CN109885977A - A kind of bank slope Deformation Prediction method - Google Patents
A kind of bank slope Deformation Prediction method Download PDFInfo
- Publication number
- CN109885977A CN109885977A CN201910227298.2A CN201910227298A CN109885977A CN 109885977 A CN109885977 A CN 109885977A CN 201910227298 A CN201910227298 A CN 201910227298A CN 109885977 A CN109885977 A CN 109885977A
- Authority
- CN
- China
- Prior art keywords
- displacement
- layer
- prediction
- term
- output
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
Abstract
The invention discloses a kind of bank slope Deformation Prediction methods, comprising the following steps: step 1: collecting displacement monitoring data, total displacement is decomposed into different response components by each factors influencing deformation action mode；Step 2: displacement time series decompose, and reject " unstable " ingredient of time series, i.e. the displacement of rejecting trend term；Step 3: being displaced with grey GM (l, 1) model extraction trend term, BP neural network model analysis is displaced by the bias that library water or rainfall induce, by the displacement superposed total displacement predicted value that can must come down of trend term and periodic term.The present invention has the advantages that by Landslide Deformation mechanism and based on the evolutionary phase, consider contribution of each risk factor to landslide displacement, establish the Grey Neural Network displacement prediction model based on more risk factors, the model is applicable to the Landslide Deformation influenced by exogenetic process factor prediction, simultaneously, not only it can realize the Medium-long Term Prediction for Landslide Deformation, but also be applicable to short-term and lin-gang new city.
Description
Technical field
The present invention relates to Landslide Prediction technical field, in particular to a kind of bank based on Grey Neural Network built-up pattern
Slope Deformation Prediction method.
Background technique
Due to landslide formation condition, the complexity of risk factor, diversity and its randomness of variation, so as to cause landslide
The extremely difficult capture of multidate information, the prediction on landslide are universally acknowledged problems.
Slide prediction method is roughly divided into experimental forecast method, statistical analysis forecasting procedure, nonlinear prediction method, physics
Mechanics forecasting procedure and Comprehensive prediction method.Wherein, statistical analysis forecasting procedure, which mainly passes through, introduces different statistical models, comes
It is fitted landslide displacement-time graph, main representative has grey GM (l, 1) model, biological growth model (Verhulst mould
Type), curvilinear regression analysis model, nonlinear multivariable relevant function method and exponential smoothing etc..However Landslide Deformation develops by all
More extraneous factors influence, such as rainfall, earthquake, the variation of library water, and existing statistical model such as grey GM (l, 1) model can not be anti-
Reflect displacement with extraneous factor changing rule, and the generation of displacement of inclined plane be control by slopes itself geologic structure condition and outside
Boundary's risk factor it is coefficient as a result, thus carried out by landslide displacement-time graph that traditional statistical model is fitted
Displacement prediction is unreasonable.Therefore, Prediction of Displacement in Landslide should consider each risk factor to landslide position based on deformation evolution mechanism
The Prediction of Displacement in Landslide model under more risk factor effects is established in the contribution of shifting, this is also theory and practice significance of the invention
Place.
The prior art one related to the present invention
Landslide System is a gray system, and gray system refers to INFORMATION OF INCOMPLETE or insufficient system[1].Grey
Systems Theory mainly by the generation to system " part " Given information, exploitation, the valuable information of extraction, is realized to system row
For correct understanding[2].Gray model is will likely be after irregular original deformation data Accumulating generation, so that it becomes relatively there is rule
Differential Equation Model is resettled after the generation ordered series of numbers of rule.So grey GM (1,1) model is actually generation series model.Cause
And the obtained data of GM (1,1) model must could use after inverse accumulated generating restores, GM (1,1) model is exponential model,
The deformation time series that can increase for index of coincidence are predicted.
If the displacement data ordered series of numbers for the time intervals such as original that come down are as follows:
X(0)=(x(0)(1),x(0)(2),…,x(0)(k)…,x(0)(n)) (k=1,2 ..., n)
In formula: n is the total number of monitoring data, and k is time serial number, x(0)(k) be the kth moment displacement monitoring data.
It utilizesTo X(0)Ordered series of numbers makees one-accumulate and generates (AGO) transformation:
X(1)=(x(1)(1),x(1)(2),…,x(1)(k)…,x(1)(n))
To x(1)Establish single order albinism differential equation[3]:
Undetermined coefficient a, b is solved using least square method:
Wherein:
Available GM (1,1) grey forecasting model are as follows:
Regressive (difference) operation is carried out to model value and obtains original series simulation and forecast value:
The shortcomings that prior art one
Currently with, there are apparent defect, being mainly manifested in: not in grey GM (1,1) model prediction bank slope Study on Deformation
The Prediction of Displacement in Landslide model established based on Landslide Deformation mechanism of Evolution is unreasonable；Do not consider bank slope displacement by extraneous factor
The prediction model that the rule of influence is established is unreasonable；It is unreasonable that prediction model is not established by the Landslide Deformation stage.Meanwhile ash
Application of color GM (1, the 1) model in Slip moinitoring predominantly carries out mid-and-long term forecasting to Landslide Deformation, but to cunning
Slope is short-term and to face sliding prediction precision poor, or even cannot be applicable in, therefore urgently improve to make and be both applicable to come down
The mid-and-long term forecasting of deformation, and can be adapted to short-term and face sliding prediction.
Bibliography
[1] Liu Sifeng, Guo Tianbang gray system theory and application break a seal: publishing house, He'nan University, and 1991；
[2] comparison application of Li Xiuzhen, Kong Jiming, Wang Chenghua grey GM (1,1) Residual Error Modified Model in Landslide Prediction
[J] mountain research, 2007,25 (6): 741-746；
[3] Wang Jianfeng Quantitative prediction of landslide using S-curve [J] Chinese Geological Disasters and prevention and treatment journal, 2003,14 (2):
1-8。
Summary of the invention
The present invention in view of the drawbacks of the prior art, provides a kind of bank slope Deformation Prediction method, can effectively solve above-mentioned
Problem of the existing technology.
In order to realize the above goal of the invention, the technical solution adopted by the present invention is as follows:
A kind of bank slope Deformation Prediction method, comprising the following steps:
Step 1: collecting bank slope displacement monitoring data, displacement total amount is decomposed into according to each factors influencing deformation action mode
Different response component, since the generation of displacement of inclined plane is to be controlled by slopes itself geologic structure condition and extraneous risk factor
It is coefficient as a result, displacement is divided into following four, and can be indicated with following model:
At=tt+ct+st+εt
In formula: tt、ct、st、εtRespectively deterministic trend term, periodic term, pulsation item and uncertain random change
Amount；
Step 2: displacement time series decompose.Resolution of displacement is carried out using moving average method, by the trend term in total displacement
Reject, it is a kind of data processing method of filtering, with linear multi moving average method be rely on, reject time series " no
Stablize " ingredient, find out sequences y1, y2... ynSeveral average values early period and later period average value, to construct a new sequence
Column, and this new sequence is smoother.Its mathematic(al) representation are as follows:
It is 3 sliding averages if k=1；K=2 is 5 sliding averages, and so on.F resulting in this waytRandom rise
Volt is reduced than original data, so that curve is more smooth, because of referred to herein as smoothed data.Meanwhile taking its residual error are as follows:
et=yt-ft
After sliding average, frequent random fluctuation in data is filtered, shows smooth variation tendency, the variation of random error
Process, the i.e. bias of trend term, thus landslide periodic term displacement is obtained, periodic term displacement is made jointly by multiple influence factors
Complex nonlinear sequence.
Step 3: with grey GM (l, 1) model extraction trend term be displaced, BP neural network model analysis by library water load or
The bias displacement that rainfall induces, finally, the total position in landslide can be obtained in trend term and the superposition of periodic term displacement prediction value
Move predicted value.
Further, steps are as follows for BP neural network model analysis bias displacement:
One typical BP neural network consists of three layers, i.e. input layer, hidden layer and output layer, realizes between each layer complete
Connection.Each layer is made of several neuron nodes, and the output valve of each neuron node is by input value, action function and threshold
Value determines that principle is as follows:
1) network inputs sample mode, is set are as follows:
Ai=(xi1,xi2,…,xin) (i=1,2 ..., m)
In formula: m is mode of learning logarithm, and n is input layer unit number.
2), corresponding output vector are as follows:
Bi=(yi1,yi2,…,yik) (i=1,2 ..., m)
In formula: m output mode number corresponding with input pattern, k are input layer unit number.
3) input of hidden layer each unit, is calculated:
Wherein: wijFor the continuous power of input layer to middle layer；θajFor the threshold value of implicit function unit, j is the nerve of hidden layer
First number.
4), with SjThe output of hidden layer each unit is calculated by lower array function as independent variable:
(S type function)
(tangsig function)
5), information flows to output layer from input layer, calculates input, the output of output layer unit:
Yi=f (Li)
In formula: YiFor the output of output layer, vjiFor middle layer to output layer connection weight；γiFor output layer unit threshold value；f
() is the function of S.
6), according to the connection weight and threshold value between the size automatic adjustment output layer of error, hidden layer and output layer, repeatedly
Training, until making the global error of network tend to minimum E.
After E, which is less than a certain precision of prediction, to be required, show that the network has been learnt well, so that it may according to new input value pair
Bank slope deformation is predicted.
Compared with prior art the present invention has the advantages that
Developing for Landslide Deformation is influenced (such as rainfall, earthquake, library water change) by many extraneous factors, solves grey
GM (1,1) statistical model can not reflect that displacement with the changing rule of extraneous factor, that is, considers each risk factor to landslide displacement
Contribution, is established Prediction of Displacement in Landslide model, and by the Landslide Deformation stage based on Landslide Deformation mechanism of Evolution with La Xiwa
For fruit foretells deformable body, the displacement of bank slope platform 1# section representativeness monitoring point is carried out using Prediction of Displacement in Landslide model pre-
It surveys, the results showed that using the GM of the displacement prediction built-up pattern set stage by stage to bank slope foundation based on bank slope deformation impact factor
The advantages of (1,1) model and BP neural network model, foretelling bank slope deformation more suitable for similar fruit is to have obvious deformation evolution rank
The displacement prediction of the deformed slope of section property.Meanwhile it is (i.e. grey based on grey GM (1,1) model after BP neural network model optimization
Color-neural network displacement prediction built-up pattern) it may be not only suitable for being influenced by outer power (rainfall, the variation of library water etc.) factor
Landslide Deformation prediction, and can realize the mid-and-long term forecasting and short-term forecast for Landslide Deformation.
Detailed description of the invention
Fig. 1 m- rate of displacement curve graph when being profiling observation point of the embodiment of the present invention；
Fig. 2 is Guo Bu bank slope of embodiment of the present invention area daily rain amount observational data curve graph；
Fig. 3 is the displacement prediction value and measured value curve graph in each water storage stage of the embodiment of the present invention；
Fig. 3 a is the first water storage periodical trend item displacement prediction and actual measurement；
Fig. 3 b is the first water storage stage bias displacement prediction and actual measurement；
Fig. 3 c is the second water storage periodical trend item displacement prediction and actual measurement；
Fig. 3 d is the second water storage stage bias displacement prediction and actual measurement；
Fig. 3 e is third water storage periodical trend item displacement prediction and actual measurement；
Fig. 3 f is third water storage stage bias displacement prediction and actual measurement；
Fig. 3 g is the 4th water storage periodical trend item displacement prediction and actual measurement；
Fig. 3 h is the 4th water storage stage bias displacement prediction and actual measurement.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, below in conjunction with attached drawing and embodiment is enumerated,
The present invention is described in further details.
A kind of bank slope Deformation Prediction method based on Grey Neural Network built-up pattern, which comprises
Step 1: collecting bank slope displacement monitoring data, displacement total amount is decomposed into according to each factors influencing deformation action mode
Different response component, since the generation of displacement of inclined plane is to be controlled by slopes itself geologic structure condition and extraneous risk factor
It is coefficient as a result, thus displacement can be divided into following four, and can be indicated with following model:
At=tt+ct+st+εtFormula 5
In formula: tt、ct、st、εtRespectively deterministic trend term, periodic term, pulsation item and uncertain random change
Amount.Trend term in displacement time series is usually the increasing function of time, is controlled by slopes itself geologic structure condition, drop
The other factors control periodic term displacement such as rain, reservoir level variation.Therefore, landslide displacement sequence can be expressed as trend term and periodic term position
The sum of move.
Step 2: displacement time series decompose.Resolution of displacement is carried out using moving average method, by the trend term in total displacement
Reject, it is a kind of data processing method of filtering, with linear multi moving average method be rely on, reject time series " no
Stablize " ingredient, find out sequences y1, y2... ynSeveral average values early period and later period average value, to construct a new sequence
Column, and this new sequence is smoother.Its mathematic(al) representation are as follows:
It is 3 sliding averages if k=1；K=2 is 5 sliding averages, and so on.F resulting in this waytRandom rise
Volt is reduced than original data, so that curve is more smooth, because of referred to herein as smoothed data.Meanwhile taking its residual error are as follows:
et=yt-ftFormula 7
After sliding average, frequent random fluctuation in data is filtered, shows smooth variation tendency, the variation of random error
Process, the i.e. bias of trend term, thus landslide periodic term displacement is obtained, periodic term displacement is made jointly by multiple influence factors
Complex nonlinear sequence.
Step 3: with grey GM (l, 1) model extraction trend term be displaced, BP neural network model analysis by library water load or
The bias displacement that rainfall induces, finally, the total position in landslide can be obtained in trend term and the superposition of periodic term displacement prediction value
Move predicted value.
Further, steps are as follows for BP neural network model analysis bias displacement:
One typical BP neural network consists of three layers, i.e. input layer, hidden layer and output layer, realizes between each layer complete
Connection.Each layer is made of several neuron nodes, and the output valve of each neuron node is by input value, action function and threshold
Value determines that principle is as follows:
1) network inputs sample mode, is set are as follows:
Ai=(xi1,xi2,…,xin) (i=1,2 ..., m) formula 8
In formula: m is mode of learning logarithm, and n is input layer unit number.
2), corresponding output vector are as follows:
Bi=(yi1,yi2,…,yik) (i=1,2 ..., m) formula 9
In formula: m output mode number corresponding with input pattern, k are input layer unit number.
3) input of hidden layer each unit, is calculated:
Wherein: wijFor the continuous power of input layer to middle layer；θajFor the threshold value of implicit function unit, j is the nerve of hidden layer
First number.
4), with SjThe output of hidden layer each unit is calculated by lower array function as independent variable:
5), information flows to output layer from input layer, calculates input, the output of output layer unit:
Yi=f (Li) formula 14
In formula: YiFor the output of output layer, vjiFor middle layer to output layer connection weight；γiFor output layer unit threshold value；f
() is the function of S.
6), according to the connection weight and threshold value between the size automatic adjustment output layer of error, hidden layer and output layer, repeatedly
Training, until making the global error of network tend to minimum E.
After E, which is less than a certain precision of prediction, to be required, show that the network has been learnt well, so that it may according to new input value pair
Bank slope deformation is predicted.
Application example of the present invention is as follows:
By taking the power station La Xiwa fruit foretells deformable body as an example, fruit foretells bank slope rate of displacement and reservoir level correlation (figure
1), i.e. displacement bias is mainly influenced by reservoir level, since monitoring in addition to there is a small amount of rainfall in the summer of, in 2009 in 2012
(Fig. 2), bank slope area totality rainfall is smaller, therefore, uses moving average method by resolution of displacement for trend term incremental at any time
Displacement and the bias displacement induced by the load of library water, rainfall, using the variable quantity of reservoir level as BP neural network model
The main input factor, rainfall as the secondary input factor, consider day Reservoir Water Level amount, moon reservoir level accumulated change respectively
Amount, daily rainfall and moon accumulation rainfall foretell bank slope displacement bias to fruit as the input impact factor of BP neural network model
It is analyzed, establishes the bank slope of reservoir Deformation Prediction in each water storage stage based on more impact factors such as reservoir level, rainfall
Grey Neural Network built-up pattern.
To on July 15th, 2014 from monitoring on August 15th, 2009, comprehensive displacement accumulation curve and rate of displacement curve are special
It was in four stages that sign, which shows bank slope Displacement Development obviously:
First stage: " rate of displacement obviously increases the stage ", the displacement curve angle of contingence are in increased trend
(2009.08.15-2010.2.22 water storage level is raised to 2420m for the first time, then falls after rise to 2400m)；
Second stage: " rate of displacement first reduces the stage ", an opposite stage deform, and rate of displacement is in decreasing trend
(2010.2.23-2011.4.28 water storage level is stablized after being raised to 2430m from 2400m to 2430m)；
Phase III: " rate of displacement second reduces the stage ", (2011.4.29-2012.12.30 water storage level is lifted from 2430m
Stablize after rising to 2448m to 2448m), rate of displacement is generally less than 10mm/d；
Fourth stage: " rate of displacement tends towards stability the stage ", rate of displacement are generally less than (1~2) mm/d (2013.1.1-
2014.7.15 water storage level is stablized to 2448m), and rate of displacement tends towards stability.
Below by taking No. 79 points as an example, bank slope displacement time series are divided into the above four-stage, establish based on reservoir level,
The Grey Neural Network built-up pattern of the bank slope of reservoir Deformation Prediction in each water storage stage of more impact factors such as rainfall:
Trend item parts and deviation are decomposed into using displacement time series of the moving average method to the aforementioned four water storage stage
Part is measured, using GM (1,1) models fitting trend term, by day Reservoir Water Level amount, moon reservoir level accumulated change amount, daily rainfall
And moon accumulation rainfall is fitted as the input factor pair displacement bias of BP neural network model, bank slope 2009 9
The displacement measured value and predicted value result such as Fig. 3 in the moon~in November, 2013.
Displacement measured value shows the model using BP neural network training with predicted value comparison result, to training data
Fitting degree it is high, except GM (1, l) model is poor to the data fitting degree in the first water storage stage outer, generally GM (1, l) mould
The predicted value error of type and BP neural network model is lower than 10%, and displacement prediction curve is consistent with measured curve trend, fitting effect
Fruit is good, therefore, using the displacement prediction built-up pattern set stage by stage to bank slope foundation based on deformation impact factor GM (1,1)
The advantages of model and BP neural network model, foretelling bank slope deformation more suitable for similar fruit has obvious deformation evolution interim
The displacement prediction of deformed slope.
According to monitoring data of displacement, analysis shows, bank slope rate of deformation tends towards stability at present, most of monitoring point rate of displacement
Less than 1~2mm/d, bank slope deformation is in the opposite stabilization sub stage, and therefore, fourth stage displacement prediction built-up pattern is suitable for 2014
Bank slope Deformation Prediction since year, with the displacement in the 4th water storage stage (01 month~in November, 2013 in 2013), Reservoir Water Level amount
And the trained BP neural network model of rainfall product data is the prediction model of bias part, chooses the forecast sample time below
Section is 01 month~in June, 2014 in 2014, using the accumulative displacement-time for GM (l, 1) this time of model prediction established
The trend term of sequence, GM (l, 1) model of No. 79 monitoring point fourth stages are as follows:
To accumulate day Reservoir Water Level amount, moon reservoir level accumulated change amount, daily rainfall and the moon rainfall as BP nerve
The input factor of network model is predicted the bias of accumulative displacement-time series using BP model has been trained, most
The trend term of prediction is added to the total displacement amount up to prediction with bias afterwards.Based on the Grey Neural Network combination established
The displacement prediction result such as table 1 of the bank slope platform 1# section representativeness monitoring point of model prediction.As shown in Table 1, with predicted time
Increase, prediction error is gradually increased, and the prediction effect of in January, 2014~3 month section is relatively preferable.Since bank slope becomes at this stage
Shape rate totally tends towards stability, and prediction technique proposed by the present invention, the selection number of roller can be used in later period bank slope displacement prediction
According to prediction model is established, displacement prediction accuracy is higher.
Table 1 is displaced prediction result (part) (position based on the platform 1# profile monitoring point of Grey Neural Network built-up pattern
It moves: mm)
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright implementation method, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.Ability
The those of ordinary skill in domain disclosed the technical disclosures can make its various for not departing from essence of the invention according to the present invention
Its various specific variations and combinations, these variations and combinations are still within the scope of the present invention.
Claims (2)
1. a kind of bank slope Deformation Prediction method, which comprises the following steps:
Step 1: collecting bank slope displacement monitoring data, displacement total amount is decomposed into difference according to each factors influencing deformation action mode
Response component, since the generation of displacement of inclined plane is to be controlled by slopes itself geologic structure condition and extraneous risk factor is common
Effect as a result, displacement is divided into following four, and can be indicated with following model:
At=tt+ct+st+εt
In formula: tt、ct、st、εtRespectively deterministic trend term, periodic term, pulsation item and uncertain stochastic variable；
Step 2: displacement time series decompose；Resolution of displacement is carried out using moving average method, the trend term in total displacement is rejected,
It is a kind of data processing method of filtering, is to rely on linear multi moving average method, rejects " unstable " of time series
Ingredient finds out sequences y1, y2... ynSeveral average values early period and later period average value, to construct a new sequence, and
This new sequence is smoother；Its mathematic(al) representation are as follows:
It is 3 sliding averages if k=1；K=2 is 5 sliding averages, and so on；F resulting in this waytRandom fluctuation ratio
Originally data reduce, so that curve is more smooth, because of referred to herein as smoothed data；Meanwhile taking its residual error are as follows:
et=yt-ft
After sliding average, frequent random fluctuation in data is filtered, shows smooth variation tendency, the variation of random error
Journey, the i.e. bias of trend term, thus landslide periodic term displacement is obtained, periodic term displacement is by multiple influence factor collective effects
Complex nonlinear sequence；
Step 3: being displaced with grey GM (l, 1) model extraction trend term, BP neural network model analysis is by the load of library water or rainfall
The bias displacement that effect induces, finally, it is pre- that landslide total displacement can be obtained in trend term and the superposition of periodic term displacement prediction value
Measured value.
2. according to the method described in claim 1, it is characterized by: being loaded using the BP neural network model analysis by library water
Or the bias displacement that rainfall induces, the specific steps of which are as follows:
One typical BP neural network consists of three layers, i.e. input layer, hidden layer and output layer, realizes between each layer and connects entirely
It connects；Each layer is made of several neuron nodes, and the output valve of each neuron node is by input value, action function and threshold value
It determines, principle is as follows:
1) network inputs sample mode, is set are as follows:
Ai=(xi1,xi2,…,xin) (i=1,2 ..., m)
In formula: m is mode of learning logarithm, and n is input layer unit number；
2), corresponding output vector are as follows:
Bi=(yi1,yi2,…,yik) (i=1,2 ..., m)
In formula: m output mode number corresponding with input pattern, k are input layer unit number；
3) input of hidden layer each unit, is calculated:
Wherein: wijFor the continuous power of input layer to middle layer；θajFor the threshold value of implicit function unit, j is the neuron number of hidden layer；
4), with SjThe output of hidden layer each unit is calculated by lower array function as independent variable:
(S type function)
(tangsig function)
5), information flows to output layer from input layer, calculates input, the output of output layer unit:
Yi=f (Li)
In formula: YiFor the output of output layer, vjiFor middle layer to output layer connection weight；γiFor output layer unit threshold value；F () is S
Function；
6) it, according to the connection weight and threshold value between the size automatic adjustment output layer of error, hidden layer and output layer, instructs repeatedly
Practice, until making the global error of network tend to minimum E；
After E, which is less than a certain precision of prediction, to be required, show that the network has been learnt well, so that it may according to new input value to bank slope
Deformation is predicted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227298.2A CN109885977A (en) | 2019-03-25 | 2019-03-25 | A kind of bank slope Deformation Prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227298.2A CN109885977A (en) | 2019-03-25 | 2019-03-25 | A kind of bank slope Deformation Prediction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109885977A true CN109885977A (en) | 2019-06-14 |
Family
ID=66934067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910227298.2A Pending CN109885977A (en) | 2019-03-25 | 2019-03-25 | A kind of bank slope Deformation Prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109885977A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110824142A (en) * | 2019-11-13 | 2020-02-21 | 杭州鲁尔物联科技有限公司 | Geological disaster prediction method, device and equipment |
| CN111199313A (en) * | 2019-12-26 | 2020-05-26 | 航天信息股份有限公司 | Method and system for predicting landslide accumulated displacement trend based on neural network |
| CN112101660A (en) * | 2020-09-15 | 2020-12-18 | 重庆交通大学 | Rainfall type landslide displacement prediction model and method based on staged attention mechanism |
| CN112270229A (en) * | 2020-10-16 | 2021-01-26 | 西安工程大学 | Landslide mass displacement prediction method based on singular spectrum analysis |
| CN113627066A (en) * | 2021-08-03 | 2021-11-09 | 成都理工大学 | Displacement prediction method for reservoir bank landslide |
| CN114001703A (en) * | 2021-10-09 | 2022-02-01 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
| CN114201923A (en) * | 2022-02-16 | 2022-03-18 | 中国地质调查局水文地质环境地质调查中心 | Processing method and device of landslide displacement prediction model |
| CN118070049A (en) * | 2024-04-22 | 2024-05-24 | 成都理工大学 | Step type landslide displacement prediction method and system under mechanism guidance |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108537360A (en) * | 2018-03-01 | 2018-09-14 | 长安大学 | One kind taking polyfactorial adaptable Kalman filter slide prediction method into account |
| CN108846199A (en) * | 2018-06-12 | 2018-11-20 | 华能澜沧江水电股份有限公司 | Ultra-high arch dam deformation space-time sequence prediction method based on space-time integration |
-
2019
- 2019-03-25 CN CN201910227298.2A patent/CN109885977A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108537360A (en) * | 2018-03-01 | 2018-09-14 | 长安大学 | One kind taking polyfactorial adaptable Kalman filter slide prediction method into account |
| CN108846199A (en) * | 2018-06-12 | 2018-11-20 | 华能澜沧江水电股份有限公司 | Ultra-high arch dam deformation space-time sequence prediction method based on space-time integration |
Non-Patent Citations (4)
| Title |
|---|
| 刘向立: "基于非平稳性和小波分析的水文序列趋势识别", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 杜娟 等: "基于诱发因素响应分析的滑坡位移预测模型研究", 《岩石力学与工程学报》 * |
| 许霄霄: "三峡库区秭归—巴东段顺层滑坡变形规律研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 邢林啸: "三峡库区典型堆积层滑坡成因机制与预测预报研究——以白水河滑坡为例", 《中国优秀博硕士学位论文全文数据库(硕士)基础科学辑》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110824142A (en) * | 2019-11-13 | 2020-02-21 | 杭州鲁尔物联科技有限公司 | Geological disaster prediction method, device and equipment |
| CN110824142B (en) * | 2019-11-13 | 2022-06-24 | 杭州鲁尔物联科技有限公司 | Geological disaster prediction method, device and equipment |
| CN111199313A (en) * | 2019-12-26 | 2020-05-26 | 航天信息股份有限公司 | Method and system for predicting landslide accumulated displacement trend based on neural network |
| CN112101660A (en) * | 2020-09-15 | 2020-12-18 | 重庆交通大学 | Rainfall type landslide displacement prediction model and method based on staged attention mechanism |
| CN112101660B (en) * | 2020-09-15 | 2022-06-28 | 重庆交通大学 | Rainfall type landslide displacement prediction model and method based on staged attention mechanism |
| CN112270229A (en) * | 2020-10-16 | 2021-01-26 | 西安工程大学 | Landslide mass displacement prediction method based on singular spectrum analysis |
| CN113627066A (en) * | 2021-08-03 | 2021-11-09 | 成都理工大学 | Displacement prediction method for reservoir bank landslide |
| CN114001703A (en) * | 2021-10-09 | 2022-02-01 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
| CN114001703B (en) * | 2021-10-09 | 2023-07-28 | 四川轻化工大学 | Landslide deformation data real-time filtering method |
| CN114201923A (en) * | 2022-02-16 | 2022-03-18 | 中国地质调查局水文地质环境地质调查中心 | Processing method and device of landslide displacement prediction model |
| CN118070049A (en) * | 2024-04-22 | 2024-05-24 | 成都理工大学 | Step type landslide displacement prediction method and system under mechanism guidance |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109885977A (en) | A kind of bank slope Deformation Prediction method | |
| CN111144542B (en) | Oil well productivity prediction method, device and equipment | |
| Solgi et al. | Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations | |
| Ozer et al. | A combined deep learning application for short term load forecasting | |
| Kişi | River suspended sediment concentration modeling using a neural differential evolution approach | |
| Wei et al. | Predicting injection profiles using ANFIS | |
| Movagharnejad et al. | Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network | |
| CN105679022B (en) | A kind of complementing method of the multi-source traffic data based on low-rank | |
| CN108009674A (en) | Air PM2.5 concentration prediction methods based on CNN and LSTM fused neural networks | |
| CN105427138A (en) | Neural network model-based product market share analysis method and system | |
| CN106408120B (en) | Local area landslide prediction device and method | |
| Kişi | Evolutionary fuzzy models for river suspended sediment concentration estimation | |
| Lin et al. | A novel dynamic progress forecasting approach for construction projects | |
| CN112699572A (en) | Method for predicting landslide deformation based on time-lag correlation analysis | |
| CN106529185B (en) | A kind of combination forecasting method and system of ancient building displacement | |
| CN111815075B (en) | Prediction method for transportation travel demand under major public health incident | |
| CN114548482A (en) | Creep type landslide kinetic energy change rate face-slip early warning method | |
| Rodriguez et al. | Methodology for the prediction of fluid production in the waterflooding process based on multivariate long–short term memory neural networks | |
| CN112539054A (en) | Production optimization method for ground pipe network and underground oil reservoir complex system | |
| CN115755219A (en) | Flood forecast error real-time correction method and system based on STGCN | |
| Rizal et al. | Sequence to sequence analysis with long short term memory for tourist arrivals prediction | |
| CN118333129B (en) | Identification model training method, nonlinear system identification method and nonlinear system identification system | |
| CN107704944B (en) | Construction method of stock market fluctuation interval prediction model based on information theory learning | |
| Behl et al. | Data-Driven Reduced-Order Models for Volve Field Using Reservoir Simulation and Physics-Informed Machine Learning Techniques | |
| Zare et al. | Using ANN and ANFIS Models for simulating and predicting groundwater level fluctuations in the Miandarband Plain, Iran |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190614 |
|
| RJ01 | Rejection of invention patent application after publication |