is：
The mud-rock flow factor, the relationship of landslide factor and the extent of injury in geological disaster area occurred for statistics respectively, and will
Each parameter factors then select the maximum range of relevant parameter measurement sensor as training data, and by 0- maximum ranges it
Between the points divided rank of training data is installed, determine that the initial relation of each parameter and geological disaster probability of happening is distributed, will be at
Calamity probability is divided into tetra- ranges of 0-5%, 5-2-%, 20-40% and 40-90%, so that it is determined that the threshold value of each parameter.
The mud-rock flow factor includes effective precipitation, soil moisture content, mud position and undersonic frequency；Landslide factor includes day synthesis
Rainfall, soil moisture content, pore water pressure and crack displacement.
The detailed process of step 1.2 is：
Forecasting model is divided into input layer, hidden layer and output layer, and input is that multi-parameter and corresponding geological disaster occur
Probability calculates the weighting parameter in hidden layer, to obtain input and output by given input layer, output layer data
Between relational expression, specific formula is as follows：
A { 1 }=radbas (netprod (dist (net, IW { 1,1 }, p), net, b { 1 })) (1)
Wherein a is output -- geological disaster probability of happening, and p is that input -- training data acquires net；Then carry it into
Training pattern formula in Calling MATLAB function library：
Net=newrb (p1, a1) (2)
Wherein, the geological disaster forecasting model that net is, a1 are the current geological disaster probability of happening of output-, and p1 is
Input -- real-time data collection.
The detailed process of step 2 is：
Loss situation, probability of causing disaster and the extent of injury easily generated caused by the condition of a disaster occurs suddenly according to geological disaster,
Geological disaster grade is divided into level Four：Especially severe happens suddenly the condition of a disaster as level Four, and geological disaster probability of happening is 40-90%, is red
Color early warning, seriousness happen suddenly the condition of a disaster as three-level, and geological disaster probability of happening is 20-40%, are orange warning, large size burst the condition of a disaster
For two level, geological disaster probability of happening is 5-20%, is yellow early warning, and usual property burst the condition of a disaster is level-one, and geological disaster occurs
Probability is 0-5%, for blue early warning.
In step 3, multi-parameter geological disaster monitoring system include monitoring terminal, monitoring terminal respectively with sensor, power supply
Module, memory module connection, with monitoring center by wireless network connection, monitoring center connects monitoring terminal with onsite alarming device
It connects, wherein monitoring terminal is using STM32F103 as the minimum system of core devices, and inside includes 16 road A/D converters；Sensing
Device includes soil moisture content sensor, rainfall amount sensor, mud speed sensor, mud level sensor and ground sound, infrasound sensor, splits
Stitch sensor, osmotic pressure and sedimentation deformation sensor.
Memory module is K9F2G08UOC-SCBO programmable storages；Power module is that solar panel adds accumulator
Power supply mode.
Wireless network is GSM, GPRS or Beidou satellite communication.
The detailed process of step 3 is：
Monitoring terminal-pair soil moisture content sensor, rainfall amount sensor, mud in multi-parameter geological disaster monitoring system
Fast sensor, mud level sensor, infrasound sensor, crack sensors, osmotic pressure and the collected data of sedimentation deformation sensor
It is collected, and the data of collection is stored into the memory module of extension, when monitoring center is monitored by wireless network requirements
Terminal sends gathered data, or monitoring terminal, from when the sending time arrival of setting, monitoring terminal can pass through nothing
Gathered data is sent to monitoring center by gauze network, after monitoring center receives, the formula that is entered into step 1.2
(2) in, geological disaster probability of happening value a is obtained, geological disaster grade then can be obtained by the method for step 2.
The invention has the advantages that carrying out the foundation of forecasting model using RBF neural, it can calculate and work as preceding article
Corresponding geological disaster probability of happening under part, and geological disaster grade can be extrapolated according to probability of happening, so as to take
The measure of corresponding grade prevents and reduces the generation of disaster；Fully consider influence callapsed landslide, Geological Hazards of debris it is more
A factor provides more accurate foundation to the decision that gives a forecast, and improving traditionally quality supervision examining system can only gathered data
And the defect that cannot be analyzed；Self-learning function is added, correction is caused pre- since initial threshold setting does not conform to the actual conditions
Inaccurate problem is reported, the accuracy of geological disaster forecasting is improved.
Description of the drawings
Fig. 1 is the flow chart of the geological disaster forecasting method merged the present invention is based on neural network and multi-parameter information；
Fig. 2 is multi-parameter geology in the geological disaster forecasting method merged the present invention is based on neural network and multi-parameter information
Disaster monitoring system structural schematic diagram.
In Fig. 2,1. monitoring terminals, 2. power modules, 3. memory modules, 4. monitoring centers, 5. alarms, 6. soil water-containings
Rate sensor, 7. rainfall amount sensors, 8. mud speed sensors, 9. mud level sensors, 10. ground sonic transducers, 11. infrasonic sounds sensing
Device, 12. crack sensors, 13. osmolarity sensors, 14. sedimentation deformation sensors.
Specific implementation mode
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
The flow of the geological disaster forecasting method merged the present invention is based on neural network and multi-parameter information as shown in Figure 1,
It is specifically implemented according to the following steps：
Step 1, the geological disaster forecasting model merged based on neural network and multi-parameter information is established；
Step 1.1：Training data arranges and the setting of threshold value；
The mud-rock flow factor, the relationship of landslide factor and the extent of injury in geological disaster area occurred for statistics respectively, and will
Each parameter factors then select the maximum range of relevant parameter measurement sensor as training data, and by 0- maximum ranges it
Between the points divided rank of training data is installed, determine that the initial relation of each parameter and geological disaster probability of happening is distributed, will be at
Calamity probability is divided into tetra- ranges of 0-5%, 5-2-%, 20-40% and 40-90%, so that it is determined that the threshold value of each parameter, wherein mud
The rock glacier factor includes effective precipitation, soil moisture content, mud position and undersonic frequency；Landslide factor includes daily synthetic rainfall, soil
Moisture content, pore water pressure and crack displacement.The relationship of the mud-rock flow factor, landslide factor and the probability that causes disaster is respectively such as Fig. 1 and Fig. 2
It is shown：
The relationship of each factor of 1 mud-rock flow of table and the probability that causes disaster
Table 2 comes down each factor and the relationship of probability of causing disaster
Step 1.2：The foundation of forecasting model based on radial base (RBF) neural network；
RBF networks can approach arbitrary nonlinear function, can have with the regularity for being difficult to parse in processing system
Good generalization ability, and have study convergence rate quickly, be successfully applied to nonlinear function approach, time series point
Analysis, data classification, pattern-recognition, information processing, image procossing, system modelling, control and fault diagnosis etc., forecasting model institute according to
The microcomputer in service station centered on the running environment of support.
Forecasting model is divided into input layer, hidden layer and output layer, and input is that multi-parameter and corresponding geological disaster occur
Probability calculates the weighting parameter in hidden layer, to obtain input and output by given input layer, output layer data
Between relational expression, specific formula is as follows：
A { 1 }=radbas (netprod (dist (net, IW { 1,1 }, p), net, b { 1 })) (1)
Wherein a is output -- geological disaster probability of happening, and p is that input -- training data acquires net；Then carry it into
Training pattern formula in Calling MATLAB function library：
Net=newrb (p1, a1) (2)
Wherein, the geological disaster forecasting model that net is, a1 are the current geological disaster probability of happening of output-, and p1 is
Input -- real-time data collection；
After geological disaster model formation (2) is set up, geological disaster forecasting model is automatically updated based on real time data
Self study be achieved in that every time acquisition come real time data, in addition to for analyzing current geological disaster probability of happening
Except grade, it is additionally operable to the update to training data, i.e. partial data in p in formula (1) is replaced by real time data, so
The training for carrying out model with updated p again afterwards obtains more accurate forecast model.
Step 2, the relationship between geological disaster probability of happening and geological disaster grade is established；
Loss situation, probability of causing disaster and the extent of injury easily generated caused by the condition of a disaster occurs suddenly according to geological disaster,
Geological disaster grade is divided into level Four：Landslide critical forecast grade into the middle and later periods in tertiary creep stage is alarm grade, especially
Serious burst the condition of a disaster is level Four, and geological disaster probability of happening is 40-90%, is red early warning, into tertiary creep stage mid-term
Landslide critical forecast grade be warning grade, seriousness happen suddenly the condition of a disaster be three-level, geological disaster probability of happening be 20-40%, be
Orange warning, the landslide critical forecast grade into the middle and later periods in initial acceleration creep stage are warning grade, and large size burst the condition of a disaster is
Two level, geological disaster probability of happening are 5-20%, are yellow early warning, the landslide critical forecast grade into the constant rate creeep stage is
Notice that grade, usual property burst the condition of a disaster are level-one, geological disaster probability of happening is 0-5%, for blue early warning, is specifically shown in Table 3：
3 grade classification of table
Step 3, data acquisition is carried out using multi-parameter geological disaster monitoring system, by collected data input step 1
With the forecast in step 2, realizing geological disaster：
Multi-parameter geological disaster monitoring system include monitoring terminal 1, monitoring terminal 1 respectively with sensor, power module 2,
Memory module 3 connects, and with monitoring center 4 by wireless network connection, monitoring center 4 connects monitoring terminal 1 with onsite alarming device 5
It connects, wherein sensor includes soil moisture content sensor 6, rainfall amount sensor 7, mud speed sensor 8, mud level sensor 9 and ground
Sonic transducer 10, infrasound sensor 11, crack sensors 12, osmolarity sensor 13 and sedimentation deformation sensor 14, structure
Schematic diagram is as shown in Figure 2.
Detailed process is：Monitoring terminal-pair soil moisture content sensor in multi-parameter geological disaster monitoring system, rainfall
Quantity sensor, mud speed sensor, mud level sensor, sonic transducer, infrasound sensor, crack sensors, osmotic pressure and sedimentation
The collected data of displacement sensor are collected, and the data of collection are stored into the memory module of extension, when in monitoring
The heart by wireless network requirements monitor terminal gathered data is sent, or monitoring terminal from setting sending time reach when
It waits, gathered data can be sent to monitoring center by monitoring terminal by wireless network, after monitoring center receives, its is defeated
Enter in the formula (2) into step 1.2, obtains geological disaster probability of happening value a, geology calamity then can be obtained by the method for step 2
Evil grade.
Wherein, monitoring terminal is using STM32F103 as the minimum system of core devices, and inside includes 16 road A/D converters,
The chip has：32 Cortex-M3CPU, highest 72MHZ working frequencies, monocycle multiplication and hardware division, 256K bytes
FLASH program memory, 48K bytes SRAM the features such as.Rain sensor should be laid in debris flow formation region and its heavy rain
Spacious, the flat and small windage location with interior surrounding should also carry out website laying when having ready conditions according to altitudinal gradient.The soil body
Pore water pressure and water content sensor should be laid in basin middle and upper reaches and be easier to the object started and fine grained is more under heavy showers
In the open slopes of source region.The relatively regular straight location of raceway groove is less in mud-rock flow small watershed, groove gradient is larger, while relatively low ditch
Bank has the danger for being silted and burying with sapping, therefore contactless mud level sensor, displacement sensor and vibrating sensor should be laid
Straight, permeability preferably, than dropping in small basin middle and lower reaches Circulation Area ditch section, with base fixed and meanwhile in flood peak line with
On boulder, basement rock, dykes and dams, debris dam, bridge etc. be advisable.Especially mud level sensor and displacement sensor are in its laying section river
Section planted agent is fed without other runoffs or increment can be ignored, and considers downstream construction area according to mud-rock flow movement speed
Withdraw the time of required advance alert.All wiring are attached using the aviation plug of water proof and dust proof.Memory module is
K9F2G08UOC-SCBO programmable storages；
Data transmission includes mainly short range wireless data transmission and two parts of long distance wireless data transmission, short-distance radio number
According to transmission device include short range wireless data transmission and relay station, long distance wireless data transmission include GPRS, GSM and big-dipper satellite
Terminal transmission, data transmission system is that data are passed back to field data by effective communication mode, while by pre-warning signal
It is transmitted to onsite alarming equipment and realizes alarm, is i.e. scene is analyzed according to sensor gathered data, after the threshold value for reaching setting
Alarm is realized by traffic lights and early warning broadcast, while gathered data can be transmitted by public network or big-dipper satellite is to indoor number
It is analyzed according to central store, the information that can directly carry out early warning to scene by data center after short range wireless communications are invalid is issued.
Pre-warning signal is transmitted to onsite alarming equipment by the business small for field data amount, emergency is high using short range wireless transmission,
Early warning is carried out in time, short-distance radio early warning propagation distance is within 2 kms, and centre is obstructed without big building or mountains and rivers,
If there is barrier leads to not receive data, it is also necessary to set up radio repeater station.For the area for thering is public network to cover, should generally select
Group (GSM/GPRS) is carried out with public network；It is general that Beidou satellite communication mode is selected to carry out group for the not covered area of public network
Net monitors region and conditional area for emphasis, and two kinds of different communication modes can be selected and give networking, realize standby each other
Part, automatically switch function, it is ensured that information transmission channel it is unimpeded.
In view of live emergency service actual conditions, it is all made of the power supply mode that solar panel adds accumulator, electric power storage
Pond uses Li-ion batteries piles, has securely and reliably, and small, light-weight, easy to use, self discharge is small, and it is permanent to can be used
The features such as, at the scene conditional local solar panel can directly lithium battery charging, for equipment work normally 10 days with
On time lithium battery can be used directly and be powered in unconditional place, lithium battery is under full state without charge condition
Under 3 days or more time are worked normally for equipment.
Claims (5)
1. the geological disaster forecasting method merged based on neural network and multi-parameter information, which is characterized in that specifically according to following
Step is implemented：
Step 1, the geological disaster forecasting model merged based on neural network and multi-parameter information is established；
Geological disaster forecasting model foundation process is：
Step 1.1：Training data arranges and the setting of threshold value；
Detailed process is：The pass of the mud-rock flow factor, landslide factor and the extent of injury that geological disaster area occurred is counted respectively
System, and using each parameter factors as training data, the maximum range of relevant parameter measurement sensor is then selected, and 0- is maximum
The points divided rank that training data is installed between range determines initial relation point of each parameter with geological disaster probability of happening
Cloth, the probability that will cause disaster is divided into tetra- ranges of 0-5%, 5-2-%, 20-40% and 40-90%, so that it is determined that the threshold value of each parameter；
Step 1.2：The foundation of forecasting model based on radial base neural net；
Detailed process is：Forecasting model is divided into input layer, hidden layer and output layer, and input is multi-parameter and corresponding geology calamity
Evil probability of happening calculates the weighting parameter in hidden layer by given input layer, output layer data, to obtain input with
Relational expression between output, specific formula are as follows：
A { 1 }=radbas (netprod (dist (net, IW { 1,1 }, p), net, b { 1 })) (1)
Wherein a is output -- geological disaster probability of happening, and p is that input -- training data acquires net；Then carry it into calling
Training pattern formula in MATLAB function libraries：
Net=newrb (p1, a1) (2)
Wherein, the geological disaster forecasting model that net is, a1 are the current geological disaster probability of happening of output-, and p1 is defeated
Enter -- real-time data collection；
Step 2, the relationship between geological disaster probability of happening and geological disaster grade is established；
Detailed process is：Loss situation, probability of causing disaster and the danger easily generated caused by the condition of a disaster occurs suddenly according to geological disaster
Evil degree, is divided into level Four by geological disaster grade：Especially severe happens suddenly the condition of a disaster as level Four, and geological disaster probability of happening is 40-
90%, it is red early warning, seriousness happens suddenly the condition of a disaster as three-level, and geological disaster probability of happening is 20-40%, is orange warning, greatly
Type happens suddenly the condition of a disaster as two level, and geological disaster probability of happening is 5-20%, is yellow early warning, and usual property burst the condition of a disaster is level-one, ground
Matter disaster probability of happening is 0-5%, for blue early warning；
Step 3, data acquisition is carried out using multi-parameter geological disaster monitoring system, by collected data input step 1 and step
In rapid 2, the forecast of geological disaster is realized；
Multi-parameter geological disaster monitoring system include monitoring terminal (1), monitoring terminal (1) respectively with sensor, power module
(2), memory module (3) connects, monitoring terminal (1) with monitoring center (4) by wireless network connection, monitoring center (4) with it is existing
Field alarm (5) connects, wherein monitoring terminal (1) is using STM32F103 as the minimum system of core devices, and inside includes 16
Road A/D converter；Sensor includes soil moisture content sensor (6), rainfall amount sensor (7), mud speed sensor (8), mud position
Sensor (9) and ground sonic transducer (10), infrasound sensor (11), crack sensors (12), osmolarity sensor (13) and sink
Decrease displacement sensor (14).
2. the geological disaster forecasting method according to claim 1 merged based on neural network and multi-parameter information, special
Sign is that the mud-rock flow factor includes effective precipitation, soil moisture content, mud position and undersonic frequency；Landslide factor includes day synthesis
Rainfall, soil moisture content, pore water pressure and crack displacement.
3. the geological disaster forecasting method according to claim 1 merged based on neural network and multi-parameter information, special
Sign is that memory module (3) is K9F2G08UOC-SCBO programmable storages；Power module (2) is that solar panel adds storage
The power supply mode of battery.
4. the geological disaster forecasting method according to claim 1 merged based on neural network and multi-parameter information, special
Sign is, wireless network GSM, GPRS or Beidou satellite communication.
5. the geological disaster forecasting method according to claim 1 merged based on neural network and multi-parameter information, special
Sign is that the detailed process of step 3 is：
Monitoring terminal-pair soil moisture content sensor, rainfall amount sensor, mud speed in multi-parameter geological disaster monitoring system pass
Sensor, mud level sensor, infrasound sensor, crack sensors, osmotic pressure and the collected data of sedimentation deformation sensor carry out
It collects, and the data of collection is stored into the memory module of extension, when monitoring center monitors terminal by wireless network requirements
Gathered data is sent, or monitoring terminal, from when the sending time arrival of setting, monitoring terminal can pass through wireless network
Gathered data is sent to monitoring center by network, after monitoring center receives, is entered into the formula in step 1.2 (2),
Geological disaster probability of happening value a is obtained, geological disaster grade then can be obtained by the method for step 2.
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