CN111860973A - Debris flow intelligent early warning method based on multi-objective optimization - Google Patents
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Abstract
The invention discloses a debris flow intelligent early warning method based on multi-objective optimization, which belongs to the field of geological disaster monitoring and early warning, particularly relates to the field of debris flow early warning, and aims to overcome the defects of larger error, inaccuracy and poor reliability of the existing debris flow prediction method; constructing a proxy model for evaluating and exciting the debris flow; solving the pareto threshold front edge of the debris flow proxy model; inputting the front edge of a debris flow pareto threshold of a debris flow sample point to be predicted and a debris flow proxy model into a debris flow early warning discriminator to perform dominant discrimination comparison, and outputting dominant discrimination results, wherein dominant discrimination results are that early warning is not performed, non-dominant discrimination results are that yellow early warning is performed, and dominant discrimination results are that red early warning is performed; evaluating the support degree of red early warning; and carrying out debris flow early warning according to the dominance judgment result and the support degree. And a more accurate proxy model is constructed, and the reliability and operability of debris flow monitoring and early warning are greatly improved.
Description
Technical Field
The invention discloses a debris flow intelligent early warning method based on multi-objective optimization, belongs to the field of geological disaster monitoring and early warning, and particularly relates to the technical field of debris flow early warning.
Background
Debris flow is the flood flow formed by saturated dilution of a sandy and soft soil mountain body containing sand and stones through rainstorm and flood, and is large in area, volume and flow rate, and landslide is a small-area of the diluted soil mountain body, and typical debris flow is composed of viscous slurry which is suspended with coarse solid debris and is rich in silt and clay. Under proper terrain conditions, a large amount of water soaks solid accumulated substances in a flowing water hillside or a ditch bed, so that the stability of the solid accumulated substances is reduced, and the solid accumulated substances saturated with water move under the action of self gravity to form debris flow. Debris flow is a disastrous geological phenomenon. Usually the debris flow is sudden, violent and can carry huge stones. It is extremely destructive because it has a strong energy due to its high speed of travel.
With the continuous development of computer technology, intelligent means is more and more applied to early warning of debris flow.
The conventional early warning technology obtains an approximate debris flow early warning curve by regression analysis of historical data. The method generally adopts a relatively universal fitting model of a linear function or a logistic function, and different models cannot be used for analysis according to different characteristics of each region. Other debris flow early warning methods based on machine learning are to consider whether debris flow occurs or not as a two-classification problem to train an optimal classifier, such as a neural network or a support vector machine.
The above methods using regression analysis, neural networks, and support vector machines require training the classifier using sample data of occurring and non-occurring debris flows. The sample data of the non-occurrence debris flow can not deduce the deviation of the sample data of the non-occurrence debris flow, so that the error of the classifier is large, and only two classifications can be carried out on the monitoring data, namely, whether the debris flow occurs or not is predicted.
Disclosure of Invention
The invention aims to: the method is used for solving the defects that the existing debris flow early warning method has larger error of a classifier because sample data which does not generate debris flow is adopted, the deviation of exciting the debris flow can not be deduced, and in addition, only two classifications can be carried out on monitoring data, namely whether the debris flow is generated or not is predicted, so that the method is inaccurate and not high in reliability.
The technical scheme adopted by the invention is as follows:
an intelligent debris flow early warning method based on multi-objective optimization comprises the following steps
Step 1, collecting a data sample of the generated debris flow;
step 2, constructing a proxy model for evaluating and exciting the debris flow;
step 3, solving the pareto threshold front edge of the debris flow proxy model;
Step 4, inputting the front edge of a debris flow pareto threshold value of a debris flow sample point to be predicted and a debris flow proxy model into a debris flow early warning discriminator to perform dominant discrimination comparison, and outputting a dominant discrimination result, wherein dominant discrimination result is red early warning;
and 6, carrying out debris flow early warning according to the dominance judgment result and the support degree.
Preferably, historical sample data of the debris flow which has occurred in the monitoring early warning area is collected.
Preferably, the debris flow sample point to be predicted is also in the monitoring early warning area.
Preferably, the debris flow proxy model in the step 2 is a multi-target proxy model, and the multi-target proxy model has a plurality of input variables, namely a plurality of debris flow influence factors, and a plurality of output variables, namely evaluation indexes for exciting the debris flow.
Preferably, the multi-target agent model is one or a group of display functions (regression equation, kernel function, etc.) or implicit models (such as neural network expressed by connecting weights and threshold values) obtained by learning and training the mud stone flow data samples, and the learning and training method of the multi-target agent model can be one or more integration of machine learning methods.
Preferably, the input of the multi-target agent model is arousal rain intensity ImaxAnd the cumulative rainfall E induced by the excitation process is output as the average hourly rainfall intensity I of the excitation process and the rainfall induction duration D of the debris flow excitation process, the average hourly rainfall intensity I of the excitation process and the rainfall induction duration D of the debris flow excitation process are optimized, the optimization algorithm adopts a multi-objective particle swarm optimization, and the obtained minimum pareto frontier of the two targets is a data set and does not represent a specific curve.
Preferably, the front edge of the debris flow pareto threshold is composed of a group of debris flow excitation index values which are not dominant to each other, and the debris flow pareto threshold is obtained by performing optimization solution on a multi-objective proxy model through a multi-objective optimization method, wherein the multi-objective optimization method comprises a data optimization method or a multi-objective evolutionary optimization method, such as a multi-objective genetic algorithm and a multi-objective particle swarm optimization algorithm. The multi-objective minimized Pareto front on the data samples of the occurring debris flow can represent a minimum threshold of an evaluation index of the occurring debris flow.
Preferably, the multi-target agent model is constructed as follows: first, a fully connected network is used to fit historical data of the occurring debris flow, and the data is obtained according to the following steps of 4: 1, dividing training data and test data in proportion, then adopting a loss function and a regularization term as two targets in a double-target optimization problem in a training process, then adopting a multi-target particle swarm optimization method to obtain a corresponding debris flow pareto threshold front edge, then adopting a two-stage optimal decision method to select 5 candidate solution sets in the debris flow pareto threshold front edge, and determining parameters of a proxy model according to the final performance of the 5 solution sets on prediction data. According to the performance of the agent models corresponding to different solution sets on test data, a group of parameters with the best performance is finally selected, and the construction of the multi-target agent model is completed. At the moment, the multi-target agent model is not a classifier, so that two target minimum pareto curves of the multi-target agent model need to be found through multi-target optimization, the multi-target particle swarm optimization is used for carrying out two target minimum optimization on average hourly rainfall intensity I of two output excitation processes of the neural network and rainfall induction duration D of the debris flow excitation process, and the corresponding pareto curves are found and input into the debris flow early warning discriminator. And then, acquiring debris flow observation data, namely debris flow sample points needing to be predicted, through a debris flow online monitor, and inputting the data into a debris flow early warning discriminator to perform dominant discrimination and comparison.
Preferably, the debris flow sample point to be predicted and the front edge of the debris flow pareto threshold have the same physical index.
Preferably, the dominance determination and comparison is performed by comparing the magnitudes of two multidimensional variables, and a Pareto dominance comparison method, a relaxed Pareto dominance comparison method (such as-Pareto dominance) or any comparison method for distinguishing multidimensional variables is adopted.
Preferably, the debris flow sample points to be predicted are obtained by acquiring data on an online debris flow monitor and performing data preprocessing, such as filtering and denoising.
Preferably, the mud-rock flow sample point to be predicted is dominant at least at one point on the pareto curve, and the mud-rock flow sample point to be predicted and the point on the pareto curve are not dominant if the dominant relationship cannot be compared.
Preferably, the debris flow sample points to be predicted are dominant, the possibility of the occurrence of the debris flow is considered to be very low, early warning is not needed, the debris flow sample points to be predicted are dominant, the debris flow is considered to be excited to send out red early warning, the debris flow sample points to be predicted are non-dominant, continuous close observation is needed, and yellow early warning is sent out.
Preferably, the debris flow sample point to be predicted is dominated by a point N on a pareto curve where the debris flow sample point to be predicted is dominated according to the point NiTo point N on the total pareto curveallCalculating the support S from the ratio ofdegreeIf the support is Sdegree＝Ni/NallFor a dominant red warning point, when SdegreeWhen the mud-rock flow is less than 10%, considering that the mud-rock flow generated at the mud-rock flow sample point needing to be predicted is slight, and making mud-rock flow prevention preparation work by governments and related departments according to responsibilities; when S isdegreeWhen the current is 10-20%, considering that debris flow generated by debris flow sample points needing to be predicted is in the middle of the debris flow, and the like, the government and related departments make debris flow prevention preparation work according to duties, and simultaneously should cut off dangerous outdoor power supply to transfer personnel in dangerous areas to safe places; when S isdegreeWhen the mud-rock flow is 20-40%, the mud-rock flow occurring at the mud-rock flow sample point needing to be predicted is considered to be serious, the government and related departments make mud-rock flow emergency work according to duties, and the traffic management department needs to adopt traffic control on part of road sections according to conditions; when S isdegreeWhen the number of the mud-rock flow sample points is more than 40%, the mud-rock flow which needs to be predicted is considered to be very serious, the government and related departments carry out emergency work of the mud-rock flow according to responsibilities, the defense and emergency work of disasters such as the mud-rock flow and the like should be carried out, and meanwhile, the related meetings and the courseware are stopped. Therefore, the index can reflect the severity of the occurrence of debris flow.
Preferably, in the step 4, a dominance judgment result is output, and the dominance is not early-warned, so that early warning is not needed; the early warning of yellow color is realized when the yellow color is not dominant, continuous close observation is needed, and the support degree is not calculated when the yellow color is dominant or not dominant.
In the technical scheme of the application, Pareto is Pareto.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, a more accurate proxy model can be constructed without too much prior knowledge, and the reliability and operability of debris flow monitoring and early warning can be greatly improved.
2. In the invention, only the samples of the existing debris flow are used when the proxy model is constructed, so that the deviation caused by the samples of the existing debris flow can be avoided on one hand, and the required sample amount can be reduced on the other hand.
3. According to the invention, through multi-objective intelligent optimization, the lower boundary of the proxy model can be quickly found, and the operability of debris flow monitoring and early warning is improved.
4. In the invention, by introducing a method for comparing the support degree and the dominance, not only can the result of three classifications be obtained, but also the severity can be judged according to the support degree, thereby more scientifically and efficiently deploying the pre-alarm work.
5. The invention provides a debris flow only early warning method based on multi-objective optimization, which can construct a proxy model without relying on prior knowledge and does not depend on samples without debris flow; the early warning point set can be found more scientifically and efficiently by adopting a multi-objective optimization method when the front edge of the Pareto threshold of the debris flow is found; and in the last early warning judgment part, the early warning work of the debris flow can be guided to be better carried out by a method of comparing the support degree with the dominance.
Drawings
FIG. 1 is a flow chart of an intelligent debris flow early warning method based on multi-objective optimization according to the invention;
fig. 2 is a diagram of a case of early warning debris flow in embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in FIG. 1, the debris flow intelligent early warning method based on multi-objective optimization comprises the following steps
Step 1, collecting data samples of debris flow in a monitoring early warning area;
step 2, constructing a proxy model for evaluating and exciting the debris flow, wherein the debris flow proxy model is a multi-target proxy model, and the multi-target proxy model is provided with a plurality of input variables, namely a plurality of debris flow influence factors, and a plurality of output variables, namely evaluation indexes for exciting the debris flow; the multi-target agent model is one or a group of display functions (regression equation, kernel function and the like) or implicit expression functions obtained by learning and training the existing debris flow data samplesModels (e.g., neural networks expressed by connecting weights and thresholds), the learning training method of the multi-objective agent model may be one or more integrations of machine learning methods; input of the multi-target agent model is aroused rain intensity I maxThe average hourly rainfall intensity I of the excitation process and the rainfall induction duration D of the debris flow excitation process are output, the optimized two targets are the average hourly rainfall intensity I of the excitation process and the rainfall induction duration D of the debris flow excitation process, the optimized algorithm adopts a multi-target particle swarm algorithm, and the obtained minimum pareto frontier of the two targets is a data set and does not represent a specific curve; constructing a multi-target agent model: first, a fully connected network is used to fit historical data of the occurring debris flow, and the data is obtained according to the following steps of 4: 1, dividing training data and test data in proportion, then adopting a loss function and a regularization term as two targets in a double-target optimization problem in a training process, then adopting a multi-target particle swarm optimization method to obtain a corresponding debris flow pareto threshold front edge, then adopting a two-stage optimal decision method to select 5 candidate solution sets in the debris flow pareto threshold front edge, and determining parameters of a proxy model according to the final performance of the 5 solution sets on prediction data. According to the performance of the agent models corresponding to different solution sets on test data, a group of parameters with the best performance (the parameters with the minimum Mean Square Error (MSE) on the test data) is finally selected, and the construction of the multi-target agent model is completed. At the moment, the multi-target agent model is not a classifier, so that two target minimum pareto curves of the multi-target agent model need to be found through multi-target optimization, the multi-target particle swarm optimization is used for carrying out two target minimum optimization on average hourly rainfall intensity I of two output excitation processes of the neural network and rainfall induction duration D of the debris flow excitation process, and the corresponding pareto curves are found and input into the debris flow early warning discriminator. Then, acquiring debris flow observation data, namely debris flow sample points needing to be predicted, through a debris flow online monitor, and inputting the data into a debris flow early warning discriminator to perform dominant discrimination and comparison;
And 3, solving the pareto threshold front edge of the debris flow proxy model, wherein the pareto threshold front edge of the debris flow is composed of a group of debris flow excitation index values which are not dominant, and the pareto threshold front edge is obtained by carrying out optimization solution on the multi-target proxy model through a multi-target optimization method, wherein the multi-target optimization method comprises a data optimization method or a multi-target evolution optimization method, such as a multi-target genetic algorithm and a multi-target particle swarm optimization algorithm. The method comprises the steps that a multi-target minimized Pareto front edge on a data sample of the existing debris flow can represent a minimum threshold value of an evaluation index of the existing debris flow; the debris flow sample point to be predicted and the front edge of the debris flow pareto threshold have the same physical index;
step 4, inputting the debris flow sample points to be predicted in the monitoring early-warning area and the front edge of the debris flow pareto threshold of the debris flow proxy model into a debris flow early-warning discriminator to perform dominant discrimination comparison, and outputting a dominant discrimination result, wherein dominant discrimination means no early warning, non-dominant discrimination means yellow early warning, and dominant discrimination means red early warning; comparing the dominance, comparing the size of two multidimensional variables, adopting a Pareto dominance comparison method, a loose Pareto dominance comparison method (such as-Pareto dominance) or any comparison method for distinguishing multidimensional variables; the debris flow sample points to be predicted are obtained by acquiring data on a debris flow on-line monitor and preprocessing the data, such as filtering and denoising; the mud-rock flow sample point to be predicted is dominant at least at one point on the pareto curve, and the mud-rock flow sample point to be predicted and the point on the pareto curve are not dominant if the dominant relation cannot be compared; if the mud-rock flow sample points to be predicted are dominant, the possibility of the occurrence of the mud-rock flow is considered to be very low, early warning is not needed, if the mud-rock flow sample points to be predicted are dominant, the mud-rock flow is considered to be excited to send out red early warning, if the mud-rock flow sample points to be predicted are not dominant, the mud-rock flow sample points to be predicted need to be continuously and closely observed to send out yellow early warning;
step 6, carrying out debris flow early warning according to the dominance judgment result and the support degree; mud to be predictedThe point N on the pareto curve where the rock flow sample point is dominated and the mud flow sample point predicted as required is dominatediTo point N on the total pareto curveallCalculating the support S from the ratio ofdegreeIf the support is Sdegree＝Ni/NallFor a dominant red warning point, when SdegreeWhen the mud-rock flow is less than 10%, considering that the mud-rock flow generated at the mud-rock flow sample point needing to be predicted is slight, and making mud-rock flow prevention preparation work by governments and related departments according to responsibilities; when S isdegreeWhen the current is 10-20%, considering that debris flow generated by debris flow sample points needing to be predicted is in the middle of the debris flow, and the like, the government and related departments make debris flow prevention preparation work according to duties, and simultaneously should cut off dangerous outdoor power supply to transfer personnel in dangerous areas to safe places; when S isdegreeWhen the mud-rock flow is 20-40%, the mud-rock flow occurring at the mud-rock flow sample point needing to be predicted is considered to be serious, the government and related departments make mud-rock flow emergency work according to duties, and the traffic management department needs to adopt traffic control on part of road sections according to conditions; when S isdegreeWhen the number of the mud-rock flow sample points is more than 40%, the mud-rock flow which needs to be predicted is considered to be very serious, the government and related departments carry out emergency work of the mud-rock flow according to responsibilities, the defense and emergency work of disasters such as the mud-rock flow and the like should be carried out, and meanwhile, the related meetings and the courseware are stopped. Therefore, the index can reflect the severity of the occurrence of debris flow.
Example 2
On the basis of the embodiment 1, 9 points of data of the pu-er ditch and the wenchuan in 2019 are selected, wherein the mud-rock flow occurs for 5 times and does not occur for 4 times, and the discriminator can output the early warning condition and the support degree at the moment. If the point is dominant, the possibility of debris flow is considered to be very low, and early warning is not needed; if the point is dominant, the possibility of debris flow is considered to be high, and early warning is needed; if the points are not dominant, the decision is made by continuously and closely observing the points. As shown in fig. 2, of the 5 points where the debris flow has occurred, 3 are classified as red, i.e., early warning is required, and 2 are classified as yellow, i.e., close observation is continuously required to make a decision. And 4 points without debris flow are divided into blue, namely early warning is not needed.
While the point is dominant or dominant at point N on the pareto curveiTo point N on the total pareto curveallCalculating the support S from the ratio ofdegree. As shown in fig. 2 (ordinate in fig. 2 indicates average rainfall intensity in the excitation process per hour), the support degrees of the 3 black regular triangle points from left to right are respectively 20.58% (it is considered that the generated debris flow is serious, the government and related departments make emergency work on the debris flow according to duties, and the traffic management department should take traffic control on partial road sections according to situations), 6.53% (the generated debris flow is slight, the government and related departments make preparation work on the debris flow according to duties), and 4.24% (the generated debris flow is slight, the government and related departments make preparation work on the debris flow according to duties), and because the support degree of the leftmost black triangle point is the highest, the probability of the debris flow is the highest. The 2 gray regular triangles and the discrimination curve are in a non-dominant state (needing continuous close observation), so that the support degree is not calculated. 4 black diamond points are non-early warning points (no measures are taken), so that the support degree is not calculated.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The debris flow intelligent early warning method based on multi-objective optimization is characterized by comprising the following steps
Step 1, collecting a data sample of the generated debris flow;
step 2, constructing a proxy model for evaluating and exciting the debris flow;
step 3, solving the pareto threshold front edge of the debris flow proxy model;
step 4, inputting the front edge of a debris flow pareto threshold value of a debris flow sample point to be predicted and a debris flow proxy model into a debris flow early warning discriminator to perform dominant discrimination comparison, and outputting a dominant discrimination result, wherein dominant discrimination result is red early warning;
step 5, evaluating the support degree of the red early warning;
and 6, carrying out debris flow early warning according to the dominance judgment result and the support degree.
2. The intelligent debris flow early warning method based on multi-objective optimization as claimed in claim 1, wherein the agent model of the debris flow in the step 2 is a multi-objective agent model, and the multi-objective agent model has a plurality of input variables and a plurality of output variables.
3. The intelligent debris flow early warning method based on multi-objective optimization as claimed in claim 1, wherein the multi-objective agent model is one or a group of display functions or implicit models obtained by learning and training samples of generated debris flow data.
4. The intelligent debris flow early warning method based on multi-objective optimization as claimed in claim 3, wherein the front edge of the pareto threshold of the debris flow is composed of a group of debris flow excitation index values which are not dominant, and the debris flow is obtained by performing optimization solution on the multi-objective agent model through a multi-objective optimization method.
5. The multi-objective optimization-based intelligent debris flow early warning method as claimed in claim 1, wherein the dominance discrimination and comparison adopts a Pareto dominance comparison method, a loose Pareto dominance comparison method or any comparison method for distinguishing multidimensional variables.
6. The intelligent debris flow early warning method based on multi-objective optimization as claimed in claim 1, wherein the debris flow sample points to be predicted are obtained by acquiring data on a debris flow online monitor and preprocessing the data.
7. The multi-objective optimization-based intelligent debris flow early warning method as claimed in claim 1, wherein at least one dominant point on a pareto curve of a debris flow sample point to be predicted is dominant, at least one dominant point on the pareto curve of the debris flow sample point to be predicted is dominant, and if the dominant relationship between the debris flow sample point to be predicted and the pareto curve cannot be compared, the dominant relationship is not dominant.
8. The multi-objective optimization-based intelligent debris flow early warning method as claimed in claim 1, wherein debris flow sample points to be predicted are dominated, and point N on the pareto curve where the debris flow sample points to be predicted are dominated is selected according to the requirementiTo point N on the total pareto curveallCalculating the support S from the ratio ofdegreeIf the support is Sdegree＝Ni/NallFor a dominant red warning point, when SdegreeWhen the mass flow rate is less than 10%, considering that the debris flow occurring at the debris flow sample point needing to be predicted is slight; when S isdegreeWhen the mass flow rate is 10-20%, determining that the debris flow occurring at the debris flow sample point needs to be predicted is medium; when S isdegreeWhen the mass flow rate is 20-40%, considering that the debris flow occurring at the debris flow sample point needing to be predicted is serious; when S isdegreeAbove 40%, the debris flow occurring at the debris flow sample point that needs to be predicted is considered to be very severe.
9. The debris flow intelligent early warning method based on multi-objective optimization as claimed in claim 1, wherein step 4 outputs a dominance judgment result, and the dominance is not early warning, and no measure is taken; the early warning of yellow color is conducted when the color is not dominant, and the color needs to be continuously and closely observed.
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