CN110703359A - Hysteresis nonlinear time forecasting method for landslide forecasting by taking rainfall as main part - Google Patents

Hysteresis nonlinear time forecasting method for landslide forecasting by taking rainfall as main part Download PDF

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Publication number
CN110703359A
CN110703359A CN201910982736.6A CN201910982736A CN110703359A CN 110703359 A CN110703359 A CN 110703359A CN 201910982736 A CN201910982736 A CN 201910982736A CN 110703359 A CN110703359 A CN 110703359A
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rainfall
soil
landslide
forecasting
water level
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谢婉丽
杨惠
李永红
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Northwest University
Northwestern University
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Northwest University
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01WMETEOROLOGY
    • G01W1/00Meteorology
    • G01W1/14Rainfall or precipitation gauges
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01FMEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F23/00Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24Earth materials
    • G01N33/246Earth materials for water content
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a lag nonlinear time forecasting method for landslide forecasting by mainly taking rainfall as a main method, which comprises the following steps: the method comprises the following steps: analyzing the correlation of rainfall with the soil shallow layer water content and the underground water level to obtain the correlation and the functional relation; step two: constructing a nonlinear model of rainfall, soil water content and underground water level based on the relation; step three: and analyzing the influence of rainfall change on the soil moisture content and the underground water level, judging the lag period of the rainfall change, and finally obtaining the lag nonlinear time forecasting method for forecasting landslide mainly based on rainfall. By the method, the rainfall data of the monitoring station is collected, meanwhile, the soil moisture content and the underground water level can be predicted, the lag period of influence of the rainfall can be judged, the landslide still needs to be closely monitored by the Internet of things monitoring and early warning technology in the lag period, and once the rainfall data are obviously changed, relevant departments can timely take emergency measures.

Description

Hysteresis nonlinear time forecasting method for landslide forecasting by taking rainfall as main part
Technical Field
The invention relates to the field of geological disaster monitoring and early warning, in particular to a lag nonlinear time forecasting method for landslide forecasting mainly based on rainfall.
Background
Landslide disasters are greatly related to rainfall, not only related to the amount of rainfall on the same day, but also possibly affected by the previous day or even days.
In the Qinba mountain area, strong rainfall is the main cause for inducing geological disasters, and the rainfall is an important inducing factor for forming the geological disasters and mainly shows three aspects: (1) the water content and the self weight of the rock-soil body are increased, so that the shear strength of the rock-soil body is reduced and the loading is unbalanced; (2) rainwater seeps down to the top surface of the bedrock to be collected in a water-proof manner, so that the soft bedrock surface is softened, a lubricating effect is achieved, and an easy-to-slide surface is formed; (3) the underground water level is raised quickly, the undercurrent speed is increased, the hydrodynamic pressure is increased, and the gliding force is enhanced. According to the geological disaster space-time distribution rule and the development characteristics in the Qinba mountain area, the time for obtaining the occurrence or deformation of the geological disaster and the hidden danger points of the geological disaster is concentrated in 5-10 months, so that the occurrence of the geological disaster in the Qinba mountain area is directly related to abundant rainfall and large rainfall in the flood season. From the perspective of annual changes, an oversize geological disaster occurs every 3-5 years, which is mainly influenced by the annual changes of precipitation. Geological disasters are diverse, sudden, concentrated, catenated and periodic.
The technology of the Internet of things is adopted to carry out multi-directional real-time monitoring, and the relation between the soil moisture content and the underground water level and the self characteristics of the soil, the rainfall and the rainfall duration is found to be tight, so that the three are selected for correlation analysis.
At present, most of landslide forecasting models are constructed by considering the influence of real-time or monthly displacement of rainfall, but the occurrence of actual landslide is not only related to the instant rainfall, but also related to the influence of accumulated rainfall on the soil moisture content and the underground water level, and the influence of the rainfall on the soil moisture content and the underground water level has certain hysteresis quality and persistence, so that the hysteresis cycle can be deduced through the method, and the aims of accurate monitoring and accurate forecasting are fulfilled.
Disclosure of Invention
The present invention is directed to provide a method for predicting a non-linear time lag with landslide prediction based on rainfall, so as to solve the problems of the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a lag nonlinear time forecasting method for landslide forecasting mainly based on rainfall comprises the following steps:
analyzing the correlation between rainfall and the shallow layer water content and the underground water level of soil, carrying out theoretical analysis on data collected by a Wangchan landslide monitoring station of geological disasters in the Qinba mountain area, selecting monitoring data of three months (8, 9 and 10) in a flood season as a research object, fitting the monitoring data comprising the rainfall, the shallow layer soil water content (50cm) and underground water bit data, and analyzing the correlation to obtain the correlation and the functional relation;
step two: constructing a nonlinear model of rainfall, soil water content and underground water level based on the relation;
step three: and analyzing the influence of rainfall change on the soil moisture content and the underground water level, wherein the soil moisture content and the underground water level are increased along with the increase of the rainfall. The larger the rainfall intensity is, the more the rainfall infiltrates to replenish the underground water, the faster the infiltration is, and the more obvious the change of the soil moisture content is; when the rainfall is not obviously changed, the underground water level is also kept in a stable state and does not fluctuate greatly, and the underground water level is obviously reduced in 9 and 10 months, which is related to the irrigation of crops in the beginning of autumn and winter in the local, so that the lag period of the underground water level is judged according to the time when the underground water level starts to change, and finally, a lag nonlinear time forecasting method for forecasting the landslide mainly based on the rainfall is obtained, and the landslide is forecasted.
As a further scheme of the invention: in the first step, when the correlation between rainfall and soil shallow water content and underground water level is analyzed, monitoring data of seasons with large rainfall and abundant rainwater in a flood season are selected, representative data are obtained, rainfall infiltration and evaporation effects are considered, and soil water content monitoring data with 50cm buried depth are selected for analysis so as to guarantee the reliability of the soil water content data.
As a still further scheme of the invention: the nonlinear model constructed in the second step is as follows:
Yi=(∝1,...,∝j)ln(Xi1,Xi2,...,Xik)+βi(1)
in the formula: y isiDependent variable of soil moisture content and underground water level
(∝1,...,∝j) -model parameter-slope
Xi1,Xi2,...,XikRainfall- -independent variable
βiDisturbance parameter- -intercept
ln(Xi1,Xi2,...,Xik) -a non-linear function.
As a still further scheme of the invention: in the third step, the lag period can be judged according to the influence change of rainfall on the soil moisture content and the underground water level change.
As a still further scheme of the invention: the basic data analyzed in the step one is real-time data acquired by a monitoring system adopting the technology of the Internet of things.
As a still further scheme of the invention: the lag period obtained according to the final judgment in the third step can be applied to actual forecasting, and if the landslide does not occur but is still in the lag period, the threat of the landslide cannot be eliminated.
As a still further scheme of the invention: and in the third step, the landslide forecast is realized on the basis of real-time networking analysis, and real-time data real-time analysis can be realized.
Compared with the prior art, the invention has the beneficial effects that: a non-linear model of rainfall, soil moisture content and underground water level is constructed through data analysis of monitoring stations of typical geological disaster points in the flood season, the moisture content and the underground water level can be monitored, analyzed and predicted in real time while the rainfall data of the monitoring stations are collected, and once the rainfall data of the monitoring stations are obviously changed, the rainfall data can be uploaded to a client in time to inform relevant departments of taking emergency measures
Drawings
FIG. 1 is a graph showing the correlation between the rainfall at 8 months of the present invention, the shallow water content of soil, and the groundwater level;
FIG. 2 is a graph showing the correlation analysis of the rainfall at 9 months, the water content of the soil shallow layer and the groundwater level;
FIG. 3 is a graph showing the correlation between the rainfall at 10 months, the shallow water content of soil and the groundwater level;
FIG. 4 is a graph of the effect of 8-month rainfall on soil moisture content, groundwater level, of the present invention;
FIG. 5 is a graph of the effect of 9-month rainfall on soil moisture content, groundwater level, of the present invention;
FIG. 6 is a graph showing the effect of rainfall on soil moisture content and groundwater level in the 10 th month period of the present invention.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to specific embodiments.
Example 1
A lag nonlinear time forecasting method for landslide forecasting mainly based on rainfall comprises the following steps:
the method comprises the following steps: analyzing the correlation of rainfall with the soil shallow layer water content and the underground water level to obtain the correlation and the functional relation;
step two: constructing a nonlinear model of rainfall, soil water content and underground water level based on the relation;
step three: and analyzing the influence of rainfall change on the soil moisture content and the underground water level, judging the lag period of the rainfall change, and finally obtaining the lag nonlinear time forecasting method for forecasting landslide mainly based on rainfall.
In the first step, when the correlation between rainfall and soil shallow water content and underground water level is analyzed, monitoring data of seasons with large rainfall and abundant rainwater in a flood season are selected, representative data are obtained, rainfall infiltration and evaporation effects are considered, and soil water content monitoring data with 50cm buried depth are selected for analysis so as to guarantee the reliability of the soil water content data.
The nonlinear model constructed in the second step is as follows:
Yi=(∝1,...,∝j)ln(Xi1,Xi2,...,Xik)+βi(1)
in the formula: y isiDependent variable of soil moisture content and underground water level
(∝1,...,aj) -model parameter-slope
Xi1,Xi2,...,XikRainfall- -independent variable
βiDisturbance parameter- -intercept
ln(Xi1,Xi2,...,Xik) -a non-linear function.
In the third step, the lag period can be judged according to the influence change of rainfall on the soil moisture content and the underground water level change.
The basic data analyzed in the step one is real-time data acquired by a monitoring system adopting the technology of the Internet of things.
The lag period obtained according to the final judgment in the third step can be applied to actual forecasting, and if the landslide does not occur but is still in the lag period, the threat of the landslide cannot be eliminated.
And in the third step, the landslide forecast is realized on the basis of real-time networking analysis, and real-time data real-time analysis can be realized.
Example 2
A lag nonlinear time forecasting method for landslide forecasting mainly based on rainfall comprises the following steps:
the method comprises the following steps: according to the space-time distribution rule and the development characteristics of geological disasters in the Qinba mountain area, the occurrence or deformation time of the geological disasters and hidden danger points is centralized for 5-10 months.
Step two: after the data of the specific monitoring station are analyzed, the fact that the water content of shallow soil is closely related to the self characteristics of the soil, rainfall and rainfall duration is obtained, the water content of the soil is higher in 8-10 months, and the relation is related to large rainfall and abundant rainwater in the Qinba mountain area in the flood season, and therefore the monitoring data of the monitoring station in 8-10 months are selected for analysis.
Step three: and fitting the data of the selected monitoring station, considering the influence of rainfall infiltration and evaporation, selecting soil moisture content monitoring data with the buried depth of 50cm in order to ensure the reliability of the soil moisture content data, and analyzing the correlation of rainfall with the shallow soil moisture content and the underground water level.
Step four: through analyzing the correlation of the three, the soil moisture content and the rainfall are in a logarithmic relation, the soil moisture content rises along with the increase of the rainfall and reaches saturation basically at about 22 percent, the average correlation coefficient is 0.94, and the correlation is strong; the rainfall and the underground water level have the same nonlinear logarithmic relation, and an obvious lag period exists, which is related to strong evaporation in 8-10 months, long rainfall infiltration time, average correlation coefficient of three months as high as 0.9, and strong correlation. Therefore, the three have high correlation and are in a nonlinear relationship.
Step five: based on the analysis, a nonlinear model of rainfall and soil moisture content can be basically constructed, as shown in the following formula:
Yi=(∝1,...,∝j)ln(Xi1,Xi2,...,Xik)+βi(1)
in the formula: y isiDependent variable of soil moisture content and underground water level
(∝1,...,∝j) -model parameter-slope
Xi1,Xi2,...,XikRainfall- -independent variable
βiDisturbance parameter- -intercept
ln(Xi1,Xi2,...,Xik) -a non-linear function.
Step six: and substituting the rest month data into the model for calculation to obtain that the soil moisture content and the underground water level increase along with the increase of rainfall, wherein the rainfall intensity is higher, the rainfall infiltration supplies more underground water, the infiltration is faster, and the change of the soil moisture content is more obvious.
Step seven: the analysis data of the three are compared, so that the delay period of the rainfall affecting the soil moisture content and the underground water level change does not exceed 24h, and when the rainfall increases, the soil moisture content and the underground water level obviously increase within 24h, so that the influence of the rainfall on the soil moisture content (50cm) and the underground water level has a delay relation, and the delay period is within 24 h.
Example 3
A lag nonlinear time forecasting method for landslide forecasting by taking rainfall as a main method comprises the following steps:
basic data are data collected by a geological disaster Wangman landslide monitoring station in the Qinba mountain area, theoretical analysis is carried out on the data in the early stage, monitoring data of three months (8, 9 and 10) in the flood season are selected as research objects, the monitoring data comprise rainfall, shallow soil moisture content (50cm) and underground water data, the data are fitted, and a correlation function is obtained:
y=AlnX+B
wherein: A. b is a fitting parameter;
and respectively generating correlation analysis graphs of rainfall, the shallow layer water content of the soil and the underground water level in 8 months, 9 months and 10 months according to the fitting result.
It can be clearly seen from fig. 1-3 that the soil moisture content and the rainfall are in a logarithmic relationship, the soil moisture content rises along with the increase of the rainfall, and basically reaches saturation at about 22%, and the average correlation coefficient is 0.94, which shows that 94% of data are correlated and have strong correlation. The change of rainfall has direct influence on the underground water level, the important source of underground water supply is rainfall infiltration, the nonlinear logarithmic relation of the rainfall and the underground water level can be seen through the graphs 1-3, a more obvious lagging process exists, the relation is related to strong evaporation in 8-10 months and long rainfall infiltration time, the average correlation coefficient of the rainfall and the underground water level in three months is up to 0.9, the correlation is strong, and therefore the change of the rainfall, the shallow soil water content and the underground water level are high in correlation and are in nonlinear relation.
Based on the analysis, a nonlinear model of rainfall, soil water content and underground water level is constructed:
Yi=(∝1,...,∝j)ln(Xi1,Xi2,...,Xik)+βi
it can be seen from fig. 4-6 that the soil moisture content and the groundwater level increase with the increase of rainfall, the rainfall intensity is higher, the rainfall infiltration supplies groundwater more, the infiltration is faster, and the change of the soil moisture content is more obvious; when the rainfall is not obviously changed, the underground water level is also kept in a stable state without large fluctuation, and the underground water level is obviously reduced in 9 and 10 months, which is related to the irrigation of crops in the beginning of the local autumn, winter.
Comparing fig. 1-3 and fig. 4-6, the delay period of the rainfall affecting the soil moisture content and the groundwater level change does not exceed 24h, and when the rainfall increases, the soil moisture content and the groundwater level have a significant rise within 24h, so that the delay relationship exists between the rainfall, the soil moisture content (50cm) and the groundwater level, and the period is within 24 h.
In conclusion, the invention provides a hysteresis nonlinear time forecasting method for landslide forecasting mainly based on rainfall, which analyzes the relation between the underground water level and the moisture content of the shallow soil layer along with the rainfall and the time change, and considers the hysteresis effect and the nonlinear effect generated by the change of the underground water level and the moisture content of the shallow soil layer along with the rainfall in time, so that the hysteresis cycle of the influence of the rainfall on the moisture content of the shallow soil layer and the underground water level is within 24h, therefore, within 24h after rainfall, the rainfall data of a monitoring station is continuously concerned, meanwhile, the moisture content of the soil and the change of the underground water level are forecasted, and once the rainfall is obviously changed, relevant departments are timely informed to take emergency measures.
While the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (7)

1. A lag nonlinear time forecasting method for landslide forecasting mainly based on rainfall is characterized by comprising the following steps:
the method comprises the following steps: analyzing the correlation of rainfall with the soil shallow layer water content and the underground water level to obtain the correlation and the functional relation;
step two: constructing a nonlinear model of rainfall, soil water content and underground water level based on the relation;
step three: and analyzing the influence of rainfall change on the soil moisture content and the underground water level, judging the lag period of the rainfall change, and finally obtaining the lag nonlinear time forecasting method for forecasting landslide mainly based on rainfall.
2. The method of claim 1, wherein the method of predicting the time of a non-linear lag in landslide prediction based on rainfall is further characterized in that,
the nonlinear model constructed in the second step is as follows:
Yi=(∝1,...,∝j)ln(Xi1,Xi2,...,Xik)+βi(1)
in the formula: y isiDependent variable of soil moisture content, ground water level
(∝1,...,∝j) -model parameter-slope
Xi1,Xi2,...,XikRainfall- -independent variable
βiDisturbance parameter-intercept
ln(Xi1,Xi2,...,Xik) -a non-linear function.
3. The method according to claim 1, wherein the lag period is determined by the change in the influence of rainfall on the moisture content of soil and the change in groundwater level in the third step.
4. The method for predicting the lag nonlinear time based on rainfall for landslide prediction according to claim 1, wherein in the first step, when the correlation between the rainfall and the soil shallow water content and the underground water level is analyzed, the monitoring data of a season with large rainfall and abundant rainwater in a flood season is selected and is representative, and meanwhile, the monitoring data of the soil water content with the burial depth of 50cm is selected for analysis in order to guarantee the reliability of the soil water content data in consideration of rainfall infiltration and evaporation.
5. The method according to claim 1, wherein the basic data analyzed in the first step is real-time data collected by a monitoring system using internet of things.
6. The method according to claim 1, wherein the lag period obtained from the final determination in the third step can be applied to actual prediction, and if the landslide does not occur but is still within the lag period, the threat of landslide cannot be eliminated.
7. The method according to claim 1, wherein the third step is based on real-time networking analysis, so as to realize real-time data real-time analysis.
CN201910982736.6A 2019-10-16 2019-10-16 Hysteresis nonlinear time forecasting method for landslide forecasting by taking rainfall as main part Pending CN110703359A (en)

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CN103217512A (en) * 2013-04-11 2013-07-24 中国科学院力学研究所 Experimental device with physical landslide model
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Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102013150A (en) * 2010-09-28 2011-04-13 浙江工业大学 System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation
WO2013009160A2 (en) * 2011-07-11 2013-01-17 Universiti Sains Malaysia A geometric method for predicting landslide disaste
CN103217512A (en) * 2013-04-11 2013-07-24 中国科学院力学研究所 Experimental device with physical landslide model
CN109815633A (en) * 2019-02-28 2019-05-28 河海大学 A kind of slope stability method of discrimination based on coupling about surface water and ground water model

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