CN107577913A - A kind of Regional Landslide disaster assessment of easy generation device - Google Patents
A kind of Regional Landslide disaster assessment of easy generation device Download PDFInfo
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- CN107577913A CN107577913A CN201710767482.7A CN201710767482A CN107577913A CN 107577913 A CN107577913 A CN 107577913A CN 201710767482 A CN201710767482 A CN 201710767482A CN 107577913 A CN107577913 A CN 107577913A
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Abstract
The invention discloses a kind of Regional Landslide disaster assessment of easy generation device, it includes：Flood inducing factors determining module, for choosing the Flood inducing factors of rainfall, landforms, the gradient and formation lithology as landslide disaster, and it is divided into five level of risk；Assignment module, for level of risk to be carried out into assignment from one to five；Training module, for using a large amount of district disasters statistics and rain making slope erosion result of the test as training sample, being trained using BP Artificial Neural Networks are improved to neutral net, obtaining the weight coefficient of each Flood inducing factors；Statistical module, for being multiplied the assignment of level of risk corresponding to each Flood inducing factors with weight coefficient to obtain influence value；Evaluation module, summed for determining level of risk corresponding to the Flood inducing factors of target slopes, and by assignment numerical value corresponding to level of risk to judge liability rank.The present invention can be assessed Regional Landslide disaster liability, and strong foundation is provided for the prevention of landslide disaster.
Description
Technical field
The present invention relates to landslide monitoring technical field, more particularly to a kind of Regional Landslide disaster assessment of easy generation device.
Background technology
Landslide disaster be mountain area, both sides of the road, river valley two sides and the recurrent geological disaster of road construction site or
Geological Hazard, outburst is unexpected, and injury power is huge, administers and prediction is difficult, if prediction is not in time, can cause huge people
Member's injures and deaths and material damage.
Due to the complicated condition of the debris flow formations such as China's topography and geomorphology, geology, at present, for the liability of landslide disaster
Assess also without ripe scheme is formed, more researchs are that the condition of a disaster after landslide disaster occurs is assessed.Therefore, compel to be essential
The liability of landslide disaster is assessed, improve disaster prevention ability.
The content of the invention
, can be to area the present invention solves the technical problem of a kind of Regional Landslide disaster assessment of easy generation device is provided
Domain landslide disaster liability is assessed, and strong foundation is provided for the prevention of landslide disaster.
In order to solve the above technical problems, one aspect of the present invention is：It is easy to provide a kind of Regional Landslide disaster
Hair property apparatus for evaluating, including：Flood inducing factors determining module, for choosing rainfall, landforms, the gradient and formation lithology as landslide
The Flood inducing factors of disaster, and each Flood inducing factors are divided into five level of risk according to the priority of degree of danger；
Assignment module, for level of risk corresponding to each Flood inducing factors to be carried out into assignment respectively from one to five, wherein, numerical value
It is bigger to represent more dangerous；Training module, for using a large amount of district disasters statistics and rain making slope erosion result of the test as instruction
Practice sample, neutral net is trained using BP Artificial Neural Networks are improved, obtain each Flood inducing factors to cunning
The weight coefficient that slope disaster liability influences；Statistical module, for by level of risk corresponding to each Flood inducing factors
Assignment is multiplied to obtain influence value with weight coefficient；Evaluation module, it is dangerous corresponding to the Flood inducing factors of target slopes for determining
Rank, and assignment numerical value corresponding to the level of risk of each Flood inducing factors is summed to obtain summing value, according to summing value
Judge liability rank, if summing value is less than first threshold, it is low to judge liability rank；If summing value is higher than first
Threshold value and it is less than Second Threshold, then in judging that liability rank is；If summing value is higher than Second Threshold, liability level is judged
Wei not be high.
Preferably, scope corresponding to five level of risk of the rainfall be respectively≤200, ＞ 200 and≤400,
＞ 400 and≤600, ＞ 600 and≤800 and ＞ 800.
Preferably, type corresponding to five level of risk of the landforms be respectively Plain, hills, high mountain, plateau and
Low mountain.
Preferably, respectively≤15 °, 15 ° of ＞ and≤25 °, the ＞ of scope corresponding to five level of risk of the gradient
25 ° and≤35 °, 35 ° of ＞ and≤40 ° and 40 ° of ＞.
Preferably, type corresponding to five level of risk of the formation lithology is respectively loose ground intrusive rock, bulk
Metamorphic rock, clastic rock, flaked metamorphic rock and carbonate rock.
The beneficial effects of the invention are as follows：It is different from the situation of prior art, the cause calamity of the invention by determining landslide disaster
The factor, Flood inducing factors are divided into five level of risk according to the priority of degree of danger, and are each Flood inducing factors pair
The level of risk answered carries out assignment respectively from one to five, is then obtained using improvement BP Artificial Neural Networks per consistent calamity
The weight coefficient that factor pair landslide disaster liability influences, finally obtains summing value, by summing value according to weight coefficient and assignment
It is compared to determine liability rank with first threshold and Second Threshold, so as to carry out Regional Landslide disaster liability
Assess, strong foundation is provided for the prevention of landslide disaster.
Brief description of the drawings
Fig. 1 is the configuration diagram of Regional Landslide disaster assessment of easy generation device provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only the part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
It is the configuration diagram of Regional Landslide disaster assessment of easy generation device provided in an embodiment of the present invention refering to Fig. 1.Area
Domain landslide disaster assessment of easy generation device includes Flood inducing factors determining module 11, assignment module 12, training module 13, statistical module
14 and evaluation module 15.
Flood inducing factors determining module 11 is used to choose the cause of rainfall, landforms, the gradient and formation lithology as landslide disaster
The calamity factor, and each Flood inducing factors are divided into five level of risk according to the priority of degree of danger.Wherein, Flood inducing factors
Classification process be Flood inducing factors quantizing process, the quantizations of Flood inducing factors can use Delphi method, statistical analysis method, person in servitude
One of category degree function method and information Contents Method are carried out, the present invention preferably membership function method.
Assignment module 12 is used to level of risk corresponding to each Flood inducing factors carrying out assignment respectively from one to five, its
In, numerical value is bigger to represent more dangerous.Wherein, each Flood inducing factors are respectively divided into different ranks, and rank is higher to represent more dangerous.
Each level of risk correspondingly carries out assignment, then the numerical value of assignment is bigger represents more dangerous.In the present embodiment, level of risk
1 assignment numerical value is 1, the assignment numerical value of level of risk 2 is 2, the assignment numerical value of level of risk 3 is 3, level of risk 4
Assignment numerical value be 4, the assignment numerical value of level of risk 5 be 5.
Due to needing to carry out level of risk division to Flood inducing factors, then it needs to be determined that each dangerous level of Flood inducing factors
Not corresponding content, in the present embodiment, scope corresponding to five level of risk of rainfall be respectively≤200, ＞ 200 and
≤ 400, ＞ 400 and≤600, ＞ 600 and≤800 and ＞ 800, unit mm, it is specific as shown in table 1：
Type corresponding to five level of risk of landforms is respectively Plain, hills, high mountain, plateau and low mountain, specifically such as
Shown in table 2：
Scope corresponding to five level of risk of the gradient respectively≤15 °, 15 ° of ＞ and≤25 °, 25 ° of ＞ and≤35 °,
35 ° of ＞ and≤40 ° and 40 ° of ＞, it is specific as shown in table 3：
Type corresponding to five level of risk of formation lithology is respectively loose ground intrusive rock, block metamorphic rock, chip
Rock, flaked metamorphic rock and carbonate rock, it is specific as shown in table 3：
Training module 13 is used for using a large amount of district disasters statistics and rain making slope erosion result of the test as training sample,
Neutral net is trained using BP Artificial Neural Networks are improved, obtains each Flood inducing factors to landslide disaster liability
The weight coefficient of influence.Wherein, training module 13 using a large amount of district disasters count and rain making slope erosion result of the test as
Training sample, using BP Artificial Neural Networks, the weight vectors between the BP networks number of plies and equivalent layer are given, using improvement
BP network calculations program is trained to neutral net until reaching error requirements, and acquisition Flood inducing factors are safe with slope stability
The mapping relations of coefficient, and then determine the weight coefficient that each Flood inducing factors influence on landslide disaster liability.
Statistical module 14 is used to be multiplied to obtain with weight coefficient by the assignment of level of risk corresponding to each Flood inducing factors
Influence value.Where it is assumed that rainfall, landforms, the weight coefficient of the gradient and formation lithology are respectively 4,3,2,1, then rainfall
Five level of risk corresponding to influence value be respectively 0.4,0.8,1.2,1.6,2, five level of risk of landforms are corresponding
Influence value be respectively 0.3,0.6,0.9,1.2,1.5, influence value corresponding to five level of risk of the gradient is respectively 0.2,
0.4th, 0.6,0.8,1, influence value corresponding to five level of risk of formation lithology is respectively 0.1,0.2,0.3,0.4,0.5.
Evaluation module 15 is used to determine level of risk corresponding to the Flood inducing factors of target slopes, and by each Flood inducing factors
Level of risk corresponding to assignment numerical value summed to obtain summing value, liability rank is judged according to summing value, if asked
It is less than first threshold with value, then it is low to judge liability rank；If summing value is higher than first threshold and is less than Second Threshold,
In judging that liability rank is；If summing value is higher than Second Threshold, judge that liability rank is height.Wherein, first threshold
Weight coefficient can be combined with Second Threshold and actual conditions are set.It is still assumed that rainfall, landforms, the gradient and formation lithology
Weight coefficient is respectively 4,3,2,1, first threshold 2, Second Threshold 4.If that summing value ＜ 2, then judge liability
Rank is low, if 2≤summing value ＜ 4, in judging that liability rank is, if summing value >=4, judges liability rank
For height.
Wherein, the monitoring of rainfall can use rainfall monitoring meter to collect rainfall by rainfall bucket, and record rainfall prison
Data are surveyed, and then obtain actual rainfall.
For liability rank is neutralizes high slopes, landslide disaster prevention measure can be strengthened, reduce landslide calamity
Harmful probability of happening.
By the above-mentioned means, the Regional Landslide disaster assessment of easy generation device of the embodiment of the present invention is by determining landslide disaster
Flood inducing factors, Flood inducing factors are divided into five level of risk according to the priority of degree of danger, and be each cause calamity
Level of risk corresponding to the factor carries out assignment respectively from one to five, then every using the acquisition of BP Artificial Neural Networks is improved
The weight coefficient that consistent calamity factor pair landslide disaster liability influences, finally obtains summing value according to weight coefficient and assignment, will
Summing value is compared to determine liability rank with first threshold and Second Threshold, so as to easily send out Regional Landslide disaster
Property is assessed, and strong foundation is provided for the prevention of landslide disaster.
Embodiments of the invention are the foregoing is only, are not intended to limit the scope of the invention, it is every to utilize this hair
The equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (5)
- A kind of 1. Regional Landslide disaster assessment of easy generation device, it is characterised in that including：Flood inducing factors determining module, for choose rainfall, landforms, the gradient and formation lithology as landslide disaster cause calamity because Son, and each Flood inducing factors are divided into five level of risk according to the priority of degree of danger；Assignment module, for level of risk corresponding to each Flood inducing factors to be carried out into assignment respectively from one to five, wherein, Numerical value is bigger to represent more dangerous；Training module, for using a large amount of district disasters statistics and rain making slope erosion result of the test as training sample, using Improve BP Artificial Neural Networks to be trained neutral net, obtain each Flood inducing factors to landslide disaster liability The weight coefficient of influence；Statistical module, for being multiplied the assignment of level of risk corresponding to each Flood inducing factors with weight coefficient to obtain shadow Ring value；Evaluation module, for determining level of risk corresponding to the Flood inducing factors of target slopes, and by the danger of each Flood inducing factors Assignment numerical value is summed to obtain summing value corresponding to dangerous rank, liability rank is judged according to summing value, if summing value Less than first threshold, then it is low to judge liability rank；If summing value is higher than first threshold and is less than Second Threshold, judge During liability rank is；If summing value is higher than Second Threshold, judge that liability rank is height.
- 2. Regional Landslide disaster assessment of easy generation device according to claim 1, it is characterised in that the five of the rainfall Scope corresponding to individual level of risk is respectively≤200, ＞ 200 and≤400, ＞ 400 and≤600, ＞ 600 and≤800 and ＞ 800。
- 3. Regional Landslide disaster assessment of easy generation device according to claim 1, it is characterised in that five of the landforms Type corresponding to level of risk is respectively Plain, hills, high mountain, plateau and low mountain.
- 4. Regional Landslide disaster assessment of easy generation device according to claim 1, it is characterised in that five of the gradient Scope corresponding to level of risk respectively≤15 °, 15 ° of ＞ and≤25 °, 25 ° of ＞ and≤35 °, 35 ° of ＞ and≤40 ° and ＞ 40°。
- 5. Regional Landslide disaster assessment of easy generation device according to claim 1, it is characterised in that the formation lithology Type corresponding to five level of risk is respectively loose ground intrusive rock, block metamorphic rock, clastic rock, flaked metamorphic rock and carbonic acid Rock salt.
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| CN201710767482.7A CN107577913A (en) | 2017-08-31 | 2017-08-31 | A kind of Regional Landslide disaster assessment of easy generation device |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165424A (en) * | 2018-08-03 | 2019-01-08 | 四川理工学院 | A kind of landslide assessment of easy generation method based on domestic GF-1 satellite data |
| CN109359738A (en) * | 2018-10-19 | 2019-02-19 | 西南交通大学 | A kind of Landslide hazard appraisal procedure based on QPSO-BP neural network |
| CN110111377A (en) * | 2019-06-06 | 2019-08-09 | 西南交通大学 | A kind of shake rear region Landslide hazard appraisal procedure considering earthquake displacement field |
| CN110456845A (en) * | 2019-07-24 | 2019-11-15 | 西安西拓电气股份有限公司 | Data processing method and device |
| CN111160644A (en) * | 2019-12-27 | 2020-05-15 | 成都理工大学 | Railway route selection method and device based on geological disaster risk assessment |
| CN111768597A (en) * | 2020-06-22 | 2020-10-13 | 核工业西南勘察设计研究院有限公司 | Debris flow early warning protection method |
| CN113408201A (en) * | 2021-06-18 | 2021-09-17 | 河南大学 | Landslide susceptibility evaluation method based on terrain unit |
| CN114036841A (en) * | 2021-11-10 | 2022-02-11 | 云南大学 | Landslide incidence prediction method and system based on semi-supervised support vector machine model |
| CN114066165A (en) * | 2021-10-20 | 2022-02-18 | 国网黑龙江省电力有限公司电力科学研究院 | Improved power transmission line high-order landslide risk evaluation system and method |
| CN113343563B (en) * | 2021-05-27 | 2022-05-03 | 中交第二公路勘察设计研究院有限公司 | Landslide susceptibility evaluation method based on automatic sample selection and surface deformation rate |
| CN115331449A (en) * | 2022-10-17 | 2022-11-11 | 四川省公路规划勘察设计研究院有限公司 | Method and device for identifying accident prone area of long and large continuous longitudinal slope section and electronic equipment |
-
2017
- 2017-08-31 CN CN201710767482.7A patent/CN107577913A/en not_active Withdrawn
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165424A (en) * | 2018-08-03 | 2019-01-08 | 四川理工学院 | A kind of landslide assessment of easy generation method based on domestic GF-1 satellite data |
| CN109359738A (en) * | 2018-10-19 | 2019-02-19 | 西南交通大学 | A kind of Landslide hazard appraisal procedure based on QPSO-BP neural network |
| CN110111377A (en) * | 2019-06-06 | 2019-08-09 | 西南交通大学 | A kind of shake rear region Landslide hazard appraisal procedure considering earthquake displacement field |
| CN110456845A (en) * | 2019-07-24 | 2019-11-15 | 西安西拓电气股份有限公司 | Data processing method and device |
| CN111160644A (en) * | 2019-12-27 | 2020-05-15 | 成都理工大学 | Railway route selection method and device based on geological disaster risk assessment |
| CN111768597A (en) * | 2020-06-22 | 2020-10-13 | 核工业西南勘察设计研究院有限公司 | Debris flow early warning protection method |
| CN113343563B (en) * | 2021-05-27 | 2022-05-03 | 中交第二公路勘察设计研究院有限公司 | Landslide susceptibility evaluation method based on automatic sample selection and surface deformation rate |
| CN113408201A (en) * | 2021-06-18 | 2021-09-17 | 河南大学 | Landslide susceptibility evaluation method based on terrain unit |
| CN113408201B (en) * | 2021-06-18 | 2022-07-26 | 河南大学 | Landslide susceptibility evaluation method based on terrain unit |
| CN114066165A (en) * | 2021-10-20 | 2022-02-18 | 国网黑龙江省电力有限公司电力科学研究院 | Improved power transmission line high-order landslide risk evaluation system and method |
| CN114036841A (en) * | 2021-11-10 | 2022-02-11 | 云南大学 | Landslide incidence prediction method and system based on semi-supervised support vector machine model |
| CN115331449A (en) * | 2022-10-17 | 2022-11-11 | 四川省公路规划勘察设计研究院有限公司 | Method and device for identifying accident prone area of long and large continuous longitudinal slope section and electronic equipment |
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Application publication date: 20180112 |