CN108269016B - Small watershed torrential flood disaster risk analysis method based on information diffusion - Google Patents
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Abstract
The invention relates to a small watershed torrent disaster risk analysis method based on information diffusion, which introduces an information diffusion theory, takes limited historical torrent data as a basis, integrates samples with single-value observation values under the incomplete information condition into fuzzy integrated value samples with fuzzy uncertainty, establishes a torrent disaster information matrix, and introduces the calculation of a Pearson's triple curve, an override probability and a confidence coefficient, thereby realizing the rapid quantitative evaluation of torrent disaster risks through limited knowledge and leading the result to be closer to the actual condition. This patent can improve the objectivity and the scientificity of mountain torrent calamity risk analysis result.
Description
Technical Field
The invention relates to the technical field of disaster prevention, in particular to a small watershed torrential flood disaster risk analysis method based on information diffusion.
Background
The mountain torrent disaster refers to disasters such as personal casualties, property losses, infrastructure damage, environmental resource damage and the like caused by flood flooding, debris flow, mountain landslide, collapse and the like in mountainous areas along rivers and stream ditches due to rainstorm, flood blocking facility burst and the like, and has the five characteristics of outburst, destructiveness, mass occurrence, easiness in occurrence and harmfulness. The method is used for analyzing the risk of the mountain torrent disasters, quantitatively analyzing and objectively describing the probability of the mountain torrent disasters with different strengths in the mountain torrent disaster risk area and the uncertainty of possible consequences and the like, has an important basic effect on strengthening disaster early warning, and is an indispensable important component in prevention and control of the mountain torrent disasters.
However, in an actual small watershed, firstly, a large watershed is cut into a plurality of small watersheds due to complex mountainous terrain, and due to the limitation of the location, in consideration of factors such as economy and society, most of the small watersheds only have a small amount of monitoring stations such as hydrological stations, water level stations and rainfall stations, and the problem that no measured hydrological data or measured hydrological data cannot meet the analysis and research requirements and other data is lacked exists, so that the number of samples is small; secondly, most of the hydrological data of the domestic unified standard come from the country construction, the data volume is insufficient due to the short year, the national economy develops rapidly since the country construction, the change of the drainage basin underlying surface is large, and part of early measured data is lack of reliability due to the influence of artificial activities; finally, the regularity of mountain torrent disasters in small watersheds is not as strong as that of large rivers, and the difficulty of evaluation is increased due to the fact that actually measured data volume is rare. The three points show that the small watershed in China mostly has the characteristics of short data information continuous time sequence and low precision, the difficulty of risk analysis of the torrential flood disasters in the small watershed is increased, and the accuracy of the risk evaluation result is reduced.
At the present stage, aiming at the disaster risk analysis work of the torrential flood in the small watershed, an analysis method which can overcome the difficulty that uncertainty is increased due to lack of data and can fully mine limited data information to expand small sample data is needed, so that the risk analysis precision of the torrential flood disaster in the small watershed is improved, the risk evaluation level of the torrential flood disaster is improved, governments and people can correctly know and understand the disaster situation of the torrential flood disaster, and the disaster defense awareness is improved.
At present, the evaluation methods for mountain torrent disaster risks at home and abroad mainly comprise a probability statistical method, a fitting curve method, an index system method, a hydrological physical model method and the like.
(1) Probability statistical method: setting a statistical interval based on the measured data, determining the precision according to the data quantity, counting the data quantity occurring in the precision interval, dividing the statistical quantity of each interval by the total data quantity to obtain the frequency occurring in the interval, and taking the frequency as the probability to carry out risk assessment. Under the condition that the sample is larger, the frequency value is closer to the probability value, but the torrent disaster data volume is less, and the error of a simple statistical method is larger;
(2) fitting a curve method: establishing a two-dimensional relation graph of the disaster causing factors and the disaster degree by utilizing a large amount of measured data, selecting a proper fitting curve, fitting the data, establishing a relation curve of the disaster causing factors and the disaster degree, and determining the disaster degree according to the disaster causing factors. The method is a simple mathematical method, does not relate to any basic objective hydrological law, and the selection of the fitting curve model is greatly influenced by artificial factors. The method needs a large amount of statistical data, and the statistical data of the mountain torrent disasters belong to small samples, so that the reliability of the fitting curve is low due to the small amount of data;
(3) index system method: the method is widely applied in practice, and a whole set of comprehensive evaluation method system for compiling a risk zoning map by selecting evaluation indexes from four aspects of disaster-causing factor risk, pregnant disaster environment sensitivity, disaster-bearing body vulnerability and disaster prevention and reduction capacity by using technologies such as GIS and RS and distributing different weights of the evaluation indexes, calculating the risk and vulnerability and comprehensively obtaining the risk by using a weighting method is preliminarily established. However, the method has the problems that each factor is greatly influenced by subjective factors on index grading and weight distribution, the simple mechanical integration of risk values of each factor is difficult to embody the mechanicalness and the comprehensiveness of the disaster, a unified calculation method is lacked, although a plurality of weight calculation optimization methods such as an Analytic Hierarchy Process (AHP) are added subsequently, the adaptability is weak, the accuracy of an evaluation result is influenced to a certain extent, a large amount of data is required to support, and the fitness with the condition of the small-watershed torrential flood disaster is not high;
(4) hydrological physical model method: the physical model is based on a hydraulics physical law and combines a digital elevation model, hydrodynamics and a rainfall evaporation runoff mode to carry out simulation calculation on the runoff production and confluence of the drainage basin. The method looks scientific with computing power permissive, as the risk of mountain torrent disasters can be calculated and analyzed from the cause. However, in view of actual conditions, most of the mountain torrent disasters occur in small watersheds, the accuracy requirement on a GIS digital elevation model is high, secondly, the factors of the watershed underlying surface are complex, the deviation in a small range is large through direct calculation of a physical model under an ideal condition, and finally, the calculation requirement is too high, the required data and data are too huge, and the small watersheds cannot meet the requirement.
In summary, the existing torrential flood disaster risk analysis method mainly aims at provincial level, county level or large watershed, and the evaluation result is more accurate for the area with actually measured hydrological data. In the actual evaluation process, on one hand, the probability distribution of most small watersheds is unknown, the number of samples is small, and due to the limitation of the number and positions of hydrologic stations and rainfall stations, the problem that no hydrologic data or hydrologic data cannot meet the analysis and research requirements and other incomplete information exists, so that the fuzzy uncertainty is realized, and the difficulty in analyzing the risk of the mountain torrent disasters is increased; on the other hand, under the condition that the disaster mechanism is not cleared, complete description of the mountain torrent disaster is difficult to realize through simple empirical statistical analysis and probabilistic statistical analysis, the probabilistic statistical method is often limited by actual operation level while pursuing accurate probability, both the fitting curve method and the hydrological physical model method require a large amount of data, the construction of the index system method and the setting of the index weight have large subjectivity and uncertainty, and the requirement for refinement of the forecast of the existing small watershed mountain torrent cannot be met, so the small watershed mountain torrent disaster risk analysis method needs to be further developed and perfected.
Disclosure of Invention
The invention aims to provide a small watershed torrent disaster risk analysis method based on information diffusion aiming at the characteristics of unknown probability distribution and small data sample quantity of small watershed torrent disaster risk factors.
In order to realize the purpose, the technical scheme is as follows:
a small watershed torrent disaster risk analysis method based on information diffusion comprises the following steps:
(1) the steps of data collection: collecting environmental data of a small watershed which is subjected to mountain torrent disasters historically;
(2) analyzing and selecting risk factors: through analysis of collected data, a plurality of environmental influence factors which can possibly induce mountain torrent disasters are selected as risk factors;
(3) the method comprises the following steps of constructing a torrential flood disaster information matrix by utilizing information diffusion: according to an information diffusion theory, a research area torrent disaster information matrix is constructed by utilizing a normal information diffusion function, and the method comprises the following specific steps:
1) determining a sample set by using measured data in the collected environmental data of the selected risk factors as observed sample points, denoted by X, assuming that the number of X is n, the sample set X is represented by equation ①,
X＝{x1,x2,x3,...,xn-1,xn} ①
2) determining discourse domain, selecting proper monitoring points m according to the maximum value b and the minimum value a of X, obtaining a monitoring point set U as shown in formula ②,
U＝{u1,u2,u3，…,um}(u1≤a≤b≤um) ②
wherein xiFor observing the ith sample point in the sample set X, i is more than or equal to 1 and less than or equal to n
3) A step of constructing a mountain torrent disaster information matrix, which is to select a normal distribution model as a diffusion model because the randomness of the hydrological process in the mountain torrent disaster conforms to normal distribution, and observe each sample point X in a sample set X through a normal information diffusion function ③iThe information carried by it is diffused to all points in the monitoring point set U,
wherein u isjJ is greater than or equal to 1 and less than or equal to m and is the jth element in the monitoring point set U; mu.sijDenotes xiDiffusion to ujH is a diffusion coefficient determined from the maximum value b and the minimum value a in the sample X and the number n of X in the sample X, as shown in equation ④:
then, the torrent disaster information matrix Q of X on the two-dimensional space X × U is as shown in equation ⑤:
wherein Q isijFor elements in ith row and jth column in mountain torrent disaster information matrix Q
(4) And normalization processing of the matrix: normalizing the rows of the torrential flood disaster information matrix Q:
dividing each row by CiSuch that the sum of the row sums equals 1, respectively, a new matrix P is generated, where P isijIs the element in the ith row and jth column of the matrix P, as shown in equation ⑦:
(5) calculating the risk probability: information obtained by distributing different measured data of each monitoring point is overlapped to obtain the total information q distributed by each monitoring pointjAs shown in formula ⑧
Then dividing the information obtained by accumulating the monitoring points by qjThe total number of the samples obtained by superposition is obtained to obtain the risk probability value w of each monitoring pointjAs shown in equation ⑨:
(6) calculating the risk exceeding probability: the risk assessment is completed by calculating more than ujSum of probabilities of values, i.e. forming transcendental probability RkOverriding the probability value indicates that a different region is facingThe difference in the risk of torrential floods of different degrees is shown in formula ⑩.
(7) Calculating the transcendental probability distribution interval, namely after calculating the transcendental probability, estimating a range of probability risks under different confidence levels, assuming that the confidence level is 1- α, and calculating RkRe-sorting from small to large to obtain new RkThereafter, confidence intervals [ R ] at 1- α confidence levels are calculatedk1,Rk2]Wherein, in the step (A),
where z is round (M (α/2)), round is a rounding function, and M is the number of monitor points;
(8) calculating a risk factor threshold: fitting a hydrological relation curve through a Pearson type III curve, obtaining disaster occurrence frequency according to historical data, determining flood frequency according to specific conditions, and obtaining a risk factor threshold through the fitting curve;
(9) determining a mountain torrent disaster risk value: substituting the calculated risk factor threshold into the transcendental probability and the distribution interval thereof to obtain a risk value and a risk interval under the value; and substituting the measured data of the risk factors corresponding to the given flood frequency into the transcendental probability distribution, and calculating the transcendental probability value and the value range in the distribution interval, namely, the risk of the flood basin occurring mountain flood disasters larger than or equal to a certain magnitude.
The mountain flood disaster mostly occurs in small watershed of a mountain area, and has five characteristics of paroxysmal, destructive, mass-sending, easy-sending and harmfulness, the small watershed of the mountain area has the characteristics of large terrain difference, non-uniform soil humidity, non-uniform rainstorm space-time and the like, and is constrained by the distribution of the existing hydrological station network, so that the continuous time sequence of the measured data is short, the accuracy is low, and the mountain flood disaster risk analysis is inevitable and can suffer from the incomplete information problem of a small sample. In order to improve the reliability and accuracy of risk analysis, the method introduces an information diffusion theory, takes limited historical torrent data as a basis, carries out valuing on a sample with a single-value observation value under the incomplete information condition, converts the sample into a fuzzy set value sample with fuzzy uncertainty, establishes a torrent disaster information matrix, and introduces the calculation of a Pearson triple curve, an overrun probability and a confidence coefficient, thereby realizing the rapid quantitative evaluation of the torrent disaster risk through limited knowledge and enabling the result to be closer to the actual condition. The method can improve the objectivity and scientificity of the mountain torrent disaster risk analysis result, develops a new idea for researching the mountain torrent disaster risk uncertainty problem, and provides a scientific basis for timely and effective mountain torrent disaster risk avoidance.
Preferably, the environmental data comprises topographic and geomorphic data, soil vegetation data, meteorological hydrological data, human activity data.
Preferably, the environmental influence factors which may induce the mountain torrent disaster include rainfall factors, soil factors, landform factors, river reach information factors, human activity factors and meteorological hydrological factors.
Compared with the prior art, the invention has the beneficial effects that:
(1) compared with the traditional method, the information diffusion method adopted by the invention has stronger operability, eliminates the interference of human factors to a certain extent, has small data demand, can fully utilize non-integrated small sample data, distributes the information carried by the known non-uniform sample points carrying data to uniformly distributed control points without carrying information through a model by fuzzy mathematical relationship, expands the control points by a method conforming to objective rules to form an integrated set with uniform distribution, enhances the reliability of small sample risk assessment, and does not need to be doped with additional parameter estimation, thereby avoiding the amplification of errors and improving the accuracy of small-watershed torrent disaster risk analysis;
(2) aiming at the problem of small samples of the mountain torrent risk analysis in the small watershed, the uncertainty of the mountain torrent disaster under the incomplete information condition is fully considered, two-dimensional empirical statistical analysis and fuzzy mathematical modeling are scientifically and reasonably combined, limited data of the mountain torrent disaster in the small watershed are mined, an existing mountain torrent disaster quantitative evaluation model is expanded, the scientificity of the mountain torrent disaster risk analysis and calculation is greatly improved, and good support can be provided for the mountain torrent disaster risk decision;
(3) the conventional mountain torrent disaster evaluation and research area is mainly concentrated in large-scale and data-rich areas, and the method is suitable for small watersheds with small scales, lack of data or no data in mountainous areas.
Drawings
FIG. 1 is a schematic flow diagram of a method.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the invention is further illustrated below with reference to the figures and examples.
Example 1
As shown in fig. 1, the method provided by the present invention comprises the following steps:
1. data collection procedure
And (4) the environmental condition factors of the small-watershed torrential flood disasters are cleared up through typical case collection. Collecting related data and data of storm and torrential flood disasters of small and medium-sized rivers at home and abroad, and collating the data of landform and geomorphology, soil vegetation, meteorological hydrology, human activities, historical torrential flood disasters and the like in the small watershed to prepare sufficient and comprehensive data for analyzing the forming conditions of the torrential flood disasters in the small watershed.
In this embodiment, the data of landform, soil vegetation, meteorological hydrology, human activities, historical mountain torrent disasters, etc. are collected by taking county B of province a of a research area as an example.
2. Risk factor analysis and selection
And analyzing the acquired torrential rain and torrential flood disaster data, and selecting indexes capable of comprehensively reflecting torrential flood disaster conditions. And (3) going deep into the area, specifically analyzing the mountain torrent disaster risk factor of the research area according to rainfall factors, soil factors, landform, river reach information, human activities and other factors, particularly in combination with acquired annual maximum flow and maximum water level data of the research area for several years.
Because the water level and the flow data are embodied by combining various disaster-causing factors of the mountain torrent disasters, the scale of the mountain torrent disasters can be visually expressed, the data are easy to obtain and are important parameters for evaluating the mountain torrent disasters, and therefore the research area of the embodiment selects two parameters of annual maximum flow and maximum water level to perform model calculation.
3. Step of constructing mountain torrent disaster information matrix by using information diffusion
According to an information diffusion theory, a research area torrent disaster information matrix is constructed by utilizing a normal information diffusion function, and the method comprises the following specific steps:
(1) step of determining a sample set
Taking the measured data of the selected risk factors as observation sample points, and using X as the expression, assuming that the number of X is n, the sample set X is expressed by formula ①,
X＝{x1,x2,x3,...,xn}①
the annual maximum flow and annual maximum water level are selected as an observation sample set X in the research area respectively.
(2) Step of determining discourse domain
Selecting proper monitoring points m according to the maximum value b and the minimum value a of X to obtain a monitoring point set U
As shown in the formula ②,
U＝{u1,u2,u3，…,um}(u1≤a≤b≤um) ②
the maximum annual flow and the maximum annual water level and the minimum annual water level are taken, the number of monitoring points is selected, and as confidence interval calculation is carried out, the monitoring point data are about 50 or more, and 47 monitoring points are taken at this time. 47 monitoring points are uniformly arranged in the interval between the maximum value and the minimum value, and can be increased or decreased appropriately according to actual conditions.
(3) Step for constructing mountain torrent disaster information matrix
Because the randomness of the hydrological process is in accordance with normal distribution, a normal distribution model is selected as a diffusion model, and each single-value observation sample point x in the sample is observed through a normal information diffusion function ③iThe information carried by it is diffused to all points in the monitoring point set U,
wherein u isjJ is greater than or equal to 1 and less than or equal to m and is the jth element in the monitoring point set U; mu.sijDenotes xiDiffusion to ujH is a diffusion coefficient determined from the maximum value b and the minimum value a in the sample X and the number n of X in the sample X, as shown in equation ④:
then, the torrent disaster information matrix Q of X on the two-dimensional space X × U is as shown in equation ⑤:
wherein Q isijFor elements in ith row and jth column in mountain torrent disaster information matrix Q
4. And normalization processing of the matrix: normalizing the rows of the torrential flood disaster information matrix Q:
dividing each row by CiSuch that the sum of the row sums equals 1, respectively, a new matrix P is generated, where P isijIs the element in the ith row and jth column of the matrix P, as shown in equation ⑦:
5. calculating the risk probability: information obtained by distributing different measured data of each monitoring point is overlapped to obtain the total information q distributed by each monitoring pointjAs shown in formula ⑧
Then dividing the information obtained by accumulating the monitoring points by qjThe total number of the samples obtained by superposition is obtained to obtain the risk probability value w of each monitoring pointjAs shown in equation ⑨:
6. calculating the risk exceeding probability: the risk assessment is completed by calculating more than ujSum of probabilities of values, i.e. forming transcendental probability RkOverriding the probability values indicates that different regions are at different levels of risk of torrential flood, as shown in equation ⑩.
The flow data respectively obtain a flow probability density curve and a flow transcendental probability distribution curve through the calculation; and respectively obtaining a water level probability density curve and a water level transcendental probability distribution curve by the water level data through the calculation.
7. Calculating the transcendental probability distribution interval, namely after calculating the transcendental probability, estimating a range of probability risks under different confidence levels, assuming that the confidence level is 1- α, and calculating RkRe-sorting from small to large to obtain new RkThereafter, confidence intervals [ R ] at 1- α confidence levels are calculatedk1,Rk2]Wherein, in the step (A),
where z is round (M (α/2)), round is a rounding function, and M is the number of monitor points.
In this embodiment, when the confidence interval is set to 90%, α is 0.1, z is 1, and N is 47, and the flow rate exceeding probability distribution interval and the water level exceeding probability distribution interval are calculated respectively.
Table one 90% confidence lower flow transcendental probability distribution
Water level override probability distribution at 90% confidence in TABLE II
8. Calculating a risk factor threshold:
after the override probability is obtained, a threshold value, also called threshold value, is required for risk classification. Fitting the data through a curve model, wherein the hydrological data generally adopts a P-III type curve, and the curve with the best fitting effect is used as a flood flow frequency relation curve; and obtaining disaster occurrence frequency according to historical data, determining flood frequency according to specific conditions, and obtaining a threshold value through a fitting curve.
In the threshold calculation of the research area of the embodiment, the data is fitted by using a P-III type curve, and the curve with a good fitting effect is selected and taken as a mountain torrent disaster frequency curve and is drawn on Hessian probability grid paper. After investigating historical statistical data, it is known that the frequency of mountain flood disasters occurring in the drainage basin is 47.8%, the frequency of large mountain flood disasters occurring is 19.6%, as large hydraulic engineering is not built in the drainage basin, the consistency of the data is not destroyed, and the reliability is high, the flow and water level data corresponding to the flood frequency of 45% are used as the threshold of the mountain flood disasters occurring, the flow and water level corresponding to the frequency of 20% are used as the threshold of the large mountain flood disasters occurring, and meanwhile, the data with the flood frequencies of 2%, 5% and 10% are introduced to further evaluate the mountain flood disaster risks, as shown in table three.
Meter-III flood frequency and corresponding information diffusion flow and water level
9. Step for determining mountain torrent disaster risk value
Substituting the calculated threshold value into the transcendental probability and the confidence interval thereof to obtain a risk value and a risk interval under the value; and substituting the flow and water level data corresponding to the given flood frequency into the transcendental probability distribution, and calculating the transcendental probability value and the value range under the confidence interval, namely the risk that the flood area has mountain flood disasters larger than or equal to a certain magnitude.
The evaluation of the mountain torrent disasters in the research area is carried out by the probability which is greater than or equal to the grading judgment value, and the exceeding probability is obtained by a probability density curve and is greater than or equal to the accumulated value of the flow or water level probability, so the exceeding probability corresponding to the grading judgment value is the risk value of the mountain torrent disasters.
In the research area of the embodiment, a flow value and a water level value corresponding to a flood frequency of 45% are used as threshold values for judging whether flood occurs, a risk value of the research area for mountain torrent disasters is 0.354 to 0.628 according to flow data, and a risk value of the research area for mountain torrent disasters is 0.358 to 0.579 according to water level data; taking the flow rate and the water level corresponding to 20% of flood frequency, namely 'meeting in five years' as threshold values for generating larger flood, obtaining the risk value of the disaster of the larger mountain torrents to be 0.152 to 0.241 according to the flow rate data, and obtaining the risk value of the disaster of the larger mountain torrents to be 0.181 to 0.255 according to the water level data; and taking the flow rate and the water level corresponding to the flood frequency of 10 percent, namely 'one time every ten years', as the threshold value for generating the flood, obtaining the risk value of the mountain flood disaster to be 0.085 to 0.123 according to the flow rate data, and obtaining the risk value of the mountain flood disaster to be 0.112 to 0.142 according to the water level data, thereby completing the risk evaluation of the mountain flood disaster in the deficient and small watershed.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (1)
1. A small watershed torrent disaster risk analysis method based on information diffusion is characterized by comprising the following steps: the method comprises the following steps:
(1) the steps of data collection: collecting environmental data of a small watershed which is subjected to mountain torrent disasters historically;
(2) analyzing and selecting risk factors: through analysis of collected data, a plurality of environmental influence factors which can possibly induce mountain torrent disasters are selected as risk factors;
(3) the method comprises the following steps of constructing a torrential flood disaster information matrix by utilizing information diffusion: according to an information diffusion theory, a research area torrent disaster information matrix is constructed by utilizing a normal information diffusion function, and the method comprises the following specific steps:
1) determining a sample set by using measured data in the collected environmental data of the selected risk factors as observed sample points, denoted by X, assuming that the number of X is n, the sample set X is represented by equation ①,
X＝{x1,x2,x3,...,xn-1,xn} ①
2) determining discourse domain, selecting proper monitoring points m according to the maximum value b and the minimum value a of X, obtaining a monitoring point set U as shown in formula ②,
wherein xiFor observing the ith sample point in the sample set X, i is more than or equal to 1 and less than or equal to n
3) A step of constructing a mountain torrent disaster information matrix, which is to select a normal distribution model as a diffusion model because the randomness of the hydrological process in the mountain torrent disaster conforms to normal distribution, and observe each sample point X in a sample set X through a normal information diffusion function ③iThe information carried by it is diffused to all points in the monitoring point set U,
wherein u isjJ is greater than or equal to 1 and less than or equal to m and is the jth element in the monitoring point set U; mu.sijDenotes xiDiffusion to ujH is a diffusion coefficient determined from the maximum value b and the minimum value a in the sample X and the number n of X in the sample X, as shown in equation ④:
then, the torrent disaster information matrix Q of X on the two-dimensional space X × U is as shown in equation ⑤:
wherein Q isijFor elements in ith row and jth column in mountain torrent disaster information matrix Q
(4) And normalization processing of the matrix: normalizing the rows of the torrential flood disaster information matrix Q:
dividing each row by CiSuch that the rows are accumulatedThe sums are respectively equal to 1, a new matrix P is generated, where PijIs the element in the ith row and jth column of the matrix P, as shown in equation ⑦:
(5) calculating the risk probability: information obtained by distributing different measured data of each monitoring point is overlapped to obtain the total information q distributed by each monitoring pointjAs shown in formula ⑧
Then dividing the information obtained by accumulating the monitoring points by qjThe total number of the samples obtained by superposition is obtained to obtain the risk probability value w of each monitoring pointjAs shown in equation ⑨:
(6) calculating the risk exceeding probability: the risk assessment is completed by calculating more than ujSum of probabilities of values, i.e. forming transcendental probability RkExceeding the probability value indicates that different areas face different degrees of torrential flood risk, as shown in equation ⑩:
(7) calculating the transcendental probability distribution interval, namely after calculating the transcendental probability, estimating a range of probability risks under different confidence levels, assuming that the confidence level is 1- α, and calculating RkRe-sorting from small to large to obtain new RkThereafter, confidence intervals [ R ] at 1- α confidence levels are calculatedk1,Rk2]Wherein, in the step (A),
where z is round (M (α/2)), round is a rounding function, and M is the number of monitor points;
(8) calculating a risk factor threshold: fitting a hydrological relation curve through a Pearson type III curve, obtaining disaster occurrence frequency according to historical data, determining flood frequency according to specific conditions, and obtaining a risk factor threshold through the fitting curve;
(9) determining a mountain torrent disaster risk value: substituting the calculated risk factor threshold into the transcendental probability and the distribution interval thereof to obtain a risk value and a risk interval under the value; substituting the measured data of the risk factors corresponding to the given flood frequency into the transcendental probability distribution, and calculating the transcendental probability value and the value range under the distribution interval, namely calculating the risk of the flood basin occurring mountain flood disasters larger than or equal to a certain magnitude;
the environment data comprises topographic and geomorphic data, soil vegetation data, meteorological hydrological data and human activity data;
the environmental influence factors which may induce mountain torrent disasters comprise rainfall factors, soil factors, terrain and landform factors, river reach information factors, human activity factors and meteorological hydrological factors.
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Application publication date: 20180710 Assignee: Guangzhou Kangpusi Network Technology Co.,Ltd. Assignor: SUN YAT-SEN University Contract record no.: X2023980054067 Denomination of invention: A risk analysis method for small watershed flash floods based on information diffusion Granted publication date: 20200327 License type: Common License Record date: 20231227 Application publication date: 20180710 Assignee: Huanyi (Guangdong) emergency safety Technology Group Co.,Ltd. Assignor: SUN YAT-SEN University Contract record no.: X2023980053956 Denomination of invention: A risk analysis method for small watershed flash floods based on information diffusion Granted publication date: 20200327 License type: Common License Record date: 20231225 |