CN108090707B - Mountain torrent disaster risk evaluation method and early warning system based on mutation theory - Google Patents
Mountain torrent disaster risk evaluation method and early warning system based on mutation theory Download PDFInfo
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Abstract
The invention discloses a mountain torrent disaster risk evaluation method and an early warning system based on a mutation theory, which are composed of a data acquisition system, a risk grade evaluation system and a grading early warning system. The data acquisition system is mainly used for automatically acquiring mountain torrent disaster influence factors by a method combining remote sensing interpretation, field investigation and field monitoring; the risk grade evaluation system constructs a hierarchical structure model by means of a mutation theory, calculates mutation grades layer by layer upwards according to the elementary mutation model, and finally determines the risk grade according to the mutation grades; the grading early warning system converts the evaluation grade into an early warning signal, and sends the early warning signal to a relevant government responsible person and residents around the government responsible person through wireless equipment. The invention establishes a set of full-automatic integrated system for mountain torrent data acquisition, disaster risk assessment and grading early warning for the first time, thereby not only avoiding the defects of strong subjectivity in the aspects of original data acquisition and index weight distribution, but also obtaining more accurate evaluation results from the internal action mechanism of the mountain torrent system.
Description
Technical Field
The invention relates to a natural disaster risk grading evaluation system, in particular to a mountain torrent disaster risk evaluation method and an early warning system based on a mutation theory.
Background
Mountain torrents refer to surface runoff phenomenon caused by rapid flood fluctuation due to heavy rainfall, are easy to cause local disasters due to strong burstiness and large destructive power, and are called mountain torrents because the mountain torrents occur in hilly areas. The mountain flood disaster has wide influence range and strong destructiveness, which not only causes huge damage and threat to the life and property safety of people in mountain areas, but also becomes a major bottleneck problem restricting the economic development of mountain areas. Therefore, the evaluation and early warning of the risk of the mountain torrent disaster are very important for effectively taking disaster prevention and reduction measures.
The research on mountain torrent disasters starts late in China and is basically in a blank state before the country is built. With the development of socio-economic and the continuous occurrence of mountain torrent disasters, the disaster prevention idea gradually changes from passive disaster prevention after the conventional disasters to active disaster prevention for disaster risk management. At present, although China has a certain foundation in the aspect of research on the risk of mountain torrent disasters, the accuracy of an evaluation result is influenced to a certain extent due to the shortage of original data in a research area and the influence of human factors on weight determination. Therefore, it is necessary to develop a set of fully automatic integrated system for mountain torrent data acquisition, disaster risk assessment and graded early warning.
The occurrence and evolution of the mountain torrent disaster are influenced and controlled by various factors with the characteristics of uncertainty, randomness and fuzziness, and the method is a complex process with jump, irreversibility and systematicness. Since mutation theory is a relatively mature and relatively sophisticated singularity theory, it is a mathematical theory that the state of the system varies discontinuously with changing external control parameters, and it is often used to recognize and predict the state behavior of complex systems. Therefore, the invention discloses a mountain torrent disaster risk evaluation method and an early warning system based on a mutation theory for the first time.
Disclosure of Invention
The invention aims to solve the problems that the prior art lacks a mountain torrent disaster risk evaluation method and an early warning system based on a mutation theory and the like in the background technology, and provides the mountain torrent disaster risk evaluation method and the early warning system based on the mutation theory; the method aims to establish a set of fully-automatic integrated system for mountain torrent data acquisition, disaster risk evaluation and grading early warning, not only avoids the defects of strong subjectivity in the aspects of original data acquisition and index weight distribution, but also obtains more accurate risk grade from the internal mechanism of the mountain torrent system.
A method and an early warning system for mountain torrent disaster risk evaluation based on mutation theory are characterized in that: the system comprises a data acquisition system, a risk grade evaluation system and a grade early warning system;
the data acquisition system comprises the following steps:
determining an evaluation index system
The method is characterized in that a mountain ditch where flood disasters occur is taken as a research object, factors influencing the risk of the mountain flood disasters are selected, and the method mainly comprises the following steps:
D1average gradient: the unit is Degree (DEG), the change degree of the terrain in the mountain ditches is reflected, the larger the gradient is, the shorter the convergence time of the mountain torrents is, the shorter the peak emergence time of the flood peak is, and the more adverse the flood control measures are adopted for the downstream;
D2relative height difference: under the action of rainfall, water flows to a lower place, and the lower the terrain is, the larger the height difference is, the larger the submerging range is;
D3soil type: under the action of rainfall, the better the permeability and the anti-scouring capability of soil are, the smaller the water and soil loss is, the less the loose substances carried in water flow are, and the smaller the influence on the flood discharge capability of a river channel is;
D4vegetation coverage rate: the better the vegetation growth, the better the soil water retention, which is helpful for conserving water source and preventing soil erosion;
D5maximum daily rainfall: the unit is mm, the rainfall size of a local area is described, and the maximum daily rainfall is larger, the probability of mountain torrent disasters is higher;
D6the rainfall frequency: the unit is time/month, which means the number of raining within one month, the higher the rainfall frequency is, the smaller the rainfall infiltration amount is, the larger the confluent water amount is, and the more easily the flood is formed;
D7water discharge and storage capacity: the unit is ten thousand meters3The capability of water drainage and storage around the mountain ditch after the mountain torrents occur is reflected, and the larger the drainage and storage capacity is, the stronger the flood control capability is, and the smaller the loss possibly caused is;
D8population density: unit is human/km2The safety of population is always disaster prevention and controlThe higher the population density, the greater the loss caused after the mountain torrents occur, and the greater the risk of needing defense;
D9material property: the unit is ten thousand yuan, which is the amount of loss of basic facilities such as houses and traffic and tangible assets such as cultivated lands and forest lands caused by disasters, and the more material and property are, the greater the economic loss caused by mountain torrents.
Secondly, collecting all evaluation index values
The influence factors of the torrential flood disasters mainly realize data acquisition by remote sensing interpretation, field investigation and field monitoring methods, wherein D is1Average slope, D2The relative height difference is automatically obtained mainly by remote sensing interpretation, D3Soil type, D8Population Density, D9The material property is mainly obtained by a field investigation method; d5Maximum amount of rainfall per day, D6The rainfall frequency is automatically obtained mainly by a field monitoring method; d4Coverage of vegetation, D7The drainage water amount is obtained by a method combining remote sensing interpretation and field investigation.
The risk level evaluation system comprises the following steps:
firstly, constructing a hierarchical structure model
According to the internal mechanism of action of the system, carrying out multi-level primary and secondary contradiction decomposition on the total target, arranging the primary and secondary contradictory decomposition into an inverted dendritic structure, and decomposing the primary and secondary contradictory decomposition layer by layer downwards until the total target is decomposed to measurable evaluation indexes;
first, a target layer (mountain torrent disaster risk rating a) is decomposed into 2 criterion layers: b is1Danger, B2Vulnerability;
then, B1The danger can be further broken down into 3 intermediate layers: c1Pregnant disaster environment, C2Disaster-causing factor, C3Flood control ability, B2Vulnerability is mainly through C4Reflecting the loss degree of the disaster bearing body;
finally, C1Pregnant disaster environment is by D on index layer1Average slope, D2Relative height difference, D3Soil type, D4Vegetation coverage composition, C2D of disaster-causing factor from index layer5Maximum amount of rainfall per day, D6Composition of rainfall frequency, C3Flood control capability is represented by D of index layer7The water discharge and storage amount is reflected, C4Disaster tolerance is by D of index layer8Population Density, D9The material and property composition decomposes the primary and secondary contradictions to form a hierarchical structure model of a target layer, a criterion layer, a middle layer and an index layer.
Second, non-dimensionalization of evaluation index
Because the value range and unit dimension of each evaluation index value are different, the influence of different measurement units on the evaluation result needs to be eliminated, so each evaluation index value needs to be converted into dimensionless data between 0 and 1, and meanwhile, the mutation theory requires that the bottommost index needs to be converted into an index which is larger and better; for positive index (D)1Average slope, D2Relative height difference, D3Soil type, D5Maximum amount of rainfall per day, D6Frequency of rainfall, D8Population Density, D9Physical property), standardized using formula (1); for the negative indexes (D4 vegetation coverage and D7 drainage capacity), carrying out standardization by adopting an equation (2);
in the formula: x is the number ofiIs an evaluation index value; x'iA normalized value as an evaluation index; x is the number ofminAnd xmaxThe minimum value and the maximum value of the evaluation index are respectively.
Derivation of normalized formula
The potential function of the mutation system is F (x), wherein x represents a state variable, coefficients u, v, w and t of x represent control variables, and a balance surface of the mutation system can be obtained by calculating a first derivative of F (x) and making F' (x) equal to 0; obtaining a singularity set of the balance surface by calculating a second derivative of F (x) and making F "(x) equal to 0; solving equations F' (x) ═ 0 and F "(x) ═ 0 simultaneously to obtain a bifurcation equation; when each control variable meets the bifurcation equation, the system is mutated, the normalization formula can be derived through the bifurcation equation in a decomposition form, different qualities of each control variable are normalized into the same quality state by the normalization formula, namely, the quality state represented by the state variable, wherein several commonly used mutation models are shown in the following table 1.
TABLE 1
| Type (B) | Variable of state | Controlled variable | Potential function | Normalized formula |
| Folding type | 1 | 1 | F(x)＝x3+ux | x＝u1/2 |
| Pointed type | 1 | 2 | F(x)＝x4+ux2+vx | xu＝u1/2,xv＝v1/3 |
| Dovetail type | 1 | 3 | F(x)＝x5+ux3+vx2+wx | xu＝u1/2,xv＝v1/3,xw＝w1/4 |
| Butterfly type | 1 | 4 | F(x)＝x6+tx4+ux3+vx2+wx | xt＝t1/2,xu＝u1/3,xv＝v1/4,xw＝w1/5 |
Fourthly, calculating mutation series
After the number of the control variables is determined, a corresponding elementary mutation model can be selected, the mutation series is calculated upwards layer by layer according to the normalization formulas of different models and dimensionless data of the bottom layer index, and finally the total mutation series is calculated for evaluation; if no correlation exists among indexes in the calculation process, adopting a non-complementary principle, namely, selecting small from large to medium; if there is a correlation between the indexes, the "complementary" principle is adopted, i.e., "taking the average".
Fifthly, determining the grade of the risk of the torrential flood disaster
Dividing the risk of the mountain torrent disaster into 4 grades, namely low risk, medium risk, high risk and extreme risk; firstly, standardizing single index values of various grades of torrential flood risk, then bringing the single index values into a normalization formula of a corresponding model for calculation, and obtaining a grade standard of mutation grades; and finally, comparing the total mutation grade of the sample to be evaluated with the grade standard of the mutation grade, thereby determining the risk grade of the torrent disaster to be evaluated.
The grading early warning system comprises the following steps:
according to the grade determined by the risk grade evaluation system: establishing a mountain torrent disaster risk grading early warning model with low risk, medium risk, high risk and extreme risk, wherein corresponding early warning signals are sequentially represented by blue, yellow, orange and red;
blue warning (low risk): the mountain torrents are distributed rarely, the scale is very small, the danger and the vulnerability are very low, the risk of damage caused by damage is very small, and the normal production and life are not influenced generally;
yellow warning (moderate risk): the mountain torrents are distributed less and have small scale, the vulnerability of disaster-bearing bodies is low, the damage is small, the comprehensive risk of the disaster is low, and certain preventive measures need to be taken;
orange warning (high risk): the mountain torrents are widely distributed, the scale is large, the vulnerability of a disaster bearing body is high, the damage of a disaster is heavy, the risk level of the disaster is high, and disaster prevention and control measures need to be laid to ensure the safety of production and life;
red warning (extreme risk): the mountain torrents are wide in distribution, large in scale, strong in destructive power, high in danger and vulnerability, extremely high in risk, frequently damaged, seriously influencing production and life, and needing to implement comprehensive disaster reduction engineering and strengthen risk management;
after the grading early warning system converts the evaluation result into a corresponding early warning signal, the early warning signal can be rapidly and accurately sent to a relevant government responsible person through a wireless device and is reported to surrounding residents and tourists, so that the relevant anti-disaster measures can be made timely.
The invention has the beneficial effects that:
1) according to the invention, a data acquisition system consisting of remote sensing interpretation, field investigation and field monitoring methods is integrated before the evaluation of the grade of the risk of the mountain torrent disaster, so that the system not only can comprehensively and automatically acquire the influence factors of the risk of the mountain torrent disaster, but also can greatly reduce the interference of artificial factors in the data acquisition process;
2) the occurrence and the evolution of the mountain torrent disasters are complex processes with sudden jump, irreversibility and systematicness, and the mutation theory is a system theory that catastrophe is caused by sudden interruption of continuous behaviors and is described by using an image mathematical model, so that the method is suitable for multi-target evaluation and decision-making, and the mutation theory is applied to evaluation of the risk level of the mountain torrent disasters, so that the inherent mechanism of the mountain torrent system can be really depicted, and the risk of the mountain torrent disasters can be accurately predicted;
3) the mutation theory carries out multi-level major-minor contradiction decomposition on a total target according to an internal action mechanism of a system, and ranks evaluation indexes according to a major-minor mode, and the theory adopts a method combining qualitative and quantitative modes, so that the relative importance of each evaluation index is balanced, and the defect of strong subjectivity in the aspect of weight distribution of the evaluation indexes is avoided;
4) because the invention integrates the grading early warning system composed of wireless equipment after the grade evaluation of the risk of the mountain torrent disaster, the system can quickly and automatically convert the evaluation result into the corresponding early warning signal, and send the early warning signal to the relevant government responsible persons and report to the surrounding residents and tourists in time.
Drawings
FIG. 1 is a flow chart of a method for assessing risk of torrential flood disasters and an early warning system according to the present invention;
FIG. 2 is an evaluation index system and a hierarchical structure model constructed based on mutation theory.
Detailed Description
Referring to fig. 1 and 2, a method for assessing risk of torrential flood disaster and an early warning system based on mutation theory are disclosed, which is characterized in that: the system comprises a data acquisition system, a risk grade evaluation system and a grade early warning system;
the data acquisition system comprises the following steps:
determining an evaluation index system
The method is characterized in that a mountain ditch where flood disasters occur is taken as a research object, factors influencing the risk of the mountain flood disasters are selected, and the method mainly comprises the following steps:
D1average gradient: the unit is Degree (DEG), the change degree of the terrain in the mountain ditches is reflected, the larger the gradient is, the shorter the convergence time of the mountain torrents is, the shorter the peak emergence time of the flood peak is, and the more adverse the flood control measures are adopted for the downstream;
D2relative height difference: under the action of rainfall, water flows to a lower place, and the lower the terrain is, the larger the height difference is, the larger the submerging range is;
D3soil type: under the action of rainfall, the better the permeability and the anti-scouring capability of soil are, the smaller the water and soil loss is, the less the loose substances carried in water flow are, and the smaller the influence on the flood discharge capability of a river channel is;
D4vegetation coverage rate: the better the vegetation growth, the better the soil water retention, which is helpful for conserving water source and preventing soil erosion;
D5maximum daily rainfall: the unit is mm, the rainfall size of a local area is described, and the maximum daily rainfall is larger, the probability of mountain torrent disasters is higher;
D6the rainfall frequency: the unit is time/month, which means the number of raining within one month, the higher the rainfall frequency is, the smaller the rainfall infiltration amount is, the larger the confluent water amount is, and the more easily the flood is formed;
D7water discharge and storage capacity: the unit is ten thousand meters3The capability of water drainage and storage around the mountain ditch after the mountain torrents occur is reflected, and the larger the drainage and storage capacity is, the stronger the flood control capability is, and the smaller the loss possibly caused is;
D8population density: unit is human/km2The safety of population is always the most important in disaster prevention and control, the greater the population density, the greater the loss caused after the occurrence of the mountain torrents and the greater the risk of needing defense;
D9material property: the unit is ten thousand yuan, which is the amount of loss of basic facilities such as houses and traffic and tangible assets such as cultivated lands and forest lands caused by disasters, and the more material and property are, the greater the economic loss caused by mountain torrents.
Secondly, collecting all evaluation index values
The influence factors of the torrential flood disasters mainly realize data acquisition by remote sensing interpretation, field investigation and field monitoring methods, wherein D is1Average slope, D2The relative height difference is automatically obtained mainly by remote sensing interpretation, D3Soil type, D8Population Density, D9The material property is mainly obtained by a field investigation method; d5Maximum amount of rainfall per day, D6The rainfall frequency is automatically obtained mainly by a field monitoring method; d4Coverage of vegetation, D7The drainage water amount is obtained by a method combining remote sensing interpretation and field investigation, and the evaluation index value of each torrent in the exemplary embodiment is shown in table 2:
TABLE 2
The risk level evaluation system comprises the following steps:
firstly, constructing a hierarchical structure model
According to the internal mechanism of action of the system, carrying out multi-level primary and secondary contradiction decomposition on the total target, arranging the primary and secondary contradictory decomposition into an inverted dendritic structure, and decomposing the primary and secondary contradictory decomposition layer by layer downwards until the total target is decomposed to measurable evaluation indexes;
first, a target layer (mountain torrent disaster risk rating a) is decomposed into 2 criterion layers: b is1Danger, B2Vulnerability;
then, B1The danger can be further broken down into 3 intermediate layers: c1Pregnant disaster environment, C2Disaster-causing factor, C3Flood control ability, B2Vulnerability is mainly through C4Reflecting the loss degree of the disaster bearing body;
finally, C1Pregnant disaster environment is by D on index layer1Average slope, D2Relative height difference, D3Soil type, D4Vegetation coverage composition, C2D of disaster-causing factor from index layer5Maximum amount of rainfall per day, D6Composition of rainfall frequency, C3The flood control capacity being provided by an indicator layerD7The water discharge and storage amount is reflected, C4Disaster tolerance is by D of index layer8Population Density, D9The material and property composition decomposes the primary and secondary contradictions to form a hierarchical structure model of a target layer, a criterion layer, a middle layer and an index layer.
Second, non-dimensionalization of evaluation index
Because the value range and unit dimension of each evaluation index value are different, the influence of different measurement units on the evaluation result needs to be eliminated, so each evaluation index value needs to be converted into dimensionless data between 0 and 1, and meanwhile, the mutation theory requires that the bottommost index needs to be converted into an index which is larger and better; for positive index (D)1Average slope, D2Relative height difference, D3Soil type, D5Maximum amount of rainfall per day, D6Frequency of rainfall, D8Population Density, D9Physical property), standardized using formula (1); for negative index (D)4Coverage of vegetation, D7Drainage capacity), standardized by formula (2);
in the formula: x is the number ofiIs an evaluation index value; x is the number ofi' is a normalized value of an evaluation index; x is the number ofminAnd xmaxRespectively the minimum value and the maximum value of the evaluation index; by using the above method, the evaluation indexes of each torrential flood in the exemplary embodiment are normalized, and the normalized values are shown in table 3:
TABLE 3
Derivation of normalized formula
The potential function of the mutation system is F (x), wherein x represents a state variable, coefficients u, v, w and t of x represent control variables, and a balance surface of the mutation system can be obtained by calculating a first derivative of F (x) and making F' (x) equal to 0; obtaining a singularity set of the balance surface by calculating a second derivative of F (x) and making F "(x) equal to 0; solving equations F' (x) ═ 0 and F "(x) ═ 0 simultaneously to obtain a bifurcation equation; when each control variable meets the bifurcation equation, the system is mutated, a normalization formula can be derived through the bifurcation equation in a decomposition form, different qualities of each control variable are normalized into the same quality state by the normalization formula, namely, the quality state represented by the state variable, wherein several commonly used mutation models and the types of elementary mutations are shown in a table 4:
TABLE 4
| Type (B) | Variable of state | Controlled variable | Potential function | Normalized formula |
| Folding type | 1 | 1 | F(x)＝x3+ux | x＝u1/2 |
| Pointed type | 1 | 2 | F(x)＝x4+ux2+vx | xu＝u1/2,xv＝v1/3 |
| Dovetail type | 1 | 3 | F(x)＝x5+ux3+vx2+wx | xu＝u1/2,xv＝v1/3,xw＝w1/4 |
| Butterfly type | 1 | 4 | F(x)＝x6+tx4+ux3+vx2+wx | xt＝t1/2,xu＝u1/3,xv＝v1/4,xw＝w1/5 |
Fourthly, calculating mutation series
After the number of the control variables is determined, the corresponding elementary mutation model can be selected, the mutation series is calculated upwards layer by layer according to the normalization formulas of different models and dimensionless data of the bottom layer index, and finally the total mutation series is calculated for evaluation. If no correlation exists among indexes in the calculation process, adopting a non-complementary principle, namely, selecting small from large to medium; if the indexes have correlation, a 'complementation' principle, namely 'average number' is adopted, mutation models selected in each level are shown in table 5, the mutation levels of evaluation indexes in each level are calculated by using a normalization formula of the mutation models, and the normalization of the number 1 torrential flood is taken as an example for explanation;
for D1、D2、D3And D4Is provided with
Because D1、D2、D3And D4Basically has no correlation among the components, and adopts the principle of 'big middle and small' according to the 'non-complementary' principle:
for D5And D6Is provided with
Because D5And D6Basically has no correlation among the components, and adopts the principle of 'big middle and small' according to the 'non-complementary' principle:
for D7Using a folded mutation model having C3＝(D7)1/2＝(0.500)1/2＝0.707；
For D8And D9Is provided with
Because D8And D9There is a correlation between them, according to the 'complementary' principle, the 'average number' is adopted:
for C, the same principle applies1、C2And C3The use of "non-complementary" dovetail mutation models, with B1＝min{(C1)1/2、(C2)1/3、(C3)1/4}＝0.672；
For C4The folding mutation model is B2＝(C4)1/2＝(0.312)1/2＝0.558；
For the same reason, for B1And B2The use of a "non-complementary" cusp-type mutation model having A1＝min{(B1)1/2、(B2)1/30.820. Therefore, the total mutation grade of the No. 1 torrential flood disaster risk is 0.820;
according to the method, the total mutation levels of the mountain torrent disaster risks of No. 2, No. 3 and No. 4 are respectively 0.869, 0.722 and 0.923.
TABLE 5
Fifthly, determining the grade of the risk of the torrential flood disaster
The risk of mountain torrent disasters is divided into 4 grades, which are respectively as follows: low risk, moderate risk, high risk, extreme risk; firstly, standardizing single index values of all grades of mountain torrent risks; then, the mutation series is brought into a normalization formula of a corresponding model for calculation, and the grade standard of the mutation series is obtained; finally, comparing the total mutation grade of the sample to be evaluated with the grade standard of the mutation grade, thereby determining the risk grade of the torrent disaster to be evaluated; wherein, the single index grading standard of the risk of the mountain torrent disaster is shown in table 6;
the total mutation series of the mountain torrent disaster risk of No. 1 is 0.820, is located 0.802 ~ 0.861, belongs to moderate risk, and similarly, the total mutation series of the mountain torrent disaster risk of No. 2, No. 3, No. 4 are 0.869, 0.722, 0.923 respectively, belong to high risk, low risk, extreme risk respectively.
TABLE 6
The grading early warning system comprises the following steps:
according to the grade determined by the risk grade evaluation system: establishing a mountain torrent disaster risk grading early warning model with low risk, medium risk, high risk and extreme risk, wherein corresponding early warning signals are sequentially represented by blue, yellow, orange and red;
blue warning (low risk): the mountain torrents are distributed rarely, the scale is very small, the danger and the vulnerability are very low, the risk of damage caused by damage is very small, and the normal production and life are not influenced generally;
yellow warning (moderate risk): the mountain torrents are distributed less and have small scale, the vulnerability of disaster-bearing bodies is low, the damage is small, the comprehensive risk of the disaster is low, and certain preventive measures need to be taken;
orange warning (high risk): the mountain torrents are widely distributed, the scale is large, the vulnerability of a disaster bearing body is high, the damage of a disaster is heavy, the risk level of the disaster is high, and disaster prevention and control measures need to be laid to ensure the safety of production and life;
red warning (extreme risk): the mountain torrents are wide in distribution, large in scale, strong in destructive power, high in danger and vulnerability, extremely high in risk, frequently damaged, seriously influencing production and life, and needing to implement comprehensive disaster reduction engineering and strengthen risk management;
after the grading early warning system converts the evaluation result into a corresponding early warning signal, the early warning signal can be quickly and accurately sent to a relevant government responsible person through wireless equipment and is reported to surrounding residents and tourists, so that the relevant anti-disaster measures can be made in time;
according to the method, the determined mountain torrent disaster risk early warning signals 1, 2, 3 and 4 are yellow early warning, orange early warning, blue early warning and red early warning respectively.
Claims (1)
1. A torrential flood disaster risk evaluation method based on a mutation theory is characterized by comprising the following steps: the method comprises the steps of data acquisition, risk grade evaluation and grading early warning;
the data acquisition comprises the following steps:
determining an evaluation index system
Taking a mountain ditch where flood disasters occur as a research object, selecting factors influencing the risk of the mountain flood disasters, wherein the factors comprise:
D1average gradient: the unit is degree, the change degree of the terrain in the mountain ditch is reflected, the larger the gradient is, the shorter the mountain torrent confluence time is, the shorter the flood peak occurrence time is, and the more adverse the flood control measures are taken for the downstream;
D2relative height difference: under the action of rainfall, water flows to a lower place, and the lower the terrain is, the larger the height difference is, the larger the submerging range is;
D3soil type: under the action of rainfall, the better the permeability and the anti-scouring capability of soil are, the smaller the water and soil loss is, the less the loose substances carried in water flow are, and the smaller the influence on the flood discharge capability of a river channel is;
D4vegetation coverage rate: the better the vegetation growth, the better the soil water retention, which is helpful for conserving water source and preventing soil erosion;
D5maximum daily rainfall: the unit is mm, the rainfall size of a local area is described, and the maximum daily rainfall is larger, the probability of mountain torrent disasters is higher;
D6the rainfall frequency: the unit is time/month, which means the number of raining within one month, the higher the rainfall frequency is, the smaller the rainfall infiltration amount is, the larger the confluent water amount is, and the more easily the flood is formed;
D7water discharge and storage capacity: the unit is ten thousand meters3The capability of water drainage and storage around the mountain ditch after the mountain torrents occur is reflected, and the larger the drainage and storage capacity is, the stronger the flood control capability is, and the smaller the loss possibly caused is;
D8population density: unit is human/km2The safety of population is always the most important in disaster prevention and control, the greater the population density, the greater the loss caused after the occurrence of the mountain torrents and the greater the risk of needing defense;
D9material property: the unit is ten thousand yuan, which is the loss amount of the infrastructure such as houses and traffic and the tangible assets such as cultivated land and forest land caused by the disaster, the more the material property is, the more the economic loss caused by the mountain torrents isLarge;
secondly, collecting all evaluation index values
The influence factors of the torrential flood disasters mainly realize data acquisition by remote sensing interpretation, field investigation and field monitoring methods, wherein D is1Average slope, D2The relative height difference is automatically obtained mainly by remote sensing interpretation, D3Soil type, D8Population Density, D9The material property is mainly obtained by a field investigation method; d5Maximum amount of rainfall per day, D6The rainfall frequency is automatically obtained mainly by a field monitoring method; d4Coverage of vegetation, D7The drainage water amount is obtained by a method combining remote sensing interpretation and field investigation;
the risk rating evaluation comprises the following steps:
firstly, constructing a hierarchical structure model
According to the internal mechanism of action of the system, carrying out multi-level primary and secondary contradiction decomposition on the total target, arranging the primary and secondary contradictory decomposition into an inverted dendritic structure, and decomposing the primary and secondary contradictory decomposition layer by layer downwards until the total target is decomposed to measurable evaluation indexes;
firstly, decomposing the mountain torrent disaster risk level evaluation A of a target layer into 2 criterion layers: b is1Danger, B2Vulnerability;
then, B1The danger can be further broken down into 3 intermediate layers: c1Pregnant disaster environment, C2Disaster-causing factor, C3Flood control ability, B2Vulnerability is mainly through C4Reflecting the loss degree of the disaster bearing body;
finally, C1Pregnant disaster environment is by D on index layer1Average slope, D2Relative height difference, D3Soil type, D4Vegetation coverage composition, C2D of disaster-causing factor from index layer5Maximum amount of rainfall per day, D6Composition of rainfall frequency, C3Flood control capability is represented by D of index layer7The water discharge and storage amount is reflected, C4Disaster tolerance is by D of index layer8Population Density, D9The material and property composition can form a layer of a target layer, a criterion layer, an intermediate layer and an index layer after the primary and secondary contradictions are decomposedA secondary structure model;
second, non-dimensionalization of evaluation index
Because the value range and unit dimension of each evaluation index value are different, the influence of different measurement units on the evaluation result needs to be eliminated, so each evaluation index value needs to be converted into dimensionless data between 0 and 1, and meanwhile, the mutation theory requires that the bottommost index needs to be converted into an index which is larger and better; for positive index D1Average slope, D2Relative height difference, D3Soil type, D5Maximum amount of rainfall per day, D6Frequency of rainfall, D8Population Density, D9Material property standardized by formula (1); standardizing the negative indexes D4 vegetation coverage and D7 water discharge capacity by adopting a formula (2);
in the formula: x is the number ofiIs an evaluation index value; x'iA normalized value as an evaluation index; x is the number ofminAnd xmaxRespectively the minimum value and the maximum value of the evaluation index;
derivation of normalized formula
The potential function of the mutation system is F (x), wherein x represents a state variable, coefficients u, v, w and t of x represent control variables, and a balance surface of the mutation system can be obtained by calculating a first derivative of F (x) and making F' (x) equal to 0; obtaining a singularity set of the balance surface by calculating a second derivative of F (x) and making F "(x) equal to 0; solving equations F' (x) ═ 0 and F "(x) ═ 0 simultaneously to obtain a bifurcation equation; when each control variable meets the bifurcation equation, the system is mutated, a normalization formula can be derived through the bifurcation equation in a decomposition form, different qualities of each control variable are normalized into the same quality state by the normalization formula, namely, the quality state represented by the state variable, wherein several commonly used mutation models are shown in the following table 1;
TABLE 1
Fourthly, calculating mutation series
After the number of the control variables is determined, a corresponding elementary mutation model can be selected, the mutation series is calculated upwards layer by layer according to the normalization formulas of different models and dimensionless data of the bottom layer index, and finally the total mutation series is calculated for evaluation; if no correlation exists among indexes in the calculation process, adopting a non-complementary principle, namely, selecting small from large to medium; if the indexes have correlation, a 'complementary' principle is adopted, namely 'average number is taken';
fifthly, determining the grade of the risk of the torrential flood disaster
Dividing the risk of the mountain torrent disaster into 4 grades, namely low risk, medium risk, high risk and extreme risk; firstly, standardizing single index values of various grades of torrential flood risk, then bringing the single index values into a normalization formula of a corresponding model for calculation, and obtaining a grade standard of mutation grades; finally, comparing the total mutation grade of the sample to be evaluated with the grade standard of the mutation grade, thereby determining the risk grade of the torrent disaster to be evaluated;
the grading early warning comprises the following steps:
according to the grade determined by the risk grade evaluation system: establishing a mountain torrent disaster risk grading early warning model with low risk, medium risk, high risk and extreme risk, wherein corresponding early warning signals are sequentially represented by blue, yellow, orange and red;
blue early warning: the mountain torrents are distributed rarely, the scale is very small, the danger and the vulnerability are very low, the risk of damage caused by damage is very small, and the normal production and life are not influenced generally;
yellow early warning: the mountain torrents are distributed less and have small scale, the vulnerability of disaster-bearing bodies is low, the damage is small, the comprehensive risk of the disaster is low, and certain preventive measures need to be taken;
orange early warning: the mountain torrents are widely distributed, the scale is large, the vulnerability of a disaster bearing body is high, the damage of a disaster is heavy, the risk level of the disaster is high, and disaster prevention and control measures need to be laid to ensure the safety of production and life;
red early warning: the mountain torrents are wide in distribution, large in scale, strong in destructive power, high in danger and vulnerability, extremely high in risk, frequently damaged, seriously influencing production and life, and needing to implement comprehensive disaster reduction engineering and strengthen risk management;
after the grading early warning system converts the evaluation result into a corresponding early warning signal, the early warning signal can be rapidly and accurately sent to a relevant government responsible person through a wireless device and is reported to surrounding residents and tourists, so that the relevant anti-disaster measures can be made timely.
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